Datasets:
ftopal
/
huggingface-datasets-embeddings

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# AutoTrain Dataset for project: skill2go_summ_mbart ## Dataset Description This dataset has been automatically processed by AutoTrain for project skill2go_summ_mbart. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "feat_Unnamed: 0": 1258, "text": "<p>\u0414\u0430\u043d\u043d\u044b\u0439 \u043a\u0443\u0440\u0441 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0430\u0434\u0430\u043f\u0442\u0430\u0446\u0438\u0435\u0439 \u043a\u043b\u0430\u0441\u0441\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b \u041c\u0413\u0423 \u0418\u0421\u0410\u0410 \u0434\u043b\u044f \u0434\u0438\u0441\u0442\u0430\u043d\u0446\u0438\u043e\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f. \u0412\u0441\u0435 \u043c\u0435\u0442\u043e\u0434\u0438\u043a\u0438, \u043f\u043e\u0434\u0445\u043e\u0434 \u043a \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044e, \u0443\u043f\u0440\u0430\u0436\u043d\u0435\u043d\u0438\u044f, \u0437\u0430\u0434\u0430\u043d\u0438\u044f, \u043c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u044b, \u0430\u0443\u0434\u0438\u043e \u0438 \u0432\u0438\u0434\u0435\u043e\u0444\u0430\u0439\u043b\u044b \u043f\u043e\u0434\u0447\u0438\u043d\u0435\u043d\u044b \u043e\u0434\u043d\u043e\u0439 \u0446\u0435\u043b\u0438 - \u0432\u043e\u0441\u043f\u0438\u0442\u0430\u043d\u0438\u044e \u0432\u044b\u0441\u043e\u043a\u043e\u043a\u043b\u0430\u0441\u0441\u043d\u044b\u0445 \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u0438\u0441\u0442\u043e\u0432 \u0432 \u043e\u0431\u043b\u0430\u0441\u0442\u0438 \u043a\u0438\u0442\u0430\u0439\u0441\u043a\u043e\u0433\u043e \u044f\u0437\u044b\u043a\u0430. \u041d\u0430 \u043f\u0440\u043e\u0442\u044f\u0436\u0435\u043d\u0438\u0438 \u0431\u043e\u043b\u0435\u0435 \u0447\u0435\u043c 70 \u043b\u0435\u0442 \u0441\u0438\u0441\u0442\u0435\u043c\u0430 \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0438 \u043a\u0438\u0442\u0430\u0438\u0441\u0442\u043e\u0432 \u0432 \u041c\u0413\u0423 \u0418\u0421\u0410\u0410 \u043d\u0435\u043f\u0440\u0435\u0440\u044b\u0432\u043d\u043e \u0441\u043e\u0432\u0435\u0440\u0448\u0435\u043d\u0441\u0442\u0432\u0443\u0435\u0442\u0441\u044f \u0438 \u043d\u0435\u0438\u0437\u043c\u0435\u043d\u043d\u043e \u0434\u0430\u0435\u0442 \u043e\u0442\u043b\u0438\u0447\u043d\u044b\u0435 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b. \u0412\u043f\u0435\u0440\u0432\u044b\u0435 \u0434\u0430\u043d\u043d\u0430\u044f \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0430,&nbsp; \u0440\u0430\u043d\u0435\u0435&nbsp; \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u0430\u044f \u043b\u0438\u0448\u044c 20-30 \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0430\u043c \u0432 \u0433\u043e\u0434\u0443,&nbsp; \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u0430 \u0434\u043b\u044f \u0448\u0438\u0440\u043e\u043a\u0438\u0445 \u043c\u0430\u0441\u0441, \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u0443\u044e\u0449\u0438\u0445\u0441\u044f \u043a\u0438\u0442\u0430\u0439\u0441\u043a\u0438\u043c \u044f\u0437\u044b\u043a\u043e\u043c. </p><p><br></p><p>\u042d\u0442\u043e\u0442 \u043a\u0443\u0440\u0441 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043f\u0435\u0440\u0432\u043e\u0439 \u0441\u0442\u0443\u043f\u0435\u043d\u044c\u044e \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0438 \u043a\u0438\u0442\u0430\u0438\u0441\u0442\u043e\u0432. \u041f\u043e\u0441\u043b\u0435 \u0443\u0441\u043f\u0435\u0448\u043d\u043e\u0433\u043e \u043e\u0441\u0432\u043e\u0435\u043d\u0438\u044f \u043c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u0430 \u0432\u044b \u0431\u0443\u0434\u0435\u0442\u0435 \u0441\u043f\u043e\u0441\u043e\u0431\u043d\u044b \u0441 \u043b\u0435\u0433\u043a\u043e\u0441\u0442\u044c\u044e \u0441\u0434\u0430\u0442\u044c \u044d\u043a\u0437\u0430\u043c\u0435\u043d 1HSK. \u0412\u044b \u0441\u043c\u043e\u0436\u0435\u0442\u0435 \u043d\u0430\u0441\u0442\u0440\u043e\u0438\u0442\u044c \u0444\u043e\u043d\u0435\u0442\u0438\u043a\u0443 \u0438 \u0431\u0443\u0434\u0435\u0442\u0435 \u0437\u0432\u0443\u0447\u0430\u0442\u044c \u043f\u0440\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u0438 \u043a\u0430\u043a \u043d\u043e\u0441\u0438\u0442\u0435\u043b\u044c \u044f\u0437\u044b\u043a\u0430.&nbsp; \u0412\u044b \u043e\u0441\u0432\u043e\u0438\u0442\u0435 \u0431\u0430\u0437\u043e\u0432\u0443\u044e \u0438\u0435\u0440\u043e\u0433\u043b\u0438\u0444\u0438\u043a\u0443, \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u0435 \u0437\u043d\u0430\u043d\u0438\u044f \u043f\u043e \u0431\u0430\u0437\u043e\u0432\u043e\u0439 \u0433\u0440\u0430\u043c\u043c\u0430\u0442\u0438\u043a\u0435 \u0438 \u0441 \u043b\u0435\u0433\u043a\u043e\u0441\u0442\u044c\u044e \u043d\u0430\u0447\u043d\u0435\u0442\u0435 \u043e\u0431\u0449\u0430\u0442\u044c\u0441\u044f \u0441 \u043d\u043e\u0441\u0438\u0442\u0435\u043b\u044f\u043c\u0438. </p><p><br></p><p>\u041d\u0435 \u0442\u0440\u0435\u0431\u0443\u0435\u0442 \u043f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0435\u0433\u043e \u043e\u043f\u044b\u0442\u0430 \u0438\u0437\u0443\u0447\u0435\u043d\u0438\u044f \u043a\u0438\u0442\u0430\u0439\u0441\u043a\u043e\u0433\u043e \u044f\u0437\u044b\u043a\u0430. \u041f\u043e\u0434\u0445\u043e\u0434\u0438\u0442 \u0434\u043b\u044f \u0432\u0441\u0435\u0445 \u0432\u043e\u0437\u0440\u0430\u0441\u0442\u043e\u0432.</p>", "target": "\u041d\u0430\u0447\u0430\u043b\u044c\u043d\u044b\u0439 \u043a\u0443\u0440\u0441 \u043f\u0440\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u043a\u0438\u0442\u0430\u0439\u0441\u043a\u043e\u0433\u043e \u044f\u0437\u044b\u043a\u0430 \u0434\u043b\u044f \u0432\u0441\u0435\u0445" }, { "feat_Unnamed: 0": 598, "text": "<p>\u041a\u0443\u0440\u0441 \u043d\u0430\u0446\u0435\u043b\u0435\u043d \u043d\u0430 \u0438\u0437\u0443\u0447\u0435\u043d\u0438\u0435 \u0440\u0435\u0436\u0438\u0441\u0441\u0443\u0440\u044b \u043c\u043e\u043d\u0442\u0430\u0436\u0430 \u0441 \u043d\u0443\u043b\u044f \u0438 \u0432 \u043f\u043e\u043b\u043d\u043e\u043c \u043e\u0431\u044a\u0435\u043c\u0435 \u0440\u0430\u0441\u043a\u0440\u044b\u0432\u0430\u0435\u0442 \u0432\u0435\u0441\u044c \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0439 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b. \u0414\u043e\u043b\u0433\u043e\u0435 \u0432\u0440\u0435\u043c\u044f \u044d\u0442\u043e\u0442 \u043a\u0443\u0440\u0441 \u0431\u044b\u043b \u043e\u0434\u0438\u043d \u0438\u0437 \u0441\u0430\u043c\u044b\u0445 \u043f\u043e\u0434\u0440\u043e\u0431\u043d\u044b\u0445 \u0438 \u0433\u043b\u0443\u0431\u043e\u043a\u0438\u0445 \u0432\u043e \u0432\u0441\u0435\u043c \u0440\u0443\u0441\u0441\u043a\u043e\u044f\u0437\u044b\u0447\u043d\u043e\u043c \u0441\u0435\u0433\u043c\u0435\u043d\u0442\u0435. \u0421\u0442\u043e\u0438\u043c\u043e\u0441\u0442\u044c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u043d\u0430 \u043d\u0435\u043c \u0431\u044b\u043b\u043e 800$. \u0421\u0435\u0439\u0447\u0430\u0441 \u043c\u044b \u0434\u0435\u043b\u0438\u043c\u0441\u044f \u0438\u043c \u0431\u0435\u0441\u043f\u043b\u0430\u0442\u043d\u043e. </p><p><strong>\u041e\u0441\u043d\u043e\u0432\u043d\u044b\u0435 \u0431\u043b\u043e\u043a\u0438 \u043a\u0443\u0440\u0441\u0430:</strong></p><ol><li><p>\u041e\u0431\u0437\u043e\u0440 \u0438\u043d\u0442\u0435\u0440\u0444\u0435\u0439\u0441\u0430</p></li><li><p>\u041d\u0430\u0447\u0430\u043b\u043e \u0440\u0430\u0431\u043e\u0442\u044b. \u041e\u0440\u0433\u0430\u043d\u0438\u0437\u0430\u0446\u0438\u044f \u0438 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u044b \u043c\u043e\u043d\u0442\u0430\u0436\u0430</p></li><li><p>\u0420\u0430\u0431\u043e\u0442\u0430 \u0441\u043e \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u044c\u044e, \u0410\u0442\u0440\u0438\u0431\u0443\u0442\u0430\u043c\u0438 \u0438 \u0410\u043d\u0438\u043c\u0430\u0446\u0438\u044f</p></li><li><p>\u0420\u0430\u0431\u043e\u0442\u0430 \u0441\u043e \u0437\u0432\u0443\u043a\u043e\u043c</p></li><li><p>\u0420\u0430\u0431\u043e\u0442\u0430 \u0441 \u0433\u0440\u0430\u0444\u0438\u043a\u043e\u0439</p></li><li><p>\u041f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u044d\u0444\u0444\u0435\u043a\u0442\u043e\u0432 \u0438 \u043f\u0435\u0440\u0435\u0445\u043e\u0434\u043e\u0432. \u0420\u0430\u0431\u043e\u0442\u0430 \u0441 \u0445\u0440\u043e\u043c\u0430\u043a\u0435\u0435\u043c</p></li><li><p>\u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u0438 \u0440\u0435\u0434\u0430\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0442\u0435\u043a\u0441\u0442\u0430</p></li><li><p>\u0418\u043d\u0434\u0436\u0435\u0441\u0442 \u0438 \u043c\u043d\u043e\u0433\u043e\u043a\u0430\u043c\u0435\u0440\u043d\u044b\u0439 \u043c\u043e\u043d\u0442\u0430\u0436</p></li><li><p>\u0426\u0432\u0435\u0442\u043e\u043a\u043e\u0440\u0440\u0435\u043a\u0446\u0438\u044f</p></li><li><p>\u0424\u0438\u043d\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u0438 \u041a\u043e\u043d\u0432\u0435\u0440\u0442\u0430\u0446\u0438\u044f</p><p><br></p></li></ol><p>Final Cut Pro 7 \u0434\u043e\u043b\u0433\u043e \u0432\u0440\u0435\u043c\u044f \u0431\u044b\u043b \u043b\u0438\u0434\u0435\u0440\u043e\u043c \u0441\u0440\u0435\u0434\u0438 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c \u0434\u043b\u044f \u043c\u043e\u043d\u0442\u0430\u0436\u0430 \u0432 \u0413\u043e\u043b\u043b\u0438\u0432\u0443\u0434\u0435 \u0438 \u0415\u0432\u0440\u043e\u043f\u0435. \u0421\u0432\u044f\u0437\u0430\u043d\u043e \u044d\u0442\u043e \u0441 \u0435\u0433\u043e \u0448\u0438\u0440\u043e\u043a\u0438\u043c \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0435\u043c \u0438 \u0443\u0434\u043e\u0431\u0441\u0442\u0432\u043e\u043c \u0440\u0430\u0431\u043e\u0442\u044b. </p><p>\u041e\u0444\u0438\u0446\u0438\u0430\u043b\u044c\u043d\u043e \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0430 \u044d\u0442\u043e\u0439 \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u0438 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b \u0431\u044b\u043b\u0430 \u043f\u0440\u0435\u043a\u0440\u0430\u0449\u0435\u043d\u0430 \u0432 2011 \u0433\u043e\u0434\u0443. \u0421\u0442\u0430\u0440\u043e\u0435 \u043f\u043e\u043a\u043e\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b \u0437\u0430\u043c\u0435\u043d\u0438\u043b \u043d\u043e\u0432\u044b\u0439 Final Cut Pro X. \u0422\u0435\u043c \u043d\u0435 \u043c\u0435\u043d\u0435\u0435, \u0434\u0430\u0436\u0435 \u0441\u043f\u0443\u0441\u0442\u044f \u0431\u043e\u043b\u0435\u0435 10 \u043b\u0435\u0442, \u043c\u043d\u043e\u0433\u0438\u0435 \u0440\u0435\u0436\u0438\u0441\u0441\u0435\u0440\u044b \u043c\u043e\u043d\u0442\u0430\u0436\u0430 \u043d\u0430 \u0442\u0435\u043b\u0435\u043a\u0430\u043d\u0430\u043b\u0430\u0445 \u0438 \u043f\u0440\u043e\u0434\u0430\u043a\u0448\u0435\u043d\u0430\u0445 \u043f\u0440\u043e\u0434\u043e\u043b\u0436\u0430\u044e\u0442 \u0440\u0430\u0431\u043e\u0442\u0443 \u0432 \u0441\u0442\u0430\u0440\u043e\u0439 \u0432\u0435\u0440\u0441\u0438\u0438 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b. \u0418\u0437\u0443\u0447\u0438\u0432 \u044d\u0442\u0443 \u0432\u0435\u0440\u0441\u0438\u044e \u0432\u044b \u043f\u043e\u0439\u043c\u0435\u0442\u0435 \u043a\u0430\u043a \u0441\u0442\u0440\u043e\u0438\u0442\u044c \u043c\u043e\u043d\u0442\u0430\u0436 \u0438 \u043d\u0430\u0443\u0447\u0438\u0442\u0435\u0441\u044c \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0441 \u043a\u043b\u0430\u0441\u0441\u0438\u0447\u0435\u0441\u043a\u0438\u043c\u0438 \u0442\u0435\u0445\u043d\u0438\u043a\u0430\u043c\u0438 \u0438 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u043c\u0438 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Final Cut Pro 7 \u043e\u0442 Apple Certified Pro" } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "feat_Unnamed: 0": "Value(dtype='int64', id=None)", "text": "Value(dtype='string', id=None)", "target": "Value(dtype='string', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 1830 | | valid | 458 |
PavelDanek/autotrain-data-skill2go_summ_mbart
[ "task_categories:summarization", "region:us" ]
2023-03-16T17:10:15+00:00
{"task_categories": ["summarization"]}
2023-03-16T17:14:59+00:00
[]
[]
TAGS #task_categories-summarization #region-us
AutoTrain Dataset for project: skill2go\_summ\_mbart ==================================================== Dataset Description ------------------- This dataset has been automatically processed by AutoTrain for project skill2go\_summ\_mbart. ### Languages The BCP-47 code for the dataset's language is unk. Dataset Structure ----------------- ### Data Instances A sample from this dataset looks as follows: ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow:
[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ "TAGS\n#task_categories-summarization #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ 16, 27, 17, 23, 27 ]
[ "passage: TAGS\n#task_categories-summarization #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
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73634ee33473c5cf77db79be214478e7aa8eb4c3
# Dataset Card for "OIG-small-chip2" OIG-small-chip2 dataset from https://laion.ai/blog/oig-dataset/ <br> Original Dataset - https://github.com/LAION-AI/Open-Instruction-Generalist
0-hero/OIG-small-chip2
[ "task_categories:conversational", "task_categories:text2text-generation", "language:en", "region:us" ]
2023-03-16T17:59:26+00:00
{"language": ["en"], "task_categories": ["conversational", "text2text-generation"], "dataset_info": {"features": [{"name": "user", "dtype": "string"}, {"name": "chip2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 82154419, "num_examples": 210289}], "download_size": 51736759, "dataset_size": 82154419}}
2023-03-16T20:10:19+00:00
[]
[ "en" ]
TAGS #task_categories-conversational #task_categories-text2text-generation #language-English #region-us
# Dataset Card for "OIG-small-chip2" OIG-small-chip2 dataset from URL <br> Original Dataset - URL
[ "# Dataset Card for \"OIG-small-chip2\"\n\nOIG-small-chip2 dataset from URL <br>\nOriginal Dataset - URL" ]
[ "TAGS\n#task_categories-conversational #task_categories-text2text-generation #language-English #region-us \n", "# Dataset Card for \"OIG-small-chip2\"\n\nOIG-small-chip2 dataset from URL <br>\nOriginal Dataset - URL" ]
[ 33, 35 ]
[ "passage: TAGS\n#task_categories-conversational #task_categories-text2text-generation #language-English #region-us \n# Dataset Card for \"OIG-small-chip2\"\n\nOIG-small-chip2 dataset from URL <br>\nOriginal Dataset - URL" ]
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949bf8b5a00d3ce9e1e81c5f70be7857a433328a
# AutoTrain Dataset for project: t5-autotrain ## Dataset Description This dataset has been automatically processed by AutoTrain for project t5-autotrain. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "target": "SHOULD I WEAR A COAT TODAY ?", "source": "Kya mujhe aj coat pehena chahiye ?", "feat_en_parse": "[IN:GET_WEATHER SHOULD I WEAR A [SL:WEATHER_ATTRIBUTE COAT ] [SL:DATE_TIME TODAY ] ? ]", "feat_cs_parse": "[IN:GET_WEATHER Kya mujhe [SL:DATE_TIME aj ] [SL:WEATHER_ATTRIBUTE coat ] pehena chahiye ? ]", "feat_domain": "weather" }, { "target": "Label my timer as Gym Timer", "source": "Mere timer ko Gym Timer ka label dijiye", "feat_en_parse": "[IN:UNSUPPORTED_TIMER Label my timer as Gym Timer ]", "feat_cs_parse": "[IN:UNSUPPORTED_TIMER Mere timer ko Gym Timer ka label dijiye ]", "feat_domain": "timer" } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "target": "Value(dtype='string', id=None)", "source": "Value(dtype='string', id=None)", "feat_en_parse": "Value(dtype='string', id=None)", "feat_cs_parse": "Value(dtype='string', id=None)", "feat_domain": "Value(dtype='string', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 2394 | | valid | 599 |
RohanHBTU/autotrain-data-t5-autotrain
[ "task_categories:translation", "region:us" ]
2023-03-16T18:19:20+00:00
{"task_categories": ["translation"]}
2023-03-16T18:31:46+00:00
[]
[]
TAGS #task_categories-translation #region-us
AutoTrain Dataset for project: t5-autotrain =========================================== Dataset Description ------------------- This dataset has been automatically processed by AutoTrain for project t5-autotrain. ### Languages The BCP-47 code for the dataset's language is unk. Dataset Structure ----------------- ### Data Instances A sample from this dataset looks as follows: ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow:
[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ "TAGS\n#task_categories-translation #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ 15, 27, 17, 23, 27 ]
[ "passage: TAGS\n#task_categories-translation #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
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d79d76a4973afe65ee626cdc15d76c46b8fe9952
# Dataset Card for "VQAv2SampleValidation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_ns_1000_OE" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/VQAv2SampleValidation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_ns_1000_OE
[ "region:us" ]
2023-03-16T19:52:37+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 141448, "num_examples": 1000}], "download_size": 53292, "dataset_size": 141448}}
2023-03-16T22:57:47+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2SampleValidation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_ns_1000_OE" More Information needed
[ "# Dataset Card for \"VQAv2SampleValidation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_ns_1000_OE\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2SampleValidation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_ns_1000_OE\"\n\nMore Information needed" ]
[ 6, 56 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2SampleValidation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_ns_1000_OE\"\n\nMore Information needed" ]
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a55cb725c4f21b165588a9d9e59cb5d807b46e86
# Dataset Card for "tokenized_legal_corpus_with_full_docs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
marcus2000/tokenized_legal_corpus_with_full_docs
[ "region:us" ]
2023-03-16T20:02:40+00:00
{"dataset_info": {"features": [{"name": "\u0422\u0435\u043a\u0441\u0442", "dtype": "string"}, {"name": "\u041a\u043e\u043c\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0439 \u0420\u0413", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 175062362, "num_examples": 1819}, {"name": "test", "num_bytes": 20792076, "num_examples": 203}], "download_size": 63001541, "dataset_size": 195854438}}
2023-03-16T20:02:48+00:00
[]
[]
TAGS #region-us
# Dataset Card for "tokenized_legal_corpus_with_full_docs" More Information needed
[ "# Dataset Card for \"tokenized_legal_corpus_with_full_docs\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"tokenized_legal_corpus_with_full_docs\"\n\nMore Information needed" ]
[ 6, 25 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"tokenized_legal_corpus_with_full_docs\"\n\nMore Information needed" ]
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489e4f8d18e45535c86b2737e63b2e3d020b3c8c
# Dataset Card for "cv_svamp_augmented_fold0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sethapun/cv_svamp_augmented_fold0
[ "region:us" ]
2023-03-16T20:20:30+00:00
{"dataset_info": {"features": [{"name": "Question", "dtype": "string"}, {"name": "Numbers", "dtype": "string"}, {"name": "Equation", "dtype": "string"}, {"name": "Answer", "dtype": "float64"}, {"name": "group_nums", "dtype": "string"}, {"name": "Body", "dtype": "string"}, {"name": "Ques", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "wrong_equation", "dtype": "string"}, {"name": "WrongAnswer", "dtype": "float64"}, {"name": "label", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 2791146, "num_examples": 3932}, {"name": "validation", "num_bytes": 163506, "num_examples": 206}], "download_size": 959212, "dataset_size": 2954652}}
2023-03-23T01:30:54+00:00
[]
[]
TAGS #region-us
# Dataset Card for "cv_svamp_augmented_fold0" More Information needed
[ "# Dataset Card for \"cv_svamp_augmented_fold0\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"cv_svamp_augmented_fold0\"\n\nMore Information needed" ]
[ 6, 23 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"cv_svamp_augmented_fold0\"\n\nMore Information needed" ]
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1e67cc8c5336fa2116806d5c1ebca369b0551acb
# Dataset Card for "cv_svamp_augmented_fold1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sethapun/cv_svamp_augmented_fold1
[ "region:us" ]
2023-03-16T20:20:34+00:00
{"dataset_info": {"features": [{"name": "Question", "dtype": "string"}, {"name": "Numbers", "dtype": "string"}, {"name": "Equation", "dtype": "string"}, {"name": "Answer", "dtype": "float64"}, {"name": "group_nums", "dtype": "string"}, {"name": "Body", "dtype": "string"}, {"name": "Ques", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "wrong_equation", "dtype": "string"}, {"name": "WrongAnswer", "dtype": "float64"}, {"name": "label", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 2792984, "num_examples": 3920}, {"name": "validation", "num_bytes": 161666, "num_examples": 218}], "download_size": 957061, "dataset_size": 2954650}}
2023-03-23T01:30:58+00:00
[]
[]
TAGS #region-us
# Dataset Card for "cv_svamp_augmented_fold1" More Information needed
[ "# Dataset Card for \"cv_svamp_augmented_fold1\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"cv_svamp_augmented_fold1\"\n\nMore Information needed" ]
[ 6, 23 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"cv_svamp_augmented_fold1\"\n\nMore Information needed" ]
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d9f24c666b877eaf8f44a3241188788721b5a79f
# Dataset Card for "cv_svamp_augmented_fold2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sethapun/cv_svamp_augmented_fold2
[ "region:us" ]
2023-03-16T20:20:41+00:00
{"dataset_info": {"features": [{"name": "Question", "dtype": "string"}, {"name": "Numbers", "dtype": "string"}, {"name": "Equation", "dtype": "string"}, {"name": "Answer", "dtype": "float64"}, {"name": "group_nums", "dtype": "string"}, {"name": "Body", "dtype": "string"}, {"name": "Ques", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "wrong_equation", "dtype": "string"}, {"name": "WrongAnswer", "dtype": "float64"}, {"name": "label", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 2786442, "num_examples": 3911}, {"name": "validation", "num_bytes": 168207, "num_examples": 227}], "download_size": 956410, "dataset_size": 2954649}}
2023-03-23T01:31:02+00:00
[]
[]
TAGS #region-us
# Dataset Card for "cv_svamp_augmented_fold2" More Information needed
[ "# Dataset Card for \"cv_svamp_augmented_fold2\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"cv_svamp_augmented_fold2\"\n\nMore Information needed" ]
[ 6, 23 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"cv_svamp_augmented_fold2\"\n\nMore Information needed" ]
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cac6b14a418a7887ee2b2ea7b0fe00acca2a5527
# Dataset Card for "cv_svamp_augmented_fold3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sethapun/cv_svamp_augmented_fold3
[ "region:us" ]
2023-03-16T20:20:44+00:00
{"dataset_info": {"features": [{"name": "Question", "dtype": "string"}, {"name": "Numbers", "dtype": "string"}, {"name": "Equation", "dtype": "string"}, {"name": "Answer", "dtype": "float64"}, {"name": "group_nums", "dtype": "string"}, {"name": "Body", "dtype": "string"}, {"name": "Ques", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "wrong_equation", "dtype": "string"}, {"name": "WrongAnswer", "dtype": "float64"}, {"name": "label", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 2824786, "num_examples": 3973}, {"name": "validation", "num_bytes": 129871, "num_examples": 165}], "download_size": 953279, "dataset_size": 2954657}}
2023-03-23T01:31:07+00:00
[]
[]
TAGS #region-us
# Dataset Card for "cv_svamp_augmented_fold3" More Information needed
[ "# Dataset Card for \"cv_svamp_augmented_fold3\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"cv_svamp_augmented_fold3\"\n\nMore Information needed" ]
[ 6, 23 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"cv_svamp_augmented_fold3\"\n\nMore Information needed" ]
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591be1684d4f2b971c9689eef1e9fc1cac3f7bb0
# Dataset Card for "cv_svamp_augmented_fold4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sethapun/cv_svamp_augmented_fold4
[ "region:us" ]
2023-03-16T20:20:50+00:00
{"dataset_info": {"features": [{"name": "Question", "dtype": "string"}, {"name": "Numbers", "dtype": "string"}, {"name": "Equation", "dtype": "string"}, {"name": "Answer", "dtype": "float64"}, {"name": "group_nums", "dtype": "string"}, {"name": "Body", "dtype": "string"}, {"name": "Ques", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "wrong_equation", "dtype": "string"}, {"name": "WrongAnswer", "dtype": "float64"}, {"name": "label", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 2820364, "num_examples": 3954}, {"name": "validation", "num_bytes": 134291, "num_examples": 184}], "download_size": 954858, "dataset_size": 2954655}}
2023-03-23T01:31:11+00:00
[]
[]
TAGS #region-us
# Dataset Card for "cv_svamp_augmented_fold4" More Information needed
[ "# Dataset Card for \"cv_svamp_augmented_fold4\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"cv_svamp_augmented_fold4\"\n\nMore Information needed" ]
[ 6, 23 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"cv_svamp_augmented_fold4\"\n\nMore Information needed" ]
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f73b22cdf8d67e43cda9de31436c6ffeb136c5f0
# Stihi.ru dataset ## Table of Contents - [Table of Contents](#table-of-contents) - [Description](#description) - [Usage](#usage) - [Personal and Sensitive Information](#personal-and-sensitive-information) ## Description **Summary:** A subset if [Taiga](https://tatianashavrina.github.io/taiga_site/), uploaded here for convenience. Additional cleaning was performed. **Script:** [create_stihi.py](https://github.com/IlyaGusev/rulm/blob/master/data_processing/create_stihi.py) **Point of Contact:** [Ilya Gusev]([email protected]) **Languages:** Russian. ## Usage Prerequisites: ```bash pip install datasets zstandard jsonlines pysimdjson ``` Dataset iteration: ```python from datasets import load_dataset dataset = load_dataset('IlyaGusev/stihi_ru', split="train", streaming=True) for example in dataset: print(example["text"]) ``` ## Personal and Sensitive Information The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible.
IlyaGusev/stihi_ru
[ "task_categories:text-generation", "size_categories:1M<n<10M", "language:ru", "region:us" ]
2023-03-16T22:05:24+00:00
{"language": ["ru"], "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "genre", "dtype": "string"}, {"name": "topic", "dtype": "string"}, {"name": "author", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6029108612, "num_examples": 5151050}], "download_size": 1892727043, "dataset_size": 6029108612}}
2023-03-20T16:01:41+00:00
[]
[ "ru" ]
TAGS #task_categories-text-generation #size_categories-1M<n<10M #language-Russian #region-us
# URL dataset ## Table of Contents - Table of Contents - Description - Usage - Personal and Sensitive Information ## Description Summary: A subset if Taiga, uploaded here for convenience. Additional cleaning was performed. Script: create_stihi.py Point of Contact: Ilya Gusev Languages: Russian. ## Usage Prerequisites: Dataset iteration: ## Personal and Sensitive Information The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible.
[ "# URL dataset", "## Table of Contents\n- Table of Contents\n- Description\n- Usage\n- Personal and Sensitive Information", "## Description\n\nSummary: A subset if Taiga, uploaded here for convenience. Additional cleaning was performed.\n\nScript: create_stihi.py\n\nPoint of Contact: Ilya Gusev\n\nLanguages: Russian.", "## Usage\n\nPrerequisites:\n\n\nDataset iteration:", "## Personal and Sensitive Information\n\nThe dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible." ]
[ "TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-Russian #region-us \n", "# URL dataset", "## Table of Contents\n- Table of Contents\n- Description\n- Usage\n- Personal and Sensitive Information", "## Description\n\nSummary: A subset if Taiga, uploaded here for convenience. Additional cleaning was performed.\n\nScript: create_stihi.py\n\nPoint of Contact: Ilya Gusev\n\nLanguages: Russian.", "## Usage\n\nPrerequisites:\n\n\nDataset iteration:", "## Personal and Sensitive Information\n\nThe dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible." ]
[ 34, 4, 22, 48, 14, 42 ]
[ "passage: TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-Russian #region-us \n# URL dataset## Table of Contents\n- Table of Contents\n- Description\n- Usage\n- Personal and Sensitive Information## Description\n\nSummary: A subset if Taiga, uploaded here for convenience. Additional cleaning was performed.\n\nScript: create_stihi.py\n\nPoint of Contact: Ilya Gusev\n\nLanguages: Russian.## Usage\n\nPrerequisites:\n\n\nDataset iteration:## Personal and Sensitive Information\n\nThe dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible." ]
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12ca3b695563788fead87a982ad1a068284413f4
# Dataset Card for Dataset Name - **Homepage: https://hazyresearch.stanford.edu/legalbench/** - **Repository: https://github.com/HazyResearch/legalbench/** - **Paper: https://arxiv.org/abs/2308.11462** ## Dataset Description ### Dataset Summary The LegalBench project is an ongoing open science effort to collaboratively curate tasks for evaluating legal reasoning in English large language models (LLMs). The benchmark currently consists of 162 tasks gathered from 40 contributors. If you have questions about the project or would like to get involved, please see the website for more information. ### Supported Tasks and Leaderboards LegalBench tasks span multiple types (binary classification, multi-class classification, extraction, generation, entailment), multiple types of text (statutes, judicial opinions, contracts, etc.), and multiple areas of law (evidence, contracts, civil procedure, etc.). For more information on tasks, we recommend visiting the website, where you can search through task descriptions, or the Github repository, which contains more granular task descriptions. We also recommend reading the paper, which provides more background on task significance and construction process. ### Languages All LegalBench tasks are in English. ## Dataset Structure ### Data Instances Detailed descriptions of the instances for each task can be found on the Github. An example of an instance, for the `abercrombie` task, is provided below: ``` { "text": "The mark "Ivory" for a product made of elephant tusks.", "label": "generic" "idx": 0 } ``` A substantial number of LegalBench tasks are binary classification tasks, which require the LLM to determine if a piece of text has some legal attribute. Because these are framed as Yes/No questions, the label space is "Yes" or "No". ### Data Fields Detailed descriptions of the instances for each task can be found on the Github. ### Data Splits Each task has a training and evaluation split. Following [RAFT](https://huggingface.co/datasets/ought/raft), train splits only consists of a few-labeled instances, reflecting the few-shot nature of most LLMs. ## Dataset Creation ### Curation Rationale LegalBench was created to enable researchers to better benchmark the legal reasoning capabilities of LLMs. ### Source Data #### Initial Data Collection and Normalization Broadly, LegalBench tasks are drawn from three sources. The first source of tasks are existing available datasets and corpora. Most of these were originally released for non-LLM evaluation settings. In creating tasks for LegalBench from these sources, we often significantly reformatted data and restructured the prediction objective. For instance, the original [CUAD dataset](https://github.com/TheAtticusProject/cuad) contains annotations on long-documents and is intended for evaluating extraction with span-prediction models. We restructure this corpora to generate a binary classification task for each type of contractual clause. While the original corpus emphasized the long-document aspects of contracts, our restructured tasks emphasize whether LLMs can identify the distinguishing features of different types of clauses. The second source of tasks are datasets that were previously constructed by legal professionals but never released. This primarily includes datasets hand-coded by legal scholars as part of prior empirical legal projects. The last category of tasks are those that were developed specifically for \name, by the authors of this paper. Overall, tasks are drawn from 36 distinct corpora. Please see the Appendix of the paper for more details. #### Who are the source language producers? LegalBench data was created by humans. Demographic information for these individuals is not available. ### Annotations #### Annotation process Please see the paper for more information on the annotation process used in the creation of each task. #### Who are the annotators? Please see the paper for more information on the identity of annotators for each task. ### Personal and Sensitive Information Data in this benchmark has either been synthetically generated, or derived from an already public source (e.g., contracts from the EDGAR database). Several tasks have been derived from the LearnedHands corpus, which consists of public posts on /r/LegalAdvice. Some posts may discuss sensitive issues. ## Considerations for Using the Data ### Social Impact of Dataset Please see the original paper for a discussion of social impact. ### Discussion of Biases Please see the original paper for a discussion of social impact. ### Other Known Limitations LegalBench primarily contains tasks corresponding to American law. ## Additional Information ### Dataset Curators Please see the website for a full list of participants in the LegalBench project. ### Licensing Information LegalBench tasks are subject to different licenses. Please see the paper for a description of the licenses. ### Citation Information If you intend to reference LegalBench broadly, please use the citation below. If you are working with a particular task, please use the citation below in addition to the task specific citation (which can be found on the task page on the website or Github). ``` @misc{guha2023legalbench, title={LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models}, author={Neel Guha and Julian Nyarko and Daniel E. Ho and Christopher Ré and Adam Chilton and Aditya Narayana and Alex Chohlas-Wood and Austin Peters and Brandon Waldon and Daniel N. Rockmore and Diego Zambrano and Dmitry Talisman and Enam Hoque and Faiz Surani and Frank Fagan and Galit Sarfaty and Gregory M. Dickinson and Haggai Porat and Jason Hegland and Jessica Wu and Joe Nudell and Joel Niklaus and John Nay and Jonathan H. Choi and Kevin Tobia and Margaret Hagan and Megan Ma and Michael Livermore and Nikon Rasumov-Rahe and Nils Holzenberger and Noam Kolt and Peter Henderson and Sean Rehaag and Sharad Goel and Shang Gao and Spencer Williams and Sunny Gandhi and Tom Zur and Varun Iyer and Zehua Li}, year={2023}, eprint={2308.11462}, archivePrefix={arXiv}, primaryClass={cs.CL} } @article{koreeda2021contractnli, title={ContractNLI: A dataset for document-level natural language inference for contracts}, author={Koreeda, Yuta and Manning, Christopher D}, journal={arXiv preprint arXiv:2110.01799}, year={2021} } @article{hendrycks2021cuad, title={Cuad: An expert-annotated nlp dataset for legal contract review}, author={Hendrycks, Dan and Burns, Collin and Chen, Anya and Ball, Spencer}, journal={arXiv preprint arXiv:2103.06268}, year={2021} } @article{wang2023maud, title={MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding}, author={Wang, Steven H and Scardigli, Antoine and Tang, Leonard and Chen, Wei and Levkin, Dimitry and Chen, Anya and Ball, Spencer and Woodside, Thomas and Zhang, Oliver and Hendrycks, Dan}, journal={arXiv preprint arXiv:2301.00876}, year={2023} } @inproceedings{wilson2016creation, title={The creation and analysis of a website privacy policy corpus}, author={Wilson, Shomir and Schaub, Florian and Dara, Aswarth Abhilash and Liu, Frederick and Cherivirala, Sushain and Leon, Pedro Giovanni and Andersen, Mads Schaarup and Zimmeck, Sebastian and Sathyendra, Kanthashree Mysore and Russell, N Cameron and others}, booktitle={Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages={1330--1340}, year={2016} } @inproceedings{zheng2021does, title={When does pretraining help? assessing self-supervised learning for law and the casehold dataset of 53,000+ legal holdings}, author={Zheng, Lucia and Guha, Neel and Anderson, Brandon R and Henderson, Peter and Ho, Daniel E}, booktitle={Proceedings of the eighteenth international conference on artificial intelligence and law}, pages={159--168}, year={2021} } @article{zimmeck2019maps, title={Maps: Scaling privacy compliance analysis to a million apps}, author={Zimmeck, Sebastian and Story, Peter and Smullen, Daniel and Ravichander, Abhilasha and Wang, Ziqi and Reidenberg, Joel R and Russell, N Cameron and Sadeh, Norman}, journal={Proc. Priv. Enhancing Tech.}, volume={2019}, pages={66}, year={2019} } @article{ravichander2019question, title={Question answering for privacy policies: Combining computational and legal perspectives}, author={Ravichander, Abhilasha and Black, Alan W and Wilson, Shomir and Norton, Thomas and Sadeh, Norman}, journal={arXiv preprint arXiv:1911.00841}, year={2019} } @article{holzenberger2021factoring, title={Factoring statutory reasoning as language understanding challenges}, author={Holzenberger, Nils and Van Durme, Benjamin}, journal={arXiv preprint arXiv:2105.07903}, year={2021} } @article{lippi2019claudette, title={CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service}, author={Lippi, Marco and Pa{\l}ka, Przemys{\l}aw and Contissa, Giuseppe and Lagioia, Francesca and Micklitz, Hans-Wolfgang and Sartor, Giovanni and Torroni, Paolo}, journal={Artificial Intelligence and Law}, volume={27}, pages={117--139}, year={2019}, publisher={Springer} } ```
nguha/legalbench
[ "task_categories:text-classification", "task_categories:question-answering", "task_categories:text-generation", "size_categories:10K<n<100K", "language:en", "license:other", "legal", "law", "finance", "arxiv:2308.11462", "region:us" ]
2023-03-16T23:03:42+00:00
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2023-10-18T16:18:18+00:00
[ "2308.11462" ]
[ "en" ]
TAGS #task_categories-text-classification #task_categories-question-answering #task_categories-text-generation #size_categories-10K<n<100K #language-English #license-other #legal #law #finance #arxiv-2308.11462 #region-us
# Dataset Card for Dataset Name - Homepage: URL - Repository: URL - Paper: URL ## Dataset Description ### Dataset Summary The LegalBench project is an ongoing open science effort to collaboratively curate tasks for evaluating legal reasoning in English large language models (LLMs). The benchmark currently consists of 162 tasks gathered from 40 contributors. If you have questions about the project or would like to get involved, please see the website for more information. ### Supported Tasks and Leaderboards LegalBench tasks span multiple types (binary classification, multi-class classification, extraction, generation, entailment), multiple types of text (statutes, judicial opinions, contracts, etc.), and multiple areas of law (evidence, contracts, civil procedure, etc.). For more information on tasks, we recommend visiting the website, where you can search through task descriptions, or the Github repository, which contains more granular task descriptions. We also recommend reading the paper, which provides more background on task significance and construction process. ### Languages All LegalBench tasks are in English. ## Dataset Structure ### Data Instances Detailed descriptions of the instances for each task can be found on the Github. An example of an instance, for the 'abercrombie' task, is provided below: A substantial number of LegalBench tasks are binary classification tasks, which require the LLM to determine if a piece of text has some legal attribute. Because these are framed as Yes/No questions, the label space is "Yes" or "No". ### Data Fields Detailed descriptions of the instances for each task can be found on the Github. ### Data Splits Each task has a training and evaluation split. Following RAFT, train splits only consists of a few-labeled instances, reflecting the few-shot nature of most LLMs. ## Dataset Creation ### Curation Rationale LegalBench was created to enable researchers to better benchmark the legal reasoning capabilities of LLMs. ### Source Data #### Initial Data Collection and Normalization Broadly, LegalBench tasks are drawn from three sources. The first source of tasks are existing available datasets and corpora. Most of these were originally released for non-LLM evaluation settings. In creating tasks for LegalBench from these sources, we often significantly reformatted data and restructured the prediction objective. For instance, the original CUAD dataset contains annotations on long-documents and is intended for evaluating extraction with span-prediction models. We restructure this corpora to generate a binary classification task for each type of contractual clause. While the original corpus emphasized the long-document aspects of contracts, our restructured tasks emphasize whether LLMs can identify the distinguishing features of different types of clauses. The second source of tasks are datasets that were previously constructed by legal professionals but never released. This primarily includes datasets hand-coded by legal scholars as part of prior empirical legal projects. The last category of tasks are those that were developed specifically for \name, by the authors of this paper. Overall, tasks are drawn from 36 distinct corpora. Please see the Appendix of the paper for more details. #### Who are the source language producers? LegalBench data was created by humans. Demographic information for these individuals is not available. ### Annotations #### Annotation process Please see the paper for more information on the annotation process used in the creation of each task. #### Who are the annotators? Please see the paper for more information on the identity of annotators for each task. ### Personal and Sensitive Information Data in this benchmark has either been synthetically generated, or derived from an already public source (e.g., contracts from the EDGAR database). Several tasks have been derived from the LearnedHands corpus, which consists of public posts on /r/LegalAdvice. Some posts may discuss sensitive issues. ## Considerations for Using the Data ### Social Impact of Dataset Please see the original paper for a discussion of social impact. ### Discussion of Biases Please see the original paper for a discussion of social impact. ### Other Known Limitations LegalBench primarily contains tasks corresponding to American law. ## Additional Information ### Dataset Curators Please see the website for a full list of participants in the LegalBench project. ### Licensing Information LegalBench tasks are subject to different licenses. Please see the paper for a description of the licenses. If you intend to reference LegalBench broadly, please use the citation below. If you are working with a particular task, please use the citation below in addition to the task specific citation (which can be found on the task page on the website or Github).
[ "# Dataset Card for Dataset Name\n\n\n- Homepage: URL \n- Repository: URL \n- Paper: URL", "## Dataset Description", "### Dataset Summary\n\nThe LegalBench project is an ongoing open science effort to collaboratively curate tasks for evaluating legal reasoning in English large language models (LLMs). The benchmark currently consists of 162 tasks gathered from 40 contributors. \n\nIf you have questions about the project or would like to get involved, please see the website for more information.", "### Supported Tasks and Leaderboards\n\nLegalBench tasks span multiple types (binary classification, multi-class classification, extraction, generation, entailment), multiple types of text (statutes, judicial opinions, contracts, etc.), and multiple areas of law (evidence, contracts, civil procedure, etc.). For more information on tasks, we recommend visiting the website, where you can search through task descriptions, or the Github repository, which contains more granular task descriptions. We also recommend reading the paper, which provides more background on task significance and construction process.", "### Languages\n\nAll LegalBench tasks are in English.", "## Dataset Structure", "### Data Instances\n\nDetailed descriptions of the instances for each task can be found on the Github. An example of an instance, for the 'abercrombie' task, is provided below:\n\n\n\nA substantial number of LegalBench tasks are binary classification tasks, which require the LLM to determine if a piece of text has some legal attribute. Because these are framed as Yes/No questions, the label space is \"Yes\" or \"No\".", "### Data Fields\n\nDetailed descriptions of the instances for each task can be found on the Github.", "### Data Splits\n\nEach task has a training and evaluation split. Following RAFT, train splits only consists of a few-labeled instances, reflecting the few-shot nature of most LLMs.", "## Dataset Creation", "### Curation Rationale\n\nLegalBench was created to enable researchers to better benchmark the legal reasoning capabilities of LLMs.", "### Source Data", "#### Initial Data Collection and Normalization\n\nBroadly, LegalBench tasks are drawn from three sources. The first source of tasks are existing available datasets and corpora. Most of these were originally released for non-LLM evaluation settings. In creating tasks for LegalBench from these sources, we often significantly reformatted data and restructured the prediction objective. For instance, the original CUAD dataset contains annotations on long-documents and is intended for evaluating extraction with span-prediction models. We restructure this corpora to generate a binary classification task for each type of contractual clause. While the original corpus emphasized the long-document aspects of contracts, our restructured tasks emphasize whether LLMs can identify the distinguishing features of different types of clauses. The second source of tasks are datasets that were previously constructed by legal professionals but never released. This primarily includes datasets hand-coded by legal scholars as part of prior empirical legal projects. The last category of tasks are those that were developed specifically for \\name, by the authors of this paper. Overall, tasks are drawn from 36 distinct corpora. Please see the Appendix of the paper for more details.", "#### Who are the source language producers?\n\nLegalBench data was created by humans. Demographic information for these individuals is not available.", "### Annotations", "#### Annotation process\n\nPlease see the paper for more information on the annotation process used in the creation of each task.", "#### Who are the annotators?\n\nPlease see the paper for more information on the identity of annotators for each task.", "### Personal and Sensitive Information\n\nData in this benchmark has either been synthetically generated, or derived from an already public source (e.g., contracts from the EDGAR database). \n\nSeveral tasks have been derived from the LearnedHands corpus, which consists of public posts on /r/LegalAdvice. Some posts may discuss sensitive issues.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nPlease see the original paper for a discussion of social impact.", "### Discussion of Biases\n\nPlease see the original paper for a discussion of social impact.", "### Other Known Limitations\n\nLegalBench primarily contains tasks corresponding to American law.", "## Additional Information", "### Dataset Curators\n\nPlease see the website for a full list of participants in the LegalBench project.", "### Licensing Information\n\nLegalBench tasks are subject to different licenses. Please see the paper for a description of the licenses.\n\n\n\nIf you intend to reference LegalBench broadly, please use the citation below. If you are working with a particular task, please use the citation below in addition to the task specific citation (which can be found on the task page on the website or Github)." ]
[ "TAGS\n#task_categories-text-classification #task_categories-question-answering #task_categories-text-generation #size_categories-10K<n<100K #language-English #license-other #legal #law #finance #arxiv-2308.11462 #region-us \n", "# Dataset Card for Dataset Name\n\n\n- Homepage: URL \n- Repository: URL \n- Paper: URL", "## Dataset Description", "### Dataset Summary\n\nThe LegalBench project is an ongoing open science effort to collaboratively curate tasks for evaluating legal reasoning in English large language models (LLMs). The benchmark currently consists of 162 tasks gathered from 40 contributors. \n\nIf you have questions about the project or would like to get involved, please see the website for more information.", "### Supported Tasks and Leaderboards\n\nLegalBench tasks span multiple types (binary classification, multi-class classification, extraction, generation, entailment), multiple types of text (statutes, judicial opinions, contracts, etc.), and multiple areas of law (evidence, contracts, civil procedure, etc.). For more information on tasks, we recommend visiting the website, where you can search through task descriptions, or the Github repository, which contains more granular task descriptions. We also recommend reading the paper, which provides more background on task significance and construction process.", "### Languages\n\nAll LegalBench tasks are in English.", "## Dataset Structure", "### Data Instances\n\nDetailed descriptions of the instances for each task can be found on the Github. An example of an instance, for the 'abercrombie' task, is provided below:\n\n\n\nA substantial number of LegalBench tasks are binary classification tasks, which require the LLM to determine if a piece of text has some legal attribute. Because these are framed as Yes/No questions, the label space is \"Yes\" or \"No\".", "### Data Fields\n\nDetailed descriptions of the instances for each task can be found on the Github.", "### Data Splits\n\nEach task has a training and evaluation split. Following RAFT, train splits only consists of a few-labeled instances, reflecting the few-shot nature of most LLMs.", "## Dataset Creation", "### Curation Rationale\n\nLegalBench was created to enable researchers to better benchmark the legal reasoning capabilities of LLMs.", "### Source Data", "#### Initial Data Collection and Normalization\n\nBroadly, LegalBench tasks are drawn from three sources. The first source of tasks are existing available datasets and corpora. Most of these were originally released for non-LLM evaluation settings. In creating tasks for LegalBench from these sources, we often significantly reformatted data and restructured the prediction objective. For instance, the original CUAD dataset contains annotations on long-documents and is intended for evaluating extraction with span-prediction models. We restructure this corpora to generate a binary classification task for each type of contractual clause. While the original corpus emphasized the long-document aspects of contracts, our restructured tasks emphasize whether LLMs can identify the distinguishing features of different types of clauses. The second source of tasks are datasets that were previously constructed by legal professionals but never released. This primarily includes datasets hand-coded by legal scholars as part of prior empirical legal projects. The last category of tasks are those that were developed specifically for \\name, by the authors of this paper. Overall, tasks are drawn from 36 distinct corpora. Please see the Appendix of the paper for more details.", "#### Who are the source language producers?\n\nLegalBench data was created by humans. Demographic information for these individuals is not available.", "### Annotations", "#### Annotation process\n\nPlease see the paper for more information on the annotation process used in the creation of each task.", "#### Who are the annotators?\n\nPlease see the paper for more information on the identity of annotators for each task.", "### Personal and Sensitive Information\n\nData in this benchmark has either been synthetically generated, or derived from an already public source (e.g., contracts from the EDGAR database). \n\nSeveral tasks have been derived from the LearnedHands corpus, which consists of public posts on /r/LegalAdvice. Some posts may discuss sensitive issues.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nPlease see the original paper for a discussion of social impact.", "### Discussion of Biases\n\nPlease see the original paper for a discussion of social impact.", "### Other Known Limitations\n\nLegalBench primarily contains tasks corresponding to American law.", "## Additional Information", "### Dataset Curators\n\nPlease see the website for a full list of participants in the LegalBench project.", "### Licensing Information\n\nLegalBench tasks are subject to different licenses. Please see the paper for a description of the licenses.\n\n\n\nIf you intend to reference LegalBench broadly, please use the citation below. If you are working with a particular task, please use the citation below in addition to the task specific citation (which can be found on the task page on the website or Github)." ]
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[ "passage: TAGS\n#task_categories-text-classification #task_categories-question-answering #task_categories-text-generation #size_categories-10K<n<100K #language-English #license-other #legal #law #finance #arxiv-2308.11462 #region-us \n# Dataset Card for Dataset Name\n\n\n- Homepage: URL \n- Repository: URL \n- Paper: URL## Dataset Description### Dataset Summary\n\nThe LegalBench project is an ongoing open science effort to collaboratively curate tasks for evaluating legal reasoning in English large language models (LLMs). The benchmark currently consists of 162 tasks gathered from 40 contributors. \n\nIf you have questions about the project or would like to get involved, please see the website for more information.### Supported Tasks and Leaderboards\n\nLegalBench tasks span multiple types (binary classification, multi-class classification, extraction, generation, entailment), multiple types of text (statutes, judicial opinions, contracts, etc.), and multiple areas of law (evidence, contracts, civil procedure, etc.). For more information on tasks, we recommend visiting the website, where you can search through task descriptions, or the Github repository, which contains more granular task descriptions. We also recommend reading the paper, which provides more background on task significance and construction process.### Languages\n\nAll LegalBench tasks are in English.## Dataset Structure### Data Instances\n\nDetailed descriptions of the instances for each task can be found on the Github. An example of an instance, for the 'abercrombie' task, is provided below:\n\n\n\nA substantial number of LegalBench tasks are binary classification tasks, which require the LLM to determine if a piece of text has some legal attribute. Because these are framed as Yes/No questions, the label space is \"Yes\" or \"No\".### Data Fields\n\nDetailed descriptions of the instances for each task can be found on the Github.", "passage: ### Data Splits\n\nEach task has a training and evaluation split. Following RAFT, train splits only consists of a few-labeled instances, reflecting the few-shot nature of most LLMs.## Dataset Creation### Curation Rationale\n\nLegalBench was created to enable researchers to better benchmark the legal reasoning capabilities of LLMs.### Source Data#### Initial Data Collection and Normalization\n\nBroadly, LegalBench tasks are drawn from three sources. The first source of tasks are existing available datasets and corpora. Most of these were originally released for non-LLM evaluation settings. In creating tasks for LegalBench from these sources, we often significantly reformatted data and restructured the prediction objective. For instance, the original CUAD dataset contains annotations on long-documents and is intended for evaluating extraction with span-prediction models. We restructure this corpora to generate a binary classification task for each type of contractual clause. While the original corpus emphasized the long-document aspects of contracts, our restructured tasks emphasize whether LLMs can identify the distinguishing features of different types of clauses. The second source of tasks are datasets that were previously constructed by legal professionals but never released. This primarily includes datasets hand-coded by legal scholars as part of prior empirical legal projects. The last category of tasks are those that were developed specifically for \\name, by the authors of this paper. Overall, tasks are drawn from 36 distinct corpora. Please see the Appendix of the paper for more details.#### Who are the source language producers?\n\nLegalBench data was created by humans. Demographic information for these individuals is not available.### Annotations#### Annotation process\n\nPlease see the paper for more information on the annotation process used in the creation of each task.#### Who are the annotators?\n\nPlease see the paper for more information on the identity of annotators for each task.### Personal and Sensitive Information\n\nData in this benchmark has either been synthetically generated, or derived from an already public source (e.g., contracts from the EDGAR database). \n\nSeveral tasks have been derived from the LearnedHands corpus, which consists of public posts on /r/LegalAdvice. Some posts may discuss sensitive issues.## Considerations for Using the Data" ]
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8b8ab0f76841ea858fe448788a4c2a9400e9c0f1
# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
radames/gradio_clicks_dataset
[ "region:us" ]
2023-03-16T23:30:15+00:00
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]}
2023-11-18T01:13:52+00:00
[]
[]
TAGS #region-us
# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions
[ "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]
[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]
[ 6, 8, 24, 6, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
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a3037faff25e2cf53e055fae9d4e2a90b6f091cc
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_T_C_A_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_T_C_A_rices_ns_1000
[ "region:us" ]
2023-03-17T00:00:37+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 620592, "num_examples": 1000}], "download_size": 111620, "dataset_size": 620592}}
2023-03-17T00:00:40+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_T_C_A_rices_ns_1000" More Information needed
[ "# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_T_C_A_rices_ns_1000\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_T_C_A_rices_ns_1000\"\n\nMore Information needed" ]
[ 6, 42 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_T_C_A_rices_ns_1000\"\n\nMore Information needed" ]
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579a7b4dd11763fad8c0d70198400ee667639659
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_rices_ns_1000
[ "region:us" ]
2023-03-17T00:09:16+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 2880491, "num_examples": 1000}, {"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 9790861, "num_examples": 1000}], "download_size": 2205019, "dataset_size": 12671352}}
2023-05-07T21:42:37+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_rices_ns_1000" More Information needed
[ "# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_rices_ns_1000\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_rices_ns_1000\"\n\nMore Information needed" ]
[ 6, 51 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_rices_ns_1000\"\n\nMore Information needed" ]
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a29b137e161454fc6d8aef5bddeb59fed0e027fe
# Dataset Card for "fakemon-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
RoryCochrane/fakemon-dataset
[ "region:us" ]
2023-03-17T00:14:55+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 156164016.815, "num_examples": 2363}], "download_size": 156073190, "dataset_size": 156164016.815}}
2023-04-02T02:57:40+00:00
[]
[]
TAGS #region-us
# Dataset Card for "fakemon-dataset" More Information needed
[ "# Dataset Card for \"fakemon-dataset\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"fakemon-dataset\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"fakemon-dataset\"\n\nMore Information needed" ]
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559151c506b1821c9e2002bb52af05318ec13855
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_T_C_A_OCR_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_T_C_A_OCR_rices_ns_1000
[ "region:us" ]
2023-03-17T00:17:04+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 806532, "num_examples": 1000}, {"name": "fewshot_5", "num_bytes": 4751103, "num_examples": 1000}, {"name": "fewshot_4", "num_bytes": 4035552, "num_examples": 1000}, {"name": "fewshot_3", "num_bytes": 3230669, "num_examples": 1000}], "download_size": 2121259, "dataset_size": 12823856}}
2023-03-17T02:55:27+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_T_C_A_OCR_rices_ns_1000" More Information needed
[ "# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_T_C_A_OCR_rices_ns_1000\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_T_C_A_OCR_rices_ns_1000\"\n\nMore Information needed" ]
[ 6, 45 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_T_C_A_OCR_rices_ns_1000\"\n\nMore Information needed" ]
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cc0dcee08e470012aa5e2d4f97024bf58daf0ed8
Dataset from: https://conala-corpus.github.io/ Uploaded here for easy remote access. To access in google colaboratory: ``` !pip install datasets from datasets import load_dataset import pandas as pd train = load_dataset('json', data_files='https://huggingface.co/datasets/janrauhl/conala/resolve/main/conala-train.json') test = load_dataset('json', data_files='https://huggingface.co/datasets/janrauhl/conala/resolve/main/conala-test.json') train = train['train'].to_pandas() test = test['train'].to_pandas() ```
janrauhl/conala
[ "region:us" ]
2023-03-17T00:58:13+00:00
{}
2023-03-17T02:00:23+00:00
[]
[]
TAGS #region-us
Dataset from: URL Uploaded here for easy remote access. To access in google colaboratory:
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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eeaf931dfc8220e68353164512f65b29b60ccc3f
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_T_A_C_OCR_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_T_A_C_OCR_rices_ns_1000
[ "region:us" ]
2023-03-17T01:07:51+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_10", "num_bytes": 8715503, "num_examples": 1000}, {"name": "fewshot_0", "num_bytes": 821592, "num_examples": 1000}, {"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 1123333, "num_examples": 1000}, {"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_Attributes_ViT_L_14_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 1141686, "num_examples": 1000}, {"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large__text", "num_bytes": 1120437, "num_examples": 1000}, {"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text", "num_bytes": 1298358, "num_examples": 1000}], "download_size": 2339668, "dataset_size": 14220909}}
2023-05-31T20:56:47+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_T_A_C_OCR_rices_ns_1000" More Information needed
[ "# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_T_A_C_OCR_rices_ns_1000\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_T_A_C_OCR_rices_ns_1000\"\n\nMore Information needed" ]
[ 6, 45 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_T_A_C_OCR_rices_ns_1000\"\n\nMore Information needed" ]
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b8e206d0b9311e686a657fb64e99aca4927dd5d2
# Dataset Card for "bookcorpus_stage2_coverage_20230316" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MartinKu/bookcorpus_stage2_coverage_20230316
[ "region:us" ]
2023-03-17T01:27:28+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "S_V_position", "sequence": "int64"}, {"name": "O_C_position", "sequence": "int64"}, {"name": "start_point_list", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 41290738426, "num_examples": 74004228}], "download_size": 5190943273, "dataset_size": 41290738426}}
2023-03-19T06:50:16+00:00
[]
[]
TAGS #region-us
# Dataset Card for "bookcorpus_stage2_coverage_20230316" More Information needed
[ "# Dataset Card for \"bookcorpus_stage2_coverage_20230316\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_stage2_coverage_20230316\"\n\nMore Information needed" ]
[ 6, 25 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_stage2_coverage_20230316\"\n\nMore Information needed" ]
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613bd8c35912eead650028981260b402e9a52f25
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_C_OCR_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_C_OCR_rices_ns_1000
[ "region:us" ]
2023-03-17T03:30:11+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 1709148, "num_examples": 1000}, {"name": "fewshot_1", "num_bytes": 1630082, "num_examples": 1000}], "download_size": 484952, "dataset_size": 3339230}}
2023-03-17T04:14:00+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_C_OCR_rices_ns_1000" More Information needed
[ "# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_C_OCR_rices_ns_1000\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_C_OCR_rices_ns_1000\"\n\nMore Information needed" ]
[ 6, 45 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_C_OCR_rices_ns_1000\"\n\nMore Information needed" ]
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521844ee80dd37760070142ce3d4b0b9b7d4b5cd
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_C_OCR_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_C_OCR_rices_ns_1000
[ "region:us" ]
2023-03-17T03:37:48+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 461028, "num_examples": 1000}, {"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 533464, "num_examples": 1000}], "download_size": 129221, "dataset_size": 994492}}
2023-06-20T23:26:44+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_C_OCR_rices_ns_1000" More Information needed
[ "# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_C_OCR_rices_ns_1000\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_C_OCR_rices_ns_1000\"\n\nMore Information needed" ]
[ 6, 41 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_C_OCR_rices_ns_1000\"\n\nMore Information needed" ]
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1e70a222de2b9ff20f157d46e7e1370a3d2769eb
# Dataset Card for "Nolan_whisper_educate1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nolan1206/Nolan_whisper_educate1
[ "region:us" ]
2023-03-17T03:39:13+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 82981.0, "num_examples": 3}, {"name": "test", "num_bytes": 82981.0, "num_examples": 3}], "download_size": 84786, "dataset_size": 165962.0}}
2023-03-17T03:39:36+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Nolan_whisper_educate1" More Information needed
[ "# Dataset Card for \"Nolan_whisper_educate1\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Nolan_whisper_educate1\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Nolan_whisper_educate1\"\n\nMore Information needed" ]
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01f13beca2a6ea57e520b7b1cda29e23429b743d
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_OCR_C_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_OCR_C_rices_ns_1000
[ "region:us" ]
2023-03-17T03:42:21+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 821808, "num_examples": 1000}], "download_size": 162892, "dataset_size": 821808}}
2023-03-17T03:42:23+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_OCR_C_rices_ns_1000" More Information needed
[ "# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_OCR_C_rices_ns_1000\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_OCR_C_rices_ns_1000\"\n\nMore Information needed" ]
[ 6, 45 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_OCR_C_rices_ns_1000\"\n\nMore Information needed" ]
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ff907e78a848852f489f0fc9bbe25b79e0fa16e2
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_D_PNP_FILTER_C_OCR_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_D_PNP_FILTER_C_OCR_rices_ns_1000
[ "region:us" ]
2023-03-17T04:25:18+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 9642700, "num_examples": 1000}, {"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 9911100, "num_examples": 1000}], "download_size": 3522817, "dataset_size": 19553800}}
2023-05-07T21:30:52+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_D_PNP_FILTER_C_OCR_rices_ns_1000" More Information needed
[ "# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_D_PNP_FILTER_C_OCR_rices_ns_1000\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_D_PNP_FILTER_C_OCR_rices_ns_1000\"\n\nMore Information needed" ]
[ 6, 54 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Hatefulmemes_test_google_flan_t5_xxl_mode_A_T_D_PNP_FILTER_C_OCR_rices_ns_1000\"\n\nMore Information needed" ]
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dac866072da6e2f01ec85a19b2c7bd21fe27ff1e
# AutoTrain Dataset for project: cv-sentiment ## Dataset Description This dataset has been automatically processed by AutoTrain for project cv-sentiment. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "text": "I have an educational background in the Information Technology, I graduated from Informatics Engineering at Parahyangan Catholic University in Bandung. I made a final project about Development of BPMS in Mobile Cordova Platform (Coordova Tasklist). I really excited learning new things such as my final project of learning about cordova and test the effectiveness and reusability in the business process management system.", "target": 1 }, { "text": "A college student who love technology and create projects about web and multi-platform apps.", "target": 0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "ClassLabel(names=['0', '1', '2', '3'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 77 | | valid | 22 |
guriko/autotrain-data-cv-sentiment
[ "task_categories:text-classification", "language:en", "region:us" ]
2023-03-17T06:07:16+00:00
{"language": ["en"], "task_categories": ["text-classification"]}
2023-03-17T06:07:41+00:00
[]
[ "en" ]
TAGS #task_categories-text-classification #language-English #region-us
AutoTrain Dataset for project: cv-sentiment =========================================== Dataset Description ------------------- This dataset has been automatically processed by AutoTrain for project cv-sentiment. ### Languages The BCP-47 code for the dataset's language is en. Dataset Structure ----------------- ### Data Instances A sample from this dataset looks as follows: ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow:
[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ "TAGS\n#task_categories-text-classification #language-English #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ 21, 26, 17, 23, 27 ]
[ "passage: TAGS\n#task_categories-text-classification #language-English #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
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6b6800df2e83f4d0148aa35e833ee0e2080d5e65
## Dataset description This dataset was used to fine-tune this [model](https://huggingface.co/keras-dreambooth/dreambooth_diffusion_toy) ## Demo You can try with this [demo](https://huggingface.co/keras-dreambooth/dreambooth_diffusion_toy) ## Intended uses & limitations Image of mother rabbit toy
keras-dreambooth/rabbit-toy
[ "size_categories:n<1K", "license:apache-2.0", "keras-dreambooth", "scifi", "diffusers", "text-to-image", "region:us" ]
2023-03-17T06:46:56+00:00
{"license": "apache-2.0", "size_categories": ["n<1K"], "tags": ["keras-dreambooth", "scifi", "diffusers", "text-to-image"]}
2023-03-18T07:36:13+00:00
[]
[]
TAGS #size_categories-n<1K #license-apache-2.0 #keras-dreambooth #scifi #diffusers #text-to-image #region-us
## Dataset description This dataset was used to fine-tune this model ## Demo You can try with this demo ## Intended uses & limitations Image of mother rabbit toy
[ "## Dataset description\nThis dataset was used to fine-tune this model", "## Demo \nYou can try with this demo", "## Intended uses & limitations\nImage of mother rabbit toy" ]
[ "TAGS\n#size_categories-n<1K #license-apache-2.0 #keras-dreambooth #scifi #diffusers #text-to-image #region-us \n", "## Dataset description\nThis dataset was used to fine-tune this model", "## Demo \nYou can try with this demo", "## Intended uses & limitations\nImage of mother rabbit toy" ]
[ 44, 15, 8, 15 ]
[ "passage: TAGS\n#size_categories-n<1K #license-apache-2.0 #keras-dreambooth #scifi #diffusers #text-to-image #region-us \n## Dataset description\nThis dataset was used to fine-tune this model## Demo \nYou can try with this demo## Intended uses & limitations\nImage of mother rabbit toy" ]
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4958a03c342fa1bbe9a38fd4bba6e423254f62f5
# Dataset Card for "flores200_8_val_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hlillemark/flores200_8_val_test
[ "region:us" ]
2023-03-17T08:03:35+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "val", "num_bytes": 1886788.3333333333, "num_examples": 5000}, {"name": "test", "num_bytes": 3773576.6666666665, "num_examples": 10000}], "download_size": 3662000, "dataset_size": 5660365.0}}
2023-03-17T08:08:35+00:00
[]
[]
TAGS #region-us
# Dataset Card for "flores200_8_val_test" More Information needed
[ "# Dataset Card for \"flores200_8_val_test\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"flores200_8_val_test\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"flores200_8_val_test\"\n\nMore Information needed" ]
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ff5e5a4873870483ea2dd3902834a848ff721847
# Dataset Card for "flores200_8_baseline" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hlillemark/flores200_8_baseline
[ "region:us" ]
2023-03-17T08:06:53+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7505410300, "num_examples": 20480000}], "download_size": 5077247260, "dataset_size": 7505410300}}
2023-03-17T08:13:10+00:00
[]
[]
TAGS #region-us
# Dataset Card for "flores200_8_baseline" More Information needed
[ "# Dataset Card for \"flores200_8_baseline\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"flores200_8_baseline\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"flores200_8_baseline\"\n\nMore Information needed" ]
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3c3d66edb30ba6c4649445c92af0b6e8d4459b77
# Dataset Card for Chatbot Instruction Prompts Datasets ### Dataset Summary This dataset has been generated from the following ones: - `tatsu-lab/alpaca` - `Dahoas/instruct-human-assistant-prompt` - `allenai/prosocial-dialog` The datasets has been cleaned up of spurious entries and artifacts. It contains ~500k of prompt and expected resposne. This DB is intended to train an instruct-type model
alespalla/chatbot_instruction_prompts
[ "task_categories:question-answering", "task_categories:conversational", "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "region:us" ]
2023-03-17T08:44:25+00:00
{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["question-answering", "conversational", "text-generation"], "dataset_info": {"features": [{"name": "response", "dtype": "string"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 24612503, "num_examples": 64511}, {"name": "train", "num_bytes": 98485829, "num_examples": 258042}], "download_size": 78591384, "dataset_size": 123098332}}
2023-03-21T13:36:36+00:00
[]
[ "en" ]
TAGS #task_categories-question-answering #task_categories-conversational #task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us
# Dataset Card for Chatbot Instruction Prompts Datasets ### Dataset Summary This dataset has been generated from the following ones: - 'tatsu-lab/alpaca' - 'Dahoas/instruct-human-assistant-prompt' - 'allenai/prosocial-dialog' The datasets has been cleaned up of spurious entries and artifacts. It contains ~500k of prompt and expected resposne. This DB is intended to train an instruct-type model
[ "# Dataset Card for Chatbot Instruction Prompts Datasets", "### Dataset Summary\n\nThis dataset has been generated from the following ones:\n\n- 'tatsu-lab/alpaca'\n- 'Dahoas/instruct-human-assistant-prompt'\n- 'allenai/prosocial-dialog'\n\nThe datasets has been cleaned up of spurious entries and artifacts. It contains ~500k of prompt and expected resposne. This DB is intended to train an instruct-type model" ]
[ "TAGS\n#task_categories-question-answering #task_categories-conversational #task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us \n", "# Dataset Card for Chatbot Instruction Prompts Datasets", "### Dataset Summary\n\nThis dataset has been generated from the following ones:\n\n- 'tatsu-lab/alpaca'\n- 'Dahoas/instruct-human-assistant-prompt'\n- 'allenai/prosocial-dialog'\n\nThe datasets has been cleaned up of spurious entries and artifacts. It contains ~500k of prompt and expected resposne. This DB is intended to train an instruct-type model" ]
[ 63, 15, 102 ]
[ "passage: TAGS\n#task_categories-question-answering #task_categories-conversational #task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us \n# Dataset Card for Chatbot Instruction Prompts Datasets### Dataset Summary\n\nThis dataset has been generated from the following ones:\n\n- 'tatsu-lab/alpaca'\n- 'Dahoas/instruct-human-assistant-prompt'\n- 'allenai/prosocial-dialog'\n\nThe datasets has been cleaned up of spurious entries and artifacts. It contains ~500k of prompt and expected resposne. This DB is intended to train an instruct-type model" ]
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a089cd182378a054bfe0cd9cca69323f47d982dd
## Corpus Summary This corpus has 192050 entries made up of descriptive sentences of the faces of the CelebA dataset. The preprocessing of the corpus has been to translate into Spanish the captions of the CelebA dataset with the algorithm used in [Text2FaceGAN](https://arxiv.org/pdf/1911.11378.pdf). In particular, all sentences are combined to generate a larger corpus. Additionally, a data preprocessing was applied that consists of eliminating stopwords, separation symbols and complementary elements that are not useful for training. Finally, using the Sent2vec library and the corpus, training was done to obtain an encoder model for sentences in the Spanish language. Specifically for captions from the CelebA dataset The training of Sent2vec + CelebA, using the present corpus was developed, resulting in the new model [Sent2vec-CelebA-Sp](https://huggingface.co/oeg/Sent2vec_CelebA_Sp). ## Corpus Fields Each corpus entry is composed of: - Descriptive sentence of a face from the CelebA dataset applied the corresponding preprocessing. You can download the file with a _.txt_ or _.csv_ extension as appropriate. ## Citation information **Citing**: If you used CelebA_Sent2vec_Sp corpus in your work, please cite the paper publish in **[Information Processing and Management](https://doi.org/10.1016/j.ipm.2024.103667)**: ```bib @article{YAURILOZANO2024103667, title = {Generative Adversarial Networks for text-to-face synthesis & generation: A quantitative–qualitative analysis of Natural Language Processing encoders for Spanish}, journal = {Information Processing & Management}, volume = {61}, number = {3}, pages = {103667}, year = {2024}, issn = {0306-4573}, doi = {https://doi.org/10.1016/j.ipm.2024.103667}, url = {https://www.sciencedirect.com/science/article/pii/S030645732400027X}, author = {Eduardo Yauri-Lozano and Manuel Castillo-Cara and Luis Orozco-Barbosa and Raúl García-Castro} } ``` ## License This corpus is available under the **[Apache License 2.0](https://github.com/manwestc/TINTO/blob/main/LICENSE)**. ## Autors - [Eduardo Yauri Lozano](https://github.com/eduar03yauri) - [Manuel Castillo-Cara](https://github.com/manwestc) - [Raúl García-Castro](https://github.com/rgcmme) [*Universidad Nacional de Ingeniería*](https://www.uni.edu.pe/), [*Ontology Engineering Group*](https://oeg.fi.upm.es/), [*Universidad Politécnica de Madrid.*](https://www.upm.es/internacional) ## Contributors See the full list of contributors [here](https://github.com/eduar03yauri/DCGAN-text2face-forSpanish). <kbd><img src="https://www.uni.edu.pe/images/logos/logo_uni_2016.png" alt="Universidad Politécnica de Madrid" width="100"></kbd> <kbd><img src="https://raw.githubusercontent.com/oeg-upm/TINTO/main/assets/logo-oeg.png" alt="Ontology Engineering Group" width="100"></kbd> <kbd><img src="https://raw.githubusercontent.com/oeg-upm/TINTO/main/assets/logo-upm.png" alt="Universidad Politécnica de Madrid" width="100"></kbd>
oeg/CelebA_Sent2Vect_Sp
[ "task_categories:table-question-answering", "task_categories:question-answering", "task_categories:translation", "task_categories:text2text-generation", "size_categories:100M<n<1B", "language:es", "license:apache-2.0", "CelebA", "Spanish", "celebFaces attributes", "face detection", "face recognition", "arxiv:1911.11378", "doi:10.57967/hf/0446", "region:us" ]
2023-03-17T08:59:40+00:00
{"language": ["es"], "license": "apache-2.0", "size_categories": ["100M<n<1B"], "task_categories": ["table-question-answering", "question-answering", "translation", "text2text-generation"], "pretty_name": "Sent2vec+CelebA training corpus in Spanish", "tags": ["CelebA", "Spanish", "celebFaces attributes", "face detection", "face recognition"]}
2024-02-05T12:23:46+00:00
[ "1911.11378" ]
[ "es" ]
TAGS #task_categories-table-question-answering #task_categories-question-answering #task_categories-translation #task_categories-text2text-generation #size_categories-100M<n<1B #language-Spanish #license-apache-2.0 #CelebA #Spanish #celebFaces attributes #face detection #face recognition #arxiv-1911.11378 #doi-10.57967/hf/0446 #region-us
## Corpus Summary This corpus has 192050 entries made up of descriptive sentences of the faces of the CelebA dataset. The preprocessing of the corpus has been to translate into Spanish the captions of the CelebA dataset with the algorithm used in Text2FaceGAN. In particular, all sentences are combined to generate a larger corpus. Additionally, a data preprocessing was applied that consists of eliminating stopwords, separation symbols and complementary elements that are not useful for training. Finally, using the Sent2vec library and the corpus, training was done to obtain an encoder model for sentences in the Spanish language. Specifically for captions from the CelebA dataset The training of Sent2vec + CelebA, using the present corpus was developed, resulting in the new model Sent2vec-CelebA-Sp. ## Corpus Fields Each corpus entry is composed of: - Descriptive sentence of a face from the CelebA dataset applied the corresponding preprocessing. You can download the file with a _.txt_ or _.csv_ extension as appropriate. information Citing: If you used CelebA_Sent2vec_Sp corpus in your work, please cite the paper publish in Information Processing and Management: ## License This corpus is available under the Apache License 2.0. ## Autors - Eduardo Yauri Lozano - Manuel Castillo-Cara - Raúl García-Castro *Universidad Nacional de Ingeniería*, *Ontology Engineering Group*, *Universidad Politécnica de Madrid.* ## Contributors See the full list of contributors here. <kbd><img src="URL alt="Universidad Politécnica de Madrid" width="100"></kbd> <kbd><img src="URL alt="Ontology Engineering Group" width="100"></kbd> <kbd><img src="URL alt="Universidad Politécnica de Madrid" width="100"></kbd>
[ "## Corpus Summary\n\nThis corpus has 192050 entries made up of descriptive sentences of the faces of the CelebA dataset.\nThe preprocessing of the corpus has been to translate into Spanish the captions of the CelebA dataset with the algorithm used in Text2FaceGAN. \nIn particular, all sentences are combined to generate a larger corpus. Additionally, a data preprocessing was applied that consists of eliminating stopwords, separation symbols and complementary elements that are not useful for training.\nFinally, using the Sent2vec library and the corpus, training was done to obtain an encoder model for sentences in the Spanish language. Specifically for captions from the CelebA \ndataset\n\nThe training of Sent2vec + CelebA, using the present corpus was developed, resulting in the new model Sent2vec-CelebA-Sp.", "## Corpus Fields\nEach corpus entry is composed of:\n- Descriptive sentence of a face from the CelebA dataset applied the corresponding preprocessing.\n\nYou can download the file with a _.txt_ or _.csv_ extension as appropriate. \n\ninformation\nCiting: If you used CelebA_Sent2vec_Sp corpus in your work, please cite the paper publish in Information Processing and Management:", "## License\n\nThis corpus is available under the Apache License 2.0.", "## Autors\n- Eduardo Yauri Lozano\n- Manuel Castillo-Cara\n- Raúl García-Castro\n\n*Universidad Nacional de Ingeniería*, *Ontology Engineering Group*, *Universidad Politécnica de Madrid.*", "## Contributors\nSee the full list of contributors here.\n\n<kbd><img src=\"URL alt=\"Universidad Politécnica de Madrid\" width=\"100\"></kbd>\n<kbd><img src=\"URL alt=\"Ontology Engineering Group\" width=\"100\"></kbd> \n<kbd><img src=\"URL alt=\"Universidad Politécnica de Madrid\" width=\"100\"></kbd>" ]
[ "TAGS\n#task_categories-table-question-answering #task_categories-question-answering #task_categories-translation #task_categories-text2text-generation #size_categories-100M<n<1B #language-Spanish #license-apache-2.0 #CelebA #Spanish #celebFaces attributes #face detection #face recognition #arxiv-1911.11378 #doi-10.57967/hf/0446 #region-us \n", "## Corpus Summary\n\nThis corpus has 192050 entries made up of descriptive sentences of the faces of the CelebA dataset.\nThe preprocessing of the corpus has been to translate into Spanish the captions of the CelebA dataset with the algorithm used in Text2FaceGAN. \nIn particular, all sentences are combined to generate a larger corpus. Additionally, a data preprocessing was applied that consists of eliminating stopwords, separation symbols and complementary elements that are not useful for training.\nFinally, using the Sent2vec library and the corpus, training was done to obtain an encoder model for sentences in the Spanish language. Specifically for captions from the CelebA \ndataset\n\nThe training of Sent2vec + CelebA, using the present corpus was developed, resulting in the new model Sent2vec-CelebA-Sp.", "## Corpus Fields\nEach corpus entry is composed of:\n- Descriptive sentence of a face from the CelebA dataset applied the corresponding preprocessing.\n\nYou can download the file with a _.txt_ or _.csv_ extension as appropriate. \n\ninformation\nCiting: If you used CelebA_Sent2vec_Sp corpus in your work, please cite the paper publish in Information Processing and Management:", "## License\n\nThis corpus is available under the Apache License 2.0.", "## Autors\n- Eduardo Yauri Lozano\n- Manuel Castillo-Cara\n- Raúl García-Castro\n\n*Universidad Nacional de Ingeniería*, *Ontology Engineering Group*, *Universidad Politécnica de Madrid.*", "## Contributors\nSee the full list of contributors here.\n\n<kbd><img src=\"URL alt=\"Universidad Politécnica de Madrid\" width=\"100\"></kbd>\n<kbd><img src=\"URL alt=\"Ontology Engineering Group\" width=\"100\"></kbd> \n<kbd><img src=\"URL alt=\"Universidad Politécnica de Madrid\" width=\"100\"></kbd>" ]
[ 121, 195, 90, 13, 51, 100 ]
[ "passage: TAGS\n#task_categories-table-question-answering #task_categories-question-answering #task_categories-translation #task_categories-text2text-generation #size_categories-100M<n<1B #language-Spanish #license-apache-2.0 #CelebA #Spanish #celebFaces attributes #face detection #face recognition #arxiv-1911.11378 #doi-10.57967/hf/0446 #region-us \n## Corpus Summary\n\nThis corpus has 192050 entries made up of descriptive sentences of the faces of the CelebA dataset.\nThe preprocessing of the corpus has been to translate into Spanish the captions of the CelebA dataset with the algorithm used in Text2FaceGAN. \nIn particular, all sentences are combined to generate a larger corpus. Additionally, a data preprocessing was applied that consists of eliminating stopwords, separation symbols and complementary elements that are not useful for training.\nFinally, using the Sent2vec library and the corpus, training was done to obtain an encoder model for sentences in the Spanish language. Specifically for captions from the CelebA \ndataset\n\nThe training of Sent2vec + CelebA, using the present corpus was developed, resulting in the new model Sent2vec-CelebA-Sp.## Corpus Fields\nEach corpus entry is composed of:\n- Descriptive sentence of a face from the CelebA dataset applied the corresponding preprocessing.\n\nYou can download the file with a _.txt_ or _.csv_ extension as appropriate. \n\ninformation\nCiting: If you used CelebA_Sent2vec_Sp corpus in your work, please cite the paper publish in Information Processing and Management:## License\n\nThis corpus is available under the Apache License 2.0.## Autors\n- Eduardo Yauri Lozano\n- Manuel Castillo-Cara\n- Raúl García-Castro\n\n*Universidad Nacional de Ingeniería*, *Ontology Engineering Group*, *Universidad Politécnica de Madrid.*" ]
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bf18a8601885b61284f5134ff50a493069f17c30
Dataset generated from HKR train set using ScrabbleGAN ====================================================== Number of images: 2476836 Sources: * [HKR dataset](https://github.com/abdoelsayed2016/HKR_Dataset) * [ScrabbleGAN code](https://github.com/ai-forever/ScrabbleGAN)
nastyboget/gan_hkr_large
[ "task_categories:image-to-text", "size_categories:1M<n<10M", "language:ru", "license:mit", "region:us" ]
2023-03-17T09:09:34+00:00
{"language": ["ru"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["image-to-text"]}
2023-03-20T10:17:36+00:00
[]
[ "ru" ]
TAGS #task_categories-image-to-text #size_categories-1M<n<10M #language-Russian #license-mit #region-us
Dataset generated from HKR train set using ScrabbleGAN ====================================================== Number of images: 2476836 Sources: * HKR dataset * ScrabbleGAN code
[]
[ "TAGS\n#task_categories-image-to-text #size_categories-1M<n<10M #language-Russian #license-mit #region-us \n" ]
[ 40 ]
[ "passage: TAGS\n#task_categories-image-to-text #size_categories-1M<n<10M #language-Russian #license-mit #region-us \n" ]
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948d883aac8e449656494f9e8dd3254b3d102a8f
# Instruction-prompted cartoonization dataset This dataset was created from 5000 images randomly sampled from the [Imagenette dataset](https://github.com/fastai/imagenette). For more details on how the dataset was created, check out [this directory](https://github.com/sayakpaul/instruction-tuned-sd/tree/main/data_preparation). Following figure depicts the data preparation workflow: <p align="center"> <img src="https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/cartoonization_data_wheel.png" width=600/> </p> ## Known limitations and biases The dataset was derived from Imagenette, which, in turn, was derived from [ImageNet](https://www.image-net.org/). So, naturally, this dataset inherits the limitations and biases of ImageNet. ## Licensing The dataset was derived from Imagenette, which, in turn, was derived from [ImageNet](https://www.image-net.org/). So, this dataset's license is the same as ImageNet.
instruction-tuning-sd/cartoonization
[ "task_categories:image-to-image", "size_categories:1K<n<10K", "language:en", "region:us" ]
2023-03-17T09:13:34+00:00
{"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["image-to-image"], "dataset_info": {"features": [{"name": "original_image", "dtype": "image"}, {"name": "edit_prompt", "dtype": "string"}, {"name": "cartoonized_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3257571330, "num_examples": 5000}], "download_size": 3296272284, "dataset_size": 3257571330}}
2023-05-11T14:16:08+00:00
[]
[ "en" ]
TAGS #task_categories-image-to-image #size_categories-1K<n<10K #language-English #region-us
# Instruction-prompted cartoonization dataset This dataset was created from 5000 images randomly sampled from the Imagenette dataset. For more details on how the dataset was created, check out this directory. Following figure depicts the data preparation workflow: <p align="center"> <img src="URL width=600/> </p> ## Known limitations and biases The dataset was derived from Imagenette, which, in turn, was derived from ImageNet. So, naturally, this dataset inherits the limitations and biases of ImageNet. ## Licensing The dataset was derived from Imagenette, which, in turn, was derived from ImageNet. So, this dataset's license is the same as ImageNet.
[ "# Instruction-prompted cartoonization dataset \n\nThis dataset was created from 5000 images randomly sampled from the Imagenette dataset. For more \ndetails on how the dataset was created, check out this directory. \n\nFollowing figure depicts the data preparation workflow:\n\n<p align=\"center\">\n<img src=\"URL width=600/>\n</p>", "## Known limitations and biases\n\nThe dataset was derived from Imagenette, which, in turn, was derived from ImageNet. So, naturally, this\ndataset inherits the limitations and biases of ImageNet.", "## Licensing\n\nThe dataset was derived from Imagenette, which, in turn, was derived from ImageNet. So, this dataset's license \nis the same as ImageNet." ]
[ "TAGS\n#task_categories-image-to-image #size_categories-1K<n<10K #language-English #region-us \n", "# Instruction-prompted cartoonization dataset \n\nThis dataset was created from 5000 images randomly sampled from the Imagenette dataset. For more \ndetails on how the dataset was created, check out this directory. \n\nFollowing figure depicts the data preparation workflow:\n\n<p align=\"center\">\n<img src=\"URL width=600/>\n</p>", "## Known limitations and biases\n\nThe dataset was derived from Imagenette, which, in turn, was derived from ImageNet. So, naturally, this\ndataset inherits the limitations and biases of ImageNet.", "## Licensing\n\nThe dataset was derived from Imagenette, which, in turn, was derived from ImageNet. So, this dataset's license \nis the same as ImageNet." ]
[ 34, 83, 53, 41 ]
[ "passage: TAGS\n#task_categories-image-to-image #size_categories-1K<n<10K #language-English #region-us \n# Instruction-prompted cartoonization dataset \n\nThis dataset was created from 5000 images randomly sampled from the Imagenette dataset. For more \ndetails on how the dataset was created, check out this directory. \n\nFollowing figure depicts the data preparation workflow:\n\n<p align=\"center\">\n<img src=\"URL width=600/>\n</p>## Known limitations and biases\n\nThe dataset was derived from Imagenette, which, in turn, was derived from ImageNet. So, naturally, this\ndataset inherits the limitations and biases of ImageNet.## Licensing\n\nThe dataset was derived from Imagenette, which, in turn, was derived from ImageNet. So, this dataset's license \nis the same as ImageNet." ]
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1cc08a93db9161fcc9c21a40e2bb3e4ffc17524b
## Corpus Summary This corpus contains 250000 entries made up of a pair of sentences in Spanish and their respective similarity value in the range 0 to 1. This corpus was used in the training of the [sentence-transformer](https://www.sbert.net/) library to improve the efficiency of the [RoBERTa-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) base model. Each of the pairs of sentences are textual descriptions of the faces of the CelebA dataset, which were previously translated into Spanish. The process followed to generate it was: - First, a translation of the original English text into Spanish was made. The original corpus in English was obtained from the work [Text2faceGAN ](https://arxiv.org/pdf/1911.11378.pdf) - An algorithm was implemented that randomly selects two sentences from the translated corpus and calculates their similarity value. _Spacy_ was used to obtain the similarity value of each pair of sentences. - Since both _Spacy_ and most of the libraries to calculate sentence similarity only work in the English language, part of the algorithm consisted in additionally selecting the pair of sentences from the original corpus in English. Finally, the final training corpus for RoBERTa is defined by the Spanish text and the similarity score. - Each pair of sentences in Spanish and the similarity value separated by the character "|", are saved as entries of the new corpus. The training of RoBERTa-large-bne + CelebA, using the present corpus was developed, resulting in the new model [RoBERTa-celebA-Sp](https://huggingface.co/oeg/RoBERTa-CelebA-Sp/blob). ## Corpus Fields Each corpus entry is composed of: - Sentence A: Descriptive sentence of a CelebA face in Spanish. - Sentence B: Descriptive sentence of a CelebA face in Spanish. - Similarity Value: Similarity of sentence A and sentence B. Each component is separated by the character "|" with the structure: ``` SentenceA | Sentence B | similarity value ``` You can download the file with a _.txt_ or _.csv_ extension as appropriate. ## Citation information **Citing**: If you used CelebA_RoBERTa_Sp corpus in your work, please cite the paper publish in **[Information Processing and Management](https://doi.org/10.1016/j.ipm.2024.103667)**: ```bib @article{YAURILOZANO2024103667, title = {Generative Adversarial Networks for text-to-face synthesis & generation: A quantitative–qualitative analysis of Natural Language Processing encoders for Spanish}, journal = {Information Processing & Management}, volume = {61}, number = {3}, pages = {103667}, year = {2024}, issn = {0306-4573}, doi = {https://doi.org/10.1016/j.ipm.2024.103667}, url = {https://www.sciencedirect.com/science/article/pii/S030645732400027X}, author = {Eduardo Yauri-Lozano and Manuel Castillo-Cara and Luis Orozco-Barbosa and Raúl García-Castro} } ``` ## License This corpus is available under the **[Apache License 2.0](https://github.com/manwestc/TINTO/blob/main/LICENSE)**. ## Autors - [Eduardo Yauri Lozano](https://github.com/eduar03yauri) - [Manuel Castillo-Cara](https://github.com/manwestc) - [Raúl García-Castro](https://github.com/rgcmme) [*Universidad Nacional de Ingeniería*](https://www.uni.edu.pe/), [*Ontology Engineering Group*](https://oeg.fi.upm.es/), [*Universidad Politécnica de Madrid.*](https://www.upm.es/internacional) ## Contributors See the full list of contributors [here](https://github.com/eduar03yauri/DCGAN-text2face-forSpanish). <kbd><img src="https://www.uni.edu.pe/images/logos/logo_uni_2016.png" alt="Universidad Politécnica de Madrid" width="100"></kbd> <kbd><img src="https://raw.githubusercontent.com/oeg-upm/TINTO/main/assets/logo-oeg.png" alt="Ontology Engineering Group" width="100"></kbd> <kbd><img src="https://raw.githubusercontent.com/oeg-upm/TINTO/main/assets/logo-upm.png" alt="Universidad Politécnica de Madrid" width="100"></kbd>
oeg/CelebA_RoBERTa_Sp
[ "task_categories:table-question-answering", "task_categories:question-answering", "task_categories:translation", "task_categories:text2text-generation", "size_categories:100M<n<1B", "language:es", "license:apache-2.0", "CelebA", "Spanish", "celebFaces attributes", "face detection", "face recognition", "arxiv:1911.11378", "doi:10.57967/hf/0447", "region:us" ]
2023-03-17T09:22:42+00:00
{"language": ["es"], "license": "apache-2.0", "size_categories": ["100M<n<1B"], "task_categories": ["table-question-answering", "question-answering", "translation", "text2text-generation"], "pretty_name": "RoBERTa+CelebA training corpus in Spanish", "tags": ["CelebA", "Spanish", "celebFaces attributes", "face detection", "face recognition"]}
2024-02-05T12:24:06+00:00
[ "1911.11378" ]
[ "es" ]
TAGS #task_categories-table-question-answering #task_categories-question-answering #task_categories-translation #task_categories-text2text-generation #size_categories-100M<n<1B #language-Spanish #license-apache-2.0 #CelebA #Spanish #celebFaces attributes #face detection #face recognition #arxiv-1911.11378 #doi-10.57967/hf/0447 #region-us
## Corpus Summary This corpus contains 250000 entries made up of a pair of sentences in Spanish and their respective similarity value in the range 0 to 1. This corpus was used in the training of the sentence-transformer library to improve the efficiency of the RoBERTa-large-bne base model. Each of the pairs of sentences are textual descriptions of the faces of the CelebA dataset, which were previously translated into Spanish. The process followed to generate it was: - First, a translation of the original English text into Spanish was made. The original corpus in English was obtained from the work Text2faceGAN - An algorithm was implemented that randomly selects two sentences from the translated corpus and calculates their similarity value. _Spacy_ was used to obtain the similarity value of each pair of sentences. - Since both _Spacy_ and most of the libraries to calculate sentence similarity only work in the English language, part of the algorithm consisted in additionally selecting the pair of sentences from the original corpus in English. Finally, the final training corpus for RoBERTa is defined by the Spanish text and the similarity score. - Each pair of sentences in Spanish and the similarity value separated by the character "|", are saved as entries of the new corpus. The training of RoBERTa-large-bne + CelebA, using the present corpus was developed, resulting in the new model RoBERTa-celebA-Sp. ## Corpus Fields Each corpus entry is composed of: - Sentence A: Descriptive sentence of a CelebA face in Spanish. - Sentence B: Descriptive sentence of a CelebA face in Spanish. - Similarity Value: Similarity of sentence A and sentence B. Each component is separated by the character "|" with the structure: You can download the file with a _.txt_ or _.csv_ extension as appropriate. information Citing: If you used CelebA_RoBERTa_Sp corpus in your work, please cite the paper publish in Information Processing and Management: ## License This corpus is available under the Apache License 2.0. ## Autors - Eduardo Yauri Lozano - Manuel Castillo-Cara - Raúl García-Castro *Universidad Nacional de Ingeniería*, *Ontology Engineering Group*, *Universidad Politécnica de Madrid.* ## Contributors See the full list of contributors here. <kbd><img src="URL alt="Universidad Politécnica de Madrid" width="100"></kbd> <kbd><img src="URL alt="Ontology Engineering Group" width="100"></kbd> <kbd><img src="URL alt="Universidad Politécnica de Madrid" width="100"></kbd>
[ "## Corpus Summary\n\nThis corpus contains 250000 entries made up of a pair of sentences in Spanish and their respective similarity value in the range 0 to 1. This corpus was used in the training of the \nsentence-transformer library to improve the efficiency of the RoBERTa-large-bne base model.\nEach of the pairs of sentences are textual descriptions of the faces of the CelebA dataset, which were previously translated into Spanish. The process followed to generate it was:\n\n- First, a translation of the original English text into Spanish was made. The original corpus in English was obtained from the work Text2faceGAN \n \n- An algorithm was implemented that randomly selects two sentences from the translated corpus and calculates their similarity value. _Spacy_ was used to obtain the similarity value of each\n pair of sentences. \n- Since both _Spacy_ and most of the libraries to calculate sentence similarity only work in the English language, part of the algorithm consisted in additionally selecting the pair of sentences from the original corpus in English.\n Finally, the final training corpus for RoBERTa is defined by the Spanish text and the similarity score.\n- Each pair of sentences in Spanish and the similarity value separated by the character \"|\", are saved as entries of the new corpus. \n\nThe training of RoBERTa-large-bne + CelebA, using the present corpus was developed, resulting in the new model RoBERTa-celebA-Sp.", "## Corpus Fields\nEach corpus entry is composed of:\n- Sentence A: Descriptive sentence of a CelebA face in Spanish.\n- Sentence B: Descriptive sentence of a CelebA face in Spanish.\n- Similarity Value: Similarity of sentence A and sentence B.\n\nEach component is separated by the character \"|\" with the structure:\n\n\nYou can download the file with a _.txt_ or _.csv_ extension as appropriate.\n\ninformation\n\nCiting: If you used CelebA_RoBERTa_Sp corpus in your work, please cite the paper publish in Information Processing and Management:", "## License\n\nThis corpus is available under the Apache License 2.0.", "## Autors\n- Eduardo Yauri Lozano\n- Manuel Castillo-Cara\n- Raúl García-Castro\n\n*Universidad Nacional de Ingeniería*, *Ontology Engineering Group*, *Universidad Politécnica de Madrid.*", "## Contributors\nSee the full list of contributors here.\n\n<kbd><img src=\"URL alt=\"Universidad Politécnica de Madrid\" width=\"100\"></kbd>\n<kbd><img src=\"URL alt=\"Ontology Engineering Group\" width=\"100\"></kbd> \n<kbd><img src=\"URL alt=\"Universidad Politécnica de Madrid\" width=\"100\"></kbd>" ]
[ "TAGS\n#task_categories-table-question-answering #task_categories-question-answering #task_categories-translation #task_categories-text2text-generation #size_categories-100M<n<1B #language-Spanish #license-apache-2.0 #CelebA #Spanish #celebFaces attributes #face detection #face recognition #arxiv-1911.11378 #doi-10.57967/hf/0447 #region-us \n", "## Corpus Summary\n\nThis corpus contains 250000 entries made up of a pair of sentences in Spanish and their respective similarity value in the range 0 to 1. This corpus was used in the training of the \nsentence-transformer library to improve the efficiency of the RoBERTa-large-bne base model.\nEach of the pairs of sentences are textual descriptions of the faces of the CelebA dataset, which were previously translated into Spanish. The process followed to generate it was:\n\n- First, a translation of the original English text into Spanish was made. The original corpus in English was obtained from the work Text2faceGAN \n \n- An algorithm was implemented that randomly selects two sentences from the translated corpus and calculates their similarity value. _Spacy_ was used to obtain the similarity value of each\n pair of sentences. \n- Since both _Spacy_ and most of the libraries to calculate sentence similarity only work in the English language, part of the algorithm consisted in additionally selecting the pair of sentences from the original corpus in English.\n Finally, the final training corpus for RoBERTa is defined by the Spanish text and the similarity score.\n- Each pair of sentences in Spanish and the similarity value separated by the character \"|\", are saved as entries of the new corpus. \n\nThe training of RoBERTa-large-bne + CelebA, using the present corpus was developed, resulting in the new model RoBERTa-celebA-Sp.", "## Corpus Fields\nEach corpus entry is composed of:\n- Sentence A: Descriptive sentence of a CelebA face in Spanish.\n- Sentence B: Descriptive sentence of a CelebA face in Spanish.\n- Similarity Value: Similarity of sentence A and sentence B.\n\nEach component is separated by the character \"|\" with the structure:\n\n\nYou can download the file with a _.txt_ or _.csv_ extension as appropriate.\n\ninformation\n\nCiting: If you used CelebA_RoBERTa_Sp corpus in your work, please cite the paper publish in Information Processing and Management:", "## License\n\nThis corpus is available under the Apache License 2.0.", "## Autors\n- Eduardo Yauri Lozano\n- Manuel Castillo-Cara\n- Raúl García-Castro\n\n*Universidad Nacional de Ingeniería*, *Ontology Engineering Group*, *Universidad Politécnica de Madrid.*", "## Contributors\nSee the full list of contributors here.\n\n<kbd><img src=\"URL alt=\"Universidad Politécnica de Madrid\" width=\"100\"></kbd>\n<kbd><img src=\"URL alt=\"Ontology Engineering Group\" width=\"100\"></kbd> \n<kbd><img src=\"URL alt=\"Universidad Politécnica de Madrid\" width=\"100\"></kbd>" ]
[ 121, 330, 131, 13, 51, 100 ]
[ "passage: TAGS\n#task_categories-table-question-answering #task_categories-question-answering #task_categories-translation #task_categories-text2text-generation #size_categories-100M<n<1B #language-Spanish #license-apache-2.0 #CelebA #Spanish #celebFaces attributes #face detection #face recognition #arxiv-1911.11378 #doi-10.57967/hf/0447 #region-us \n## Corpus Summary\n\nThis corpus contains 250000 entries made up of a pair of sentences in Spanish and their respective similarity value in the range 0 to 1. This corpus was used in the training of the \nsentence-transformer library to improve the efficiency of the RoBERTa-large-bne base model.\nEach of the pairs of sentences are textual descriptions of the faces of the CelebA dataset, which were previously translated into Spanish. The process followed to generate it was:\n\n- First, a translation of the original English text into Spanish was made. The original corpus in English was obtained from the work Text2faceGAN \n \n- An algorithm was implemented that randomly selects two sentences from the translated corpus and calculates their similarity value. _Spacy_ was used to obtain the similarity value of each\n pair of sentences. \n- Since both _Spacy_ and most of the libraries to calculate sentence similarity only work in the English language, part of the algorithm consisted in additionally selecting the pair of sentences from the original corpus in English.\n Finally, the final training corpus for RoBERTa is defined by the Spanish text and the similarity score.\n- Each pair of sentences in Spanish and the similarity value separated by the character \"|\", are saved as entries of the new corpus. \n\nThe training of RoBERTa-large-bne + CelebA, using the present corpus was developed, resulting in the new model RoBERTa-celebA-Sp." ]
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5ab1d0822517c1470e4891277d9de0eead69b8c2
# Dataset Card for SciQA ## 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:** [SciQA Homepage]() - **Repository:** [SciQA Repository](https://zenodo.org/record/7744048) - **Paper:** The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge - **Point of Contact:** [Yaser Jaradeh](mailto:[email protected]) ### Dataset Summary SciQA contains 2,565 SPARQL query - question pairs along with answers fetched from the open research knowledge graph (ORKG) via a Virtuoso SPARQL endpoint, it is a collection of both handcrafted and autogenerated questions and queries. The dataset is split into 70% training, 10% validation and 20% test examples. ## Dataset Structure ### Data Instances An example of a question is given below: ```json { "id": "AQ2251", "query_type": "Factoid", "question": { "string": "Provide a list of papers that have utilized the Depth DDPPO model and include the links to their code?" }, "paraphrased_question": [], "query": { "sparql": "SELECT DISTINCT ?code\nWHERE {\n ?model a orkgc:Model;\n rdfs:label ?model_lbl.\n FILTER (str(?model_lbl) = \"Depth DDPPO\")\n ?benchmark orkgp:HAS_DATASET ?dataset.\n ?cont orkgp:HAS_BENCHMARK ?benchmark.\n ?cont orkgp:HAS_MODEL ?model;\n orkgp:HAS_SOURCE_CODE ?code.\n}" }, "template_id": "T07", "auto_generated": true, "query_shape": "Tree", "query_class": "WHICH-WHAT", "number_of_patterns": 4, } ``` ### Data Fields - `id`: the id of the question - `question`: a string containing the question - `paraphrased_question`: a set of paraphrased versions of the question - `query`: a SPARQL query that answers the question - `query_type`: the type of the query - `query_template`: an optional template of the query - `query_shape`: a string indicating the shape of the query - `query_class`: a string indicating the class of the query - `auto_generated`: a boolean indicating whether the question is auto-generated or not - `number_of_patterns`: an integer number indicating the number of gtaph patterns in the query ### Data Splits The dataset is split into 70% training, 10% validation and 20% test questions. ## Additional Information ### Licensing Information SciQA is licensed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). ### Citation Information ```bibtex @Article{SciQA2023, author={Auer, S{\"o}ren and Barone, Dante A. C. and Bartz, Cassiano and Cortes, Eduardo G. and Jaradeh, Mohamad Yaser and Karras, Oliver and Koubarakis, Manolis and Mouromtsev, Dmitry and Pliukhin, Dmitrii and Radyush, Daniil and Shilin, Ivan and Stocker, Markus and Tsalapati, Eleni}, title={The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge}, journal={Scientific Reports}, year={2023}, month={May}, day={04}, volume={13}, number={1}, pages={7240}, abstract={Knowledge graphs have gained increasing popularity in the last decade in science and technology. However, knowledge graphs are currently relatively simple to moderate semantic structures that are mainly a collection of factual statements. Question answering (QA) benchmarks and systems were so far mainly geared towards encyclopedic knowledge graphs such as DBpedia and Wikidata. We present SciQA a scientific QA benchmark for scholarly knowledge. The benchmark leverages the Open Research Knowledge Graph (ORKG) which includes almost 170,000 resources describing research contributions of almost 15,000 scholarly articles from 709 research fields. Following a bottom-up methodology, we first manually developed a set of 100 complex questions that can be answered using this knowledge graph. Furthermore, we devised eight question templates with which we automatically generated further 2465 questions, that can also be answered with the ORKG. The questions cover a range of research fields and question types and are translated into corresponding SPARQL queries over the ORKG. Based on two preliminary evaluations, we show that the resulting SciQA benchmark represents a challenging task for next-generation QA systems. This task is part of the open competitions at the 22nd International Semantic Web Conference 2023 as the Scholarly Question Answering over Linked Data (QALD) Challenge.}, issn={2045-2322}, doi={10.1038/s41598-023-33607-z}, url={https://doi.org/10.1038/s41598-023-33607-z} } ``` ### Contributions Thanks to [@YaserJaradeh](https://github.com/YaserJaradeh) for adding this dataset.
orkg/SciQA
[ "task_categories:question-answering", "annotations_creators:expert-generated", "annotations_creators:auto-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "knowledge-base-qa", "region:us" ]
2023-03-17T09:55:39+00:00
{"annotations_creators": ["expert-generated", "auto-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": [], "pretty_name": "The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge", "tags": ["knowledge-base-qa"]}
2023-05-22T09:13:44+00:00
[]
[ "en" ]
TAGS #task_categories-question-answering #annotations_creators-expert-generated #annotations_creators-auto-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #knowledge-base-qa #region-us
# Dataset Card for SciQA ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: [SciQA Homepage]() - Repository: SciQA Repository - Paper: The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge - Point of Contact: Yaser Jaradeh ### Dataset Summary SciQA contains 2,565 SPARQL query - question pairs along with answers fetched from the open research knowledge graph (ORKG) via a Virtuoso SPARQL endpoint, it is a collection of both handcrafted and autogenerated questions and queries. The dataset is split into 70% training, 10% validation and 20% test examples. ## Dataset Structure ### Data Instances An example of a question is given below: ### Data Fields - 'id': the id of the question - 'question': a string containing the question - 'paraphrased_question': a set of paraphrased versions of the question - 'query': a SPARQL query that answers the question - 'query_type': the type of the query - 'query_template': an optional template of the query - 'query_shape': a string indicating the shape of the query - 'query_class': a string indicating the class of the query - 'auto_generated': a boolean indicating whether the question is auto-generated or not - 'number_of_patterns': an integer number indicating the number of gtaph patterns in the query ### Data Splits The dataset is split into 70% training, 10% validation and 20% test questions. ## Additional Information ### Licensing Information SciQA is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). ### Contributions Thanks to @YaserJaradeh for adding this dataset.
[ "# Dataset Card for SciQA", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: [SciQA Homepage]()\n- Repository: SciQA Repository\n- Paper: The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge\n- Point of Contact: Yaser Jaradeh", "### Dataset Summary\n\nSciQA contains 2,565 SPARQL query - question pairs along with answers fetched from the open research knowledge graph (ORKG) via a Virtuoso SPARQL endpoint, it is a collection of both handcrafted and autogenerated questions and queries. The dataset is split into 70% training, 10% validation and 20% test examples.", "## Dataset Structure", "### Data Instances\n\nAn example of a question is given below:", "### Data Fields\n\n- 'id': the id of the question\n- 'question': a string containing the question\n- 'paraphrased_question': a set of paraphrased versions of the question\n- 'query': a SPARQL query that answers the question\n- 'query_type': the type of the query\n- 'query_template': an optional template of the query\n- 'query_shape': a string indicating the shape of the query\n- 'query_class': a string indicating the class of the query\n- 'auto_generated': a boolean indicating whether the question is auto-generated or not\n- 'number_of_patterns': an integer number indicating the number of gtaph patterns in the query", "### Data Splits\n\nThe dataset is split into 70% training, 10% validation and 20% test questions.", "## Additional Information", "### Licensing Information\n\nSciQA is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).", "### Contributions\n\nThanks to @YaserJaradeh for adding this dataset." ]
[ "TAGS\n#task_categories-question-answering #annotations_creators-expert-generated #annotations_creators-auto-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #knowledge-base-qa #region-us \n", "# Dataset Card for SciQA", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: [SciQA Homepage]()\n- Repository: SciQA Repository\n- Paper: The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge\n- Point of Contact: Yaser Jaradeh", "### Dataset Summary\n\nSciQA contains 2,565 SPARQL query - question pairs along with answers fetched from the open research knowledge graph (ORKG) via a Virtuoso SPARQL endpoint, it is a collection of both handcrafted and autogenerated questions and queries. The dataset is split into 70% training, 10% validation and 20% test examples.", "## Dataset Structure", "### Data Instances\n\nAn example of a question is given below:", "### Data Fields\n\n- 'id': the id of the question\n- 'question': a string containing the question\n- 'paraphrased_question': a set of paraphrased versions of the question\n- 'query': a SPARQL query that answers the question\n- 'query_type': the type of the query\n- 'query_template': an optional template of the query\n- 'query_shape': a string indicating the shape of the query\n- 'query_class': a string indicating the class of the query\n- 'auto_generated': a boolean indicating whether the question is auto-generated or not\n- 'number_of_patterns': an integer number indicating the number of gtaph patterns in the query", "### Data Splits\n\nThe dataset is split into 70% training, 10% validation and 20% test questions.", "## Additional Information", "### Licensing Information\n\nSciQA is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).", "### Contributions\n\nThanks to @YaserJaradeh for adding this dataset." ]
[ 103, 7, 120, 53, 86, 6, 15, 188, 22, 5, 24, 19 ]
[ "passage: TAGS\n#task_categories-question-answering #annotations_creators-expert-generated #annotations_creators-auto-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #knowledge-base-qa #region-us \n# Dataset Card for SciQA## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage: [SciQA Homepage]()\n- Repository: SciQA Repository\n- Paper: The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge\n- Point of Contact: Yaser Jaradeh### Dataset Summary\n\nSciQA contains 2,565 SPARQL query - question pairs along with answers fetched from the open research knowledge graph (ORKG) via a Virtuoso SPARQL endpoint, it is a collection of both handcrafted and autogenerated questions and queries. The dataset is split into 70% training, 10% validation and 20% test examples.## Dataset Structure### Data Instances\n\nAn example of a question is given below:" ]
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af57d975914fd294d2d02c572dab2b3e5fa69fe7
A collection of fashion images for the [PromptHero Academy Students](https://prompthero.com/academy/courses). ![Group 51.png](https://s3.amazonaws.com/moonup/production/uploads/1679051855731-63265d019f9d19bfd4f45031.png)
prompthero-diffusion-models/fashion-diffusion
[ "region:us" ]
2023-03-17T11:09:34+00:00
{}
2023-03-17T17:50:51+00:00
[]
[]
TAGS #region-us
A collection of fashion images for the PromptHero Academy Students. !Group URL
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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82eab0253983c1e3c2bd8ea14f552e149141b3a4
# Dataset Card for "processed_gpt_dataset_medium" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sanagnos/processed_gpt_dataset_medium
[ "region:us" ]
2023-03-17T12:24:49+00:00
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 14320218276.0, "num_examples": 9250787}], "download_size": 4490005652, "dataset_size": 14320218276.0}}
2023-03-17T12:39:41+00:00
[]
[]
TAGS #region-us
# Dataset Card for "processed_gpt_dataset_medium" More Information needed
[ "# Dataset Card for \"processed_gpt_dataset_medium\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"processed_gpt_dataset_medium\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"processed_gpt_dataset_medium\"\n\nMore Information needed" ]
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4787c0aae32f0ba7b9e0fba8944292f55a27340e
# Latvian text dataset Data set of latvian language texts. Intended for use in AI tool development, like speech recognition or spellcheckers ## Data sources used * Latvian Wikisource articles - https://wikisource.org/wiki/Category:Latvian * Literary works of Rainis - https://repository.clarin.lv/repository/xmlui/handle/20.500.12574/41 * Latvian Wikipedia articles - https://huggingface.co/datasets/joelito/EU_Wikipedias * European Parliament Proceedings Parallel Corpus - https://huggingface.co/datasets/europarl_bilingual * Tilde MODEL Corpus - Multilingual Open Data for European Languages - https://huggingface.co/datasets/tilde_model To get Wikipedia dataset (197MB) run. ``` python tools/wikipedia/GetWikipedia.py ``` To get Europarl dataset (1.7GB) run. ``` python tools/europarl/GetEuroparl.py ``` To get Tilde dataset (834MB) run. ``` python tools/europarl/GetTilde.py ``` To combine all datasets run ``` sh combine-all.sh ``` To clean out some junk run. ``` sh clean.sh ``` Also maybe you want to remove duplocate lines. To do so run ``` sort lv.txt | uniq > lv-uniq.txt ``` ## Notes Possible future sources * Parliament proceedings transcripts - https://www.saeima.lv/lv/transcripts * Discussions of Latvian Wikipedia pages - https://lv.wikipedia.org/wiki/Special:AllPages * Out of copyright books from LNB collection - https://data.gov.lv/dati/lv/dataset/gramatu-digitala-kolekcija Data sets not used * Web scrapes, as they tend to yield data from comments with improper spelling like "atrashanaas vieta" instead of "atrašanās vieta" * Open Subtitles, as they contain data with improper spelling like "atrashanaas vieta" instead of "atrašanās vieta" Possible issues: * Data sets contain foreign language characters, like "蠻子" or cyrilic f.e. "Рига"
RaivisDejus/latvian-text
[ "task_categories:automatic-speech-recognition", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|tilde_model", "source_datasets:extended|wikipedia", "source_datasets:extended|europarl_bilingual", "language:lv", "license:cc-by-4.0", "lv", "latvian", "region:us" ]
2023-03-17T12:54:45+00:00
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["lv"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|tilde_model", "extended|wikipedia", "extended|europarl_bilingual"], "task_categories": ["automatic-speech-recognition"], "task_ids": [], "pretty_name": "Latvian text dataset", "tags": ["lv", "latvian"]}
2023-04-01T12:37:35+00:00
[]
[ "lv" ]
TAGS #task_categories-automatic-speech-recognition #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|tilde_model #source_datasets-extended|wikipedia #source_datasets-extended|europarl_bilingual #language-Latvian #license-cc-by-4.0 #lv #latvian #region-us
# Latvian text dataset Data set of latvian language texts. Intended for use in AI tool development, like speech recognition or spellcheckers ## Data sources used * Latvian Wikisource articles - URL * Literary works of Rainis - URL * Latvian Wikipedia articles - URL * European Parliament Proceedings Parallel Corpus - URL * Tilde MODEL Corpus - Multilingual Open Data for European Languages - URL To get Wikipedia dataset (197MB) run. To get Europarl dataset (1.7GB) run. To get Tilde dataset (834MB) run. To combine all datasets run To clean out some junk run. Also maybe you want to remove duplocate lines. To do so run ## Notes Possible future sources * Parliament proceedings transcripts - URL * Discussions of Latvian Wikipedia pages - URL * Out of copyright books from LNB collection - URL Data sets not used * Web scrapes, as they tend to yield data from comments with improper spelling like "atrashanaas vieta" instead of "atrašanās vieta" * Open Subtitles, as they contain data with improper spelling like "atrashanaas vieta" instead of "atrašanās vieta" Possible issues: * Data sets contain foreign language characters, like "蠻子" or cyrilic f.e. "Рига"
[ "# Latvian text dataset\n\nData set of latvian language texts. Intended for use in AI tool development, like speech recognition or spellcheckers", "## Data sources used\n* Latvian Wikisource articles - URL\n* Literary works of Rainis - URL\n* Latvian Wikipedia articles - URL\n* European Parliament Proceedings Parallel Corpus - URL\n* Tilde MODEL Corpus - Multilingual Open Data for European Languages - URL\n\n\n\nTo get Wikipedia dataset (197MB) run. \n\n\nTo get Europarl dataset (1.7GB) run. \n\n\nTo get Tilde dataset (834MB) run. \n\n\nTo combine all datasets run \n\n\nTo clean out some junk run.\n\n\nAlso maybe you want to remove duplocate lines. To do so run", "## Notes\n\nPossible future sources\n* Parliament proceedings transcripts - URL\n* Discussions of Latvian Wikipedia pages - URL\n* Out of copyright books from LNB collection - URL\n\nData sets not used\n* Web scrapes, as they tend to yield data from comments with improper spelling like \"atrashanaas vieta\" instead of \"atrašanās vieta\"\n* Open Subtitles, as they contain data with improper spelling like \"atrashanaas vieta\" instead of \"atrašanās vieta\"\n\nPossible issues:\n* Data sets contain foreign language characters, like \"蠻子\" or cyrilic f.e. \"Рига\"" ]
[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|tilde_model #source_datasets-extended|wikipedia #source_datasets-extended|europarl_bilingual #language-Latvian #license-cc-by-4.0 #lv #latvian #region-us \n", "# Latvian text dataset\n\nData set of latvian language texts. Intended for use in AI tool development, like speech recognition or spellcheckers", "## Data sources used\n* Latvian Wikisource articles - URL\n* Literary works of Rainis - URL\n* Latvian Wikipedia articles - URL\n* European Parliament Proceedings Parallel Corpus - URL\n* Tilde MODEL Corpus - Multilingual Open Data for European Languages - URL\n\n\n\nTo get Wikipedia dataset (197MB) run. \n\n\nTo get Europarl dataset (1.7GB) run. \n\n\nTo get Tilde dataset (834MB) run. \n\n\nTo combine all datasets run \n\n\nTo clean out some junk run.\n\n\nAlso maybe you want to remove duplocate lines. To do so run", "## Notes\n\nPossible future sources\n* Parliament proceedings transcripts - URL\n* Discussions of Latvian Wikipedia pages - URL\n* Out of copyright books from LNB collection - URL\n\nData sets not used\n* Web scrapes, as they tend to yield data from comments with improper spelling like \"atrashanaas vieta\" instead of \"atrašanās vieta\"\n* Open Subtitles, as they contain data with improper spelling like \"atrashanaas vieta\" instead of \"atrašanās vieta\"\n\nPossible issues:\n* Data sets contain foreign language characters, like \"蠻子\" or cyrilic f.e. \"Рига\"" ]
[ 125, 33, 120, 137 ]
[ "passage: TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|tilde_model #source_datasets-extended|wikipedia #source_datasets-extended|europarl_bilingual #language-Latvian #license-cc-by-4.0 #lv #latvian #region-us \n# Latvian text dataset\n\nData set of latvian language texts. Intended for use in AI tool development, like speech recognition or spellcheckers## Data sources used\n* Latvian Wikisource articles - URL\n* Literary works of Rainis - URL\n* Latvian Wikipedia articles - URL\n* European Parliament Proceedings Parallel Corpus - URL\n* Tilde MODEL Corpus - Multilingual Open Data for European Languages - URL\n\n\n\nTo get Wikipedia dataset (197MB) run. \n\n\nTo get Europarl dataset (1.7GB) run. \n\n\nTo get Tilde dataset (834MB) run. \n\n\nTo combine all datasets run \n\n\nTo clean out some junk run.\n\n\nAlso maybe you want to remove duplocate lines. To do so run## Notes\n\nPossible future sources\n* Parliament proceedings transcripts - URL\n* Discussions of Latvian Wikipedia pages - URL\n* Out of copyright books from LNB collection - URL\n\nData sets not used\n* Web scrapes, as they tend to yield data from comments with improper spelling like \"atrashanaas vieta\" instead of \"atrašanās vieta\"\n* Open Subtitles, as they contain data with improper spelling like \"atrashanaas vieta\" instead of \"atrašanās vieta\"\n\nPossible issues:\n* Data sets contain foreign language characters, like \"蠻子\" or cyrilic f.e. \"Рига\"" ]
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7d77f1bd2bcb3d87b5490ba1491d8d2c147f0fab
# Dataset Card for "psuh_no_inner_data_dir" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
polinaeterna/psuh_no_inner_data_dir
[ "region:us" ]
2023-03-17T13:09:57+00:00
{"dataset_info": [{"config_name": "default", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 160, "num_examples": 10}], "download_size": 1371, "dataset_size": 160}, {"config_name": "first", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 48, "num_examples": 3}], "download_size": 1301, "dataset_size": 48}, {"config_name": "second", "features": [{"name": "a", "dtype": "string"}, {"name": "b", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24, "num_examples": 2}], "download_size": 1083, "dataset_size": 24}], "builder_config": [{"config_name": "default", "data_dir": "./"}, {"config_name": "first", "data_dir": "first"}, {"config_name": "second", "data_dir": "second"}]}
2023-03-17T13:10:17+00:00
[]
[]
TAGS #region-us
# Dataset Card for "psuh_no_inner_data_dir" More Information needed
[ "# Dataset Card for \"psuh_no_inner_data_dir\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"psuh_no_inner_data_dir\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"psuh_no_inner_data_dir\"\n\nMore Information needed" ]
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b9e8a8d3bbe6774b9cd4cfc499a1ce4ca72ede11
# Dataset Card for "psuh_no_inner_data_dir2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
polinaeterna/psuh_no_inner_data_dir2
[ "region:us" ]
2023-03-17T13:10:57+00:00
{"dataset_info": [{"config_name": "default", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 160, "num_examples": 10}], "download_size": 1371, "dataset_size": 160}, {"config_name": "first", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 48, "num_examples": 3}], "download_size": 1301, "dataset_size": 48}, {"config_name": "second", "features": [{"name": "a", "dtype": "string"}, {"name": "b", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24, "num_examples": 2}], "download_size": 1083, "dataset_size": 24}], "builder_config": [{"config_name": "default", "data_dir": "data"}, {"config_name": "first", "data_dir": "first"}, {"config_name": "second", "data_dir": "second"}]}
2023-03-17T13:11:16+00:00
[]
[]
TAGS #region-us
# Dataset Card for "psuh_no_inner_data_dir2" More Information needed
[ "# Dataset Card for \"psuh_no_inner_data_dir2\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"psuh_no_inner_data_dir2\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"psuh_no_inner_data_dir2\"\n\nMore Information needed" ]
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fbc8b912d7343ba93bc68b00bc1100ece008209d
# Dataset Card for "hal-svd" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rntc/hal-svd
[ "region:us" ]
2023-03-17T13:35:25+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2471053405.603222, "num_examples": 1377663}, {"name": "validation", "num_bytes": 274562087.39677817, "num_examples": 153074}], "download_size": 1621625829, "dataset_size": 2745615493.0}}
2023-03-20T16:05:43+00:00
[]
[]
TAGS #region-us
# Dataset Card for "hal-svd" More Information needed
[ "# Dataset Card for \"hal-svd\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"hal-svd\"\n\nMore Information needed" ]
[ 6, 14 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"hal-svd\"\n\nMore Information needed" ]
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bfbe05a4241f0b7d474b9497d5b24585e023140f
# Dataset Card for "vira-intents-live" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vira-chatbot/vira-intents-live
[ "region:us" ]
2023-03-17T13:37:14+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 549293, "num_examples": 7632}, {"name": "validation", "num_bytes": 235916, "num_examples": 3272}], "download_size": 357988, "dataset_size": 785209}}
2023-03-21T13:51:52+00:00
[]
[]
TAGS #region-us
# Dataset Card for "vira-intents-live" More Information needed
[ "# Dataset Card for \"vira-intents-live\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"vira-intents-live\"\n\nMore Information needed" ]
[ 6, 16 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"vira-intents-live\"\n\nMore Information needed" ]
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350dbb1d39520e07ba4aa0b48b43b2f06ff65fbc
# Dataset Card for "bookcorpus_stage1_SV_20230316" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MartinKu/bookcorpus_stage1_SV_20230316
[ "region:us" ]
2023-03-17T13:55:16+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2091780208, "num_examples": 109310887}], "download_size": 1356114102, "dataset_size": 2091780208}}
2023-03-19T04:14:29+00:00
[]
[]
TAGS #region-us
# Dataset Card for "bookcorpus_stage1_SV_20230316" More Information needed
[ "# Dataset Card for \"bookcorpus_stage1_SV_20230316\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_stage1_SV_20230316\"\n\nMore Information needed" ]
[ 6, 24 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_stage1_SV_20230316\"\n\nMore Information needed" ]
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189d126225766b0246699395d6a676be18dffccb
# Dataset Card for "bookcorpus_stage1_OC_20230316" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MartinKu/bookcorpus_stage1_OC_20230316
[ "region:us" ]
2023-03-17T13:56:03+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3008149579, "num_examples": 100268570}], "download_size": 2035464392, "dataset_size": 3008149579}}
2023-03-19T04:16:33+00:00
[]
[]
TAGS #region-us
# Dataset Card for "bookcorpus_stage1_OC_20230316" More Information needed
[ "# Dataset Card for \"bookcorpus_stage1_OC_20230316\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_stage1_OC_20230316\"\n\nMore Information needed" ]
[ 6, 24 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_stage1_OC_20230316\"\n\nMore Information needed" ]
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e40ce28c2ca72f429720fa83a10e98e97fca0abe
# Dataset Card for "rl-prompt-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Dahoas/rl-prompt-dataset
[ "region:us" ]
2023-03-17T13:57:19+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 331075688.0, "num_examples": 201417}, {"name": "test", "num_bytes": 7649255, "num_examples": 5103}], "download_size": 206459232, "dataset_size": 338724943.0}}
2023-03-17T14:08:30+00:00
[]
[]
TAGS #region-us
# Dataset Card for "rl-prompt-dataset" More Information needed
[ "# Dataset Card for \"rl-prompt-dataset\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"rl-prompt-dataset\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"rl-prompt-dataset\"\n\nMore Information needed" ]
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3addfe5913729fa88549aba87e2f47d749982b55
# Dataset Card for CAMERA 📷 [![CI](https://github.com/shunk031/huggingface-datasets_CAMERA/actions/workflows/ci.yaml/badge.svg)](https://github.com/shunk031/huggingface-datasets_CAMERA/actions/workflows/ci.yaml) ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/CyberAgentAILab/camera - **Repository:** https://github.com/shunk031/huggingface-datasets_CAMERA ### Dataset Summary From [the official README.md](https://github.com/CyberAgentAILab/camera#camera-dataset): > CAMERA (CyberAgent Multimodal Evaluation for Ad Text GeneRAtion) is the Japanese ad text generation dataset. We hope that our dataset will be useful in research for realizing more advanced ad text generation models. ### Supported Tasks and Leaderboards [More Information Needed] #### Supported Tasks [More Information Needed] #### Leaderboard [More Information Needed] ### Languages The language data in CAMERA is in Japanese ([BCP-47 ja-JP](https://www.rfc-editor.org/info/bcp47)). ## Dataset Structure ### Data Instances When loading a specific configuration, users has to append a version dependent suffix: #### without-lp-images ```python from datasets import load_dataset dataset = load_dataset("shunk031/CAMERA", name="without-lp-images") print(dataset) # DatasetDict({ # train: Dataset({ # features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation'], # num_rows: 12395 # }) # validation: Dataset({ # features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation'], # num_rows: 3098 # }) # test: Dataset({ # features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation'], # num_rows: 872 # }) # }) ``` An example of the CAMERA (w/o LP images) dataset looks as follows: ```json { "asset_id": 13861, "kw": "仙台 ホテル", "lp_meta_description": "仙台のホテルや旅館をお探しなら楽天トラベルへ！楽天ポイントが使えて、貯まって、とってもお得な宿泊予約サイトです。さらに割引クーポンも使える！国内ツアー・航空券・レンタカー・バス予約も！", "title_org": "仙台市のホテル", "title_ne1": "", "title_ne2": "", "title_ne3": "", "domain": "", "parsed_full_text_annotation": { "text": [ "trivago", "Oops...AccessDenied 可", "Youarenotallowedtoviewthispage!Ifyouthinkthisisanerror,pleasecontacttrivago.", "Errorcode:0.3c99e86e.1672026945.25ba640YourIP:240d:1a:4d8:2800:b9b0:ea86:2087:d141AffectedURL:https://www.trivago.jp/ja/odr/%E8%BB%92", "%E4%BB%99%E5%8F%B0-%E5%9B%BD%E5%86%85?search=20072325", "Backtotrivago" ], "xmax": [ 653, 838, 765, 773, 815, 649 ], "xmin": [ 547, 357, 433, 420, 378, 550 ], "ymax": [ 47, 390, 475, 558, 598, 663 ], "ymin": [ 18, 198, 439, 504, 566, 651 ] } } ``` #### with-lp-images ```python from datasets import load_dataset dataset = load_dataset("shunk031/CAMERA", name="with-lp-images") print(dataset) # DatasetDict({ # train: Dataset({ # features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation', 'lp_image'], # num_rows: 12395 # }) # validation: Dataset({ # features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation', 'lp_image'], # num_rows: 3098 # }) # test: Dataset({ # features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation', 'lp_image'], # num_rows: 872 # }) # }) ``` An example of the CAMERA (w/ LP images) dataset looks as follows: ```json { "asset_id": 13861, "kw": "仙台 ホテル", "lp_meta_description": "仙台のホテルや旅館をお探しなら楽天トラベルへ！楽天ポイントが使えて、貯まって、とってもお得な宿泊予約サイトです。さらに割引クーポンも使える！国内ツアー・航空券・レンタカー・バス予約も！", "title_org": "仙台市のホテル", "title_ne1": "", "title_ne2": "", "title_ne3": "", "domain": "", "parsed_full_text_annotation": { "text": [ "trivago", "Oops...AccessDenied 可", "Youarenotallowedtoviewthispage!Ifyouthinkthisisanerror,pleasecontacttrivago.", "Errorcode:0.3c99e86e.1672026945.25ba640YourIP:240d:1a:4d8:2800:b9b0:ea86:2087:d141AffectedURL:https://www.trivago.jp/ja/odr/%E8%BB%92", "%E4%BB%99%E5%8F%B0-%E5%9B%BD%E5%86%85?search=20072325", "Backtotrivago" ], "xmax": [ 653, 838, 765, 773, 815, 649 ], "xmin": [ 547, 357, 433, 420, 378, 550 ], "ymax": [ 47, 390, 475, 558, 598, 663 ], "ymin": [ 18, 198, 439, 504, 566, 651 ] }, "lp_image": <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1200x680 at 0x7F8513446B20> } ``` ### Data Fields #### without-lp-images - `asset_id`: ids (associated with LP images) - `kw`: search keyword - `lp_meta_description`: meta description extracted from LP (i.e., LP Text) - `title_org`: ad text (original gold reference) - `title_ne{1-3}`: ad text (additonal gold references for multi-reference evaluation) - `domain`: industry domain (HR, EC, Fin, Edu) for industry-wise evaluation - `parsed_full_text_annotation`: OCR results for LP images #### with-lp-images - `asset_id`: ids (associated with LP images) - `kw`: search keyword - `lp_meta_description`: meta description extracted from LP (i.e., LP Text) - `title_org`: ad text (original gold reference) - `title_ne{1-3}`: ad text (additional gold references for multi-reference evaluation) - `domain`: industry domain (HR, EC, Fin, Edu) for industry-wise evaluation - `parsed_full_text_annotation`: OCR results for LP images - `lp_image`: Landing page (LP) image ### Data Splits From [the official paper](https://www.anlp.jp/proceedings/annual_meeting/2023/pdf_dir/H11-4.pdf): | Split | # of data | # of reference ad text | industry domain label | |-------|----------:|-----------------------:|:---------------------:| | Train | 12,395 | 1 | - | | Valid | 3,098 | 1 | - | | Test | 869 | 4 | ✔ | ## 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 [More Information Needed] ### Dataset Curators [More Information Needed] ### Licensing Information > This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. ### Citation Information ```bibtex @inproceedings{mita-et-al:nlp2023, author = "三田 雅人 and 村上 聡一朗 and 張 培楠", title = "広告文生成タスクの規定とベンチマーク構築", booktitle = "言語処理学会 第 29 回年次大会", year = 2023, } ``` ### Contributions Thanks to [Masato Mita](https://github.com/chemicaltree), [Soichiro Murakami](https://github.com/ichiroex), and [Peinan Zhang](https://github.com/peinan) for creating this dataset.
shunk031/CAMERA
[ "task_categories:text-generation", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:ja", "license:cc-by-nc-sa-4.0", "region:us" ]
2023-03-17T14:18:03+00:00
{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["ja"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": [], "source_datasets": ["original"], "task_categories": ["text-generation"], "task_ids": [], "pretty_name": "CAMERA", "tags": []}
2023-03-17T14:49:35+00:00
[]
[ "ja" ]
TAGS #task_categories-text-generation #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #source_datasets-original #language-Japanese #license-cc-by-nc-sa-4.0 #region-us
Dataset Card for CAMERA ======================= ![CI](URL Table of Contents ----------------- * Table of Contents * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: URL ### Dataset Summary From the official URL: > > CAMERA (CyberAgent Multimodal Evaluation for Ad Text GeneRAtion) is the Japanese ad text generation dataset. We hope that our dataset will be useful in research for realizing more advanced ad text generation models. > > > ### Supported Tasks and Leaderboards #### Supported Tasks #### Leaderboard ### Languages The language data in CAMERA is in Japanese (BCP-47 ja-JP). Dataset Structure ----------------- ### Data Instances When loading a specific configuration, users has to append a version dependent suffix: #### without-lp-images An example of the CAMERA (w/o LP images) dataset looks as follows: #### with-lp-images An example of the CAMERA (w/ LP images) dataset looks as follows: ### Data Fields #### without-lp-images * 'asset\_id': ids (associated with LP images) * 'kw': search keyword * 'lp\_meta\_description': meta description extracted from LP (i.e., LP Text) * 'title\_org': ad text (original gold reference) * 'title\_ne{1-3}': ad text (additonal gold references for multi-reference evaluation) * 'domain': industry domain (HR, EC, Fin, Edu) for industry-wise evaluation * 'parsed\_full\_text\_annotation': OCR results for LP images #### with-lp-images * 'asset\_id': ids (associated with LP images) * 'kw': search keyword * 'lp\_meta\_description': meta description extracted from LP (i.e., LP Text) * 'title\_org': ad text (original gold reference) * 'title\_ne{1-3}': ad text (additional gold references for multi-reference evaluation) * 'domain': industry domain (HR, EC, Fin, Edu) for industry-wise evaluation * 'parsed\_full\_text\_annotation': OCR results for LP images * 'lp\_image': Landing page (LP) image ### Data Splits From the official paper: Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information > > This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. > > > ### Contributions Thanks to Masato Mita, Soichiro Murakami, and Peinan Zhang for creating this dataset.
[ "### Dataset Summary\n\n\nFrom the official URL:\n\n\n\n> \n> CAMERA (CyberAgent Multimodal Evaluation for Ad Text GeneRAtion) is the Japanese ad text generation dataset. We hope that our dataset will be useful in research for realizing more advanced ad text generation models.\n> \n> \n>", "### Supported Tasks and Leaderboards", "#### Supported Tasks", "#### Leaderboard", "### Languages\n\n\nThe language data in CAMERA is in Japanese (BCP-47 ja-JP).\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nWhen loading a specific configuration, users has to append a version dependent suffix:", "#### without-lp-images\n\n\nAn example of the CAMERA (w/o LP images) dataset looks as follows:", "#### with-lp-images\n\n\nAn example of the CAMERA (w/ LP images) dataset looks as follows:", "### Data Fields", "#### without-lp-images\n\n\n* 'asset\\_id': ids (associated with LP images)\n* 'kw': search keyword\n* 'lp\\_meta\\_description': meta description extracted from LP (i.e., LP Text)\n* 'title\\_org': ad text (original gold reference)\n* 'title\\_ne{1-3}': ad text (additonal gold references for multi-reference evaluation)\n* 'domain': industry domain (HR, EC, Fin, Edu) for industry-wise evaluation\n* 'parsed\\_full\\_text\\_annotation': OCR results for LP images", "#### with-lp-images\n\n\n* 'asset\\_id': ids (associated with LP images)\n* 'kw': search keyword\n* 'lp\\_meta\\_description': meta description extracted from LP (i.e., LP Text)\n* 'title\\_org': ad text (original gold reference)\n* 'title\\_ne{1-3}': ad text (additional gold references for multi-reference evaluation)\n* 'domain': industry domain (HR, EC, Fin, Edu) for industry-wise evaluation\n* 'parsed\\_full\\_text\\_annotation': OCR results for LP images\n* 'lp\\_image': Landing page (LP) image", "### Data Splits\n\n\nFrom the official paper:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\n\n> \n> This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.\n> \n> \n>", "### Contributions\n\n\nThanks to Masato Mita, Soichiro Murakami, and Peinan Zhang for creating this dataset." ]
[ "TAGS\n#task_categories-text-generation #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #source_datasets-original #language-Japanese #license-cc-by-nc-sa-4.0 #region-us \n", "### Dataset Summary\n\n\nFrom the official URL:\n\n\n\n> \n> CAMERA (CyberAgent Multimodal Evaluation for Ad Text GeneRAtion) is the Japanese ad text generation dataset. We hope that our dataset will be useful in research for realizing more advanced ad text generation models.\n> \n> \n>", "### Supported Tasks and Leaderboards", "#### Supported Tasks", "#### Leaderboard", "### Languages\n\n\nThe language data in CAMERA is in Japanese (BCP-47 ja-JP).\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nWhen loading a specific configuration, users has to append a version dependent suffix:", "#### without-lp-images\n\n\nAn example of the CAMERA (w/o LP images) dataset looks as follows:", "#### with-lp-images\n\n\nAn example of the CAMERA (w/ LP images) dataset looks as follows:", "### Data Fields", "#### without-lp-images\n\n\n* 'asset\\_id': ids (associated with LP images)\n* 'kw': search keyword\n* 'lp\\_meta\\_description': meta description extracted from LP (i.e., LP Text)\n* 'title\\_org': ad text (original gold reference)\n* 'title\\_ne{1-3}': ad text (additonal gold references for multi-reference evaluation)\n* 'domain': industry domain (HR, EC, Fin, Edu) for industry-wise evaluation\n* 'parsed\\_full\\_text\\_annotation': OCR results for LP images", "#### with-lp-images\n\n\n* 'asset\\_id': ids (associated with LP images)\n* 'kw': search keyword\n* 'lp\\_meta\\_description': meta description extracted from LP (i.e., LP Text)\n* 'title\\_org': ad text (original gold reference)\n* 'title\\_ne{1-3}': ad text (additional gold references for multi-reference evaluation)\n* 'domain': industry domain (HR, EC, Fin, Edu) for industry-wise evaluation\n* 'parsed\\_full\\_text\\_annotation': OCR results for LP images\n* 'lp\\_image': Landing page (LP) image", "### Data Splits\n\n\nFrom the official paper:\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\n\n> \n> This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.\n> \n> \n>", "### Contributions\n\n\nThanks to Masato Mita, Soichiro Murakami, and Peinan Zhang for creating this dataset." ]
[ 73, 66, 10, 6, 4, 28, 24, 28, 27, 5, 145, 160, 16, 7, 4, 10, 10, 5, 5, 9, 18, 7, 8, 14, 6, 32, 29 ]
[ "passage: TAGS\n#task_categories-text-generation #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #source_datasets-original #language-Japanese #license-cc-by-nc-sa-4.0 #region-us \n### Dataset Summary\n\n\nFrom the official URL:\n\n\n\n> \n> CAMERA (CyberAgent Multimodal Evaluation for Ad Text GeneRAtion) is the Japanese ad text generation dataset. We hope that our dataset will be useful in research for realizing more advanced ad text generation models.\n> \n> \n>### Supported Tasks and Leaderboards#### Supported Tasks#### Leaderboard### Languages\n\n\nThe language data in CAMERA is in Japanese (BCP-47 ja-JP).\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nWhen loading a specific configuration, users has to append a version dependent suffix:#### without-lp-images\n\n\nAn example of the CAMERA (w/o LP images) dataset looks as follows:#### with-lp-images\n\n\nAn example of the CAMERA (w/ LP images) dataset looks as follows:### Data Fields#### without-lp-images\n\n\n* 'asset\\_id': ids (associated with LP images)\n* 'kw': search keyword\n* 'lp\\_meta\\_description': meta description extracted from LP (i.e., LP Text)\n* 'title\\_org': ad text (original gold reference)\n* 'title\\_ne{1-3}': ad text (additonal gold references for multi-reference evaluation)\n* 'domain': industry domain (HR, EC, Fin, Edu) for industry-wise evaluation\n* 'parsed\\_full\\_text\\_annotation': OCR results for LP images" ]
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cfd6325101dc968f80e0dee0d902af34329fec3b
# Dataset Card for "vira-dialog-acts-live" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vira-chatbot/vira-dialog-acts-live
[ "region:us" ]
2023-03-17T15:00:17+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 23507, "num_examples": 571}, {"name": "validation", "num_bytes": 3165, "num_examples": 71}, {"name": "test", "num_bytes": 2591, "num_examples": 72}], "download_size": 20086, "dataset_size": 29263}}
2023-03-17T15:00:25+00:00
[]
[]
TAGS #region-us
# Dataset Card for "vira-dialog-acts-live" More Information needed
[ "# Dataset Card for \"vira-dialog-acts-live\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"vira-dialog-acts-live\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"vira-dialog-acts-live\"\n\nMore Information needed" ]
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56f9a0d373604eff727aec909ed89bda80ab158b
# Dataset Card for "DENTAL_CLICK" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
JBJoyce/DENTAL_CLICK
[ "region:us" ]
2023-03-17T16:25:00+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 126813363.116, "num_examples": 3956}, {"name": "test", "num_bytes": 32697248.72, "num_examples": 1020}], "download_size": 149989127, "dataset_size": 159510611.836}}
2023-03-17T16:32:57+00:00
[]
[]
TAGS #region-us
# Dataset Card for "DENTAL_CLICK" More Information needed
[ "# Dataset Card for \"DENTAL_CLICK\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"DENTAL_CLICK\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"DENTAL_CLICK\"\n\nMore Information needed" ]
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d770cd2a23e90cc9098c3bc63f0db8e68bdac387
# Dataset Card for "UA_speech_normalized_2c2p" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AravindVadlapudi02/UA_Speech_noisereduced_2c2p
[ "region:us" ]
2023-03-17T16:58:42+00:00
{"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "healthy control", "1": "pathology"}}}}, {"name": "input_features", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 768265600, "num_examples": 800}, {"name": "test", "num_bytes": 4599029948, "num_examples": 4789}], "download_size": 620157452, "dataset_size": 5367295548}}
2023-03-17T17:00:20+00:00
[]
[]
TAGS #region-us
# Dataset Card for "UA_speech_normalized_2c2p" More Information needed
[ "# Dataset Card for \"UA_speech_normalized_2c2p\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"UA_speech_normalized_2c2p\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"UA_speech_normalized_2c2p\"\n\nMore Information needed" ]
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bc393981d3a007a756e8a290fbab9d5aed7d68e7
# Dataset Card for "arch_gastric" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Reasat/arch_gastric
[ "region:us" ]
2023-03-17T16:59:34+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "index", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16504060705.626, "num_examples": 270354}], "download_size": 13500347937, "dataset_size": 16504060705.626}}
2023-03-17T17:11:04+00:00
[]
[]
TAGS #region-us
# Dataset Card for "arch_gastric" More Information needed
[ "# Dataset Card for \"arch_gastric\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"arch_gastric\"\n\nMore Information needed" ]
[ 6, 14 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"arch_gastric\"\n\nMore Information needed" ]
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a59144ee3f0320ee27bbf53a2e2b5af912ae6675
# Dataset Card for "flores200_8_baseline_mt5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hlillemark/flores200_8_baseline_mt5
[ "region:us" ]
2023-03-17T18:04:41+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 14085207567, "num_examples": 20480000}, {"name": "val", "num_bytes": 3532704, "num_examples": 5000}, {"name": "test", "num_bytes": 7062030, "num_examples": 10000}], "download_size": 5157483346, "dataset_size": 14095802301}}
2023-03-18T19:27:32+00:00
[]
[]
TAGS #region-us
# Dataset Card for "flores200_8_baseline_mt5" More Information needed
[ "# Dataset Card for \"flores200_8_baseline_mt5\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"flores200_8_baseline_mt5\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"flores200_8_baseline_mt5\"\n\nMore Information needed" ]
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ec2cb49ccaf7f4076abddc47b063a8b7fa2472b3
# Model-Written Evaluation Datasets This repository includes datasets written by language models, used in the paper "Discovering Language Model Behaviors with Model-Written Evaluations." The evaluations in this dataset were designed for dialogue agents, such as models fine-tuned to respond to user utterances or pretrained language models prompted to simulate a dialogue agent's behavior. However, the data can be adapted to test various other types of models as well. The dataset consis of each of the following: 1. persona: Datasets designed to evaluate models on different aspects of their behavior, such as their political and religious views, personality traits, moral beliefs, and willingness to pursue potentially risky objectives (e.g., self-preservation or power-seeking). 2. sycophancy: Datasets created to assess models based on their tendency to echo a user's perspective when presented with various questions in fields like philosophy, NLP research, and politics. 3. winogenerated: An extended version of the Winogender Dataset (Rudinger et al., 2018) generated by models. The dataset includes occupation titles generated specifically for this dataset, alongside occupation gender statistics from the Bureau of Labor Statistics. 4. advanced-ai-risk: Datasets evaluating models on behaviors associated with potential catastrophic risks posed by advanced AI systems. These datasets were generated in a few-shot manner. Please see the cited paper for additional details on the datasets. **Disclaimer**: As discussed in the paper, some data contains content that includes social biases and stereotypes. The data may also contain other forms of harmful or offensive content. The views expressed in the data do not reflect the views of Anthropic or any of its employees. ## Bibtex Citation If you would like to cite this work or data, you may use the following bibtex citation: ``` @misc{perez2022discovering, doi = {10.48550/ARXIV.2212.09251}, url = {https://arxiv.org/abs/2212.09251}, author = {Perez, Ethan and Ringer, Sam and Lukošiūtė, Kamilė and Nguyen, Karina and Chen, Edwin and Heiner, Scott and Pettit, Craig and Olsson, Catherine and Kundu, Sandipan and Kadavath, Saurav and Jones, Andy and Chen, Anna and Mann, Ben and Israel, Brian and Seethor, Bryan and McKinnon, Cameron and Olah, Christopher and Yan, Da and Amodei, Daniela and Amodei, Dario and Drain, Dawn and Li, Dustin and Tran-Johnson, Eli and Khundadze, Guro and Kernion, Jackson and Landis, James and Kerr, Jamie and Mueller, Jared and Hyun, Jeeyoon and Landau, Joshua and Ndousse, Kamal and Goldberg, Landon and Lovitt, Liane and Lucas, Martin and Sellitto, Michael and Zhang, Miranda and Kingsland, Neerav and Elhage, Nelson and Joseph, Nicholas and Mercado, Noemí and DasSarma, Nova and Rausch, Oliver and Larson, Robin and McCandlish, Sam and Johnston, Scott and Kravec, Shauna and {El Showk}, Sheer and Lanham, Tamera and Telleen-Lawton, Timothy and Brown, Tom and Henighan, Tom and Hume, Tristan and Bai, Yuntao and Hatfield-Dodds, Zac and Clark, Jack and Bowman, Samuel R. and Askell, Amanda and Grosse, Roger and Hernandez, Danny and Ganguli, Deep and Hubinger, Evan and Schiefer, Nicholas and Kaplan, Jared}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Discovering Language Model Behaviors with Model-Written Evaluations}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ```
khalidalt/model-written-evals
[ "task_categories:multiple-choice", "task_categories:zero-shot-classification", "task_categories:question-answering", "task_ids:multiple-choice-qa", "task_ids:multiple-choice-coreference-resolution", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "gender bias", "social bias", "AI safety", "personality", "politics", "arxiv:2212.09251", "region:us" ]
2023-03-17T18:42:09+00:00
{"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["multiple-choice", "zero-shot-classification", "question-answering"], "task_ids": ["multiple-choice-qa", "multiple-choice-coreference-resolution"], "pretty_name": "Evaluations from \"Discovering Language Model Behaviors with Model-Written Evaluations\"", "tags": ["gender bias", "social bias", "AI safety", "personality", "politics"]}
2023-07-02T19:24:29+00:00
[ "2212.09251" ]
[ "en" ]
TAGS #task_categories-multiple-choice #task_categories-zero-shot-classification #task_categories-question-answering #task_ids-multiple-choice-qa #task_ids-multiple-choice-coreference-resolution #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-4.0 #gender bias #social bias #AI safety #personality #politics #arxiv-2212.09251 #region-us
# Model-Written Evaluation Datasets This repository includes datasets written by language models, used in the paper "Discovering Language Model Behaviors with Model-Written Evaluations." The evaluations in this dataset were designed for dialogue agents, such as models fine-tuned to respond to user utterances or pretrained language models prompted to simulate a dialogue agent's behavior. However, the data can be adapted to test various other types of models as well. The dataset consis of each of the following: 1. persona: Datasets designed to evaluate models on different aspects of their behavior, such as their political and religious views, personality traits, moral beliefs, and willingness to pursue potentially risky objectives (e.g., self-preservation or power-seeking). 2. sycophancy: Datasets created to assess models based on their tendency to echo a user's perspective when presented with various questions in fields like philosophy, NLP research, and politics. 3. winogenerated: An extended version of the Winogender Dataset (Rudinger et al., 2018) generated by models. The dataset includes occupation titles generated specifically for this dataset, alongside occupation gender statistics from the Bureau of Labor Statistics. 4. advanced-ai-risk: Datasets evaluating models on behaviors associated with potential catastrophic risks posed by advanced AI systems. These datasets were generated in a few-shot manner. Please see the cited paper for additional details on the datasets. Disclaimer: As discussed in the paper, some data contains content that includes social biases and stereotypes. The data may also contain other forms of harmful or offensive content. The views expressed in the data do not reflect the views of Anthropic or any of its employees. ## Bibtex Citation If you would like to cite this work or data, you may use the following bibtex citation:
[ "# Model-Written Evaluation Datasets\n\nThis repository includes datasets written by language models, used in the paper \"Discovering Language Model Behaviors with Model-Written Evaluations.\"\n\nThe evaluations in this dataset were designed for dialogue agents, such as models fine-tuned to respond to user utterances or pretrained language models prompted to simulate a dialogue agent's behavior. However, the data can be adapted to test various other types of models as well.\n\nThe dataset consis of each of the following:\n1. persona: Datasets designed to evaluate models on different aspects of their behavior, such as their political and religious views, personality traits, moral beliefs, and willingness to pursue potentially risky objectives (e.g., self-preservation or power-seeking).\n\n2. sycophancy: Datasets created to assess models based on their tendency to echo a user's perspective when presented with various questions in fields like philosophy, NLP research, and politics.\n\n3. winogenerated: An extended version of the Winogender Dataset (Rudinger et al., 2018) generated by models. The dataset includes occupation titles generated specifically for this dataset, alongside occupation gender statistics from the Bureau of Labor Statistics.\n\n4. advanced-ai-risk: Datasets evaluating models on behaviors associated with potential catastrophic risks posed by advanced AI systems. These datasets were generated in a few-shot manner.\n\n\n\nPlease see the cited paper for additional details on the datasets.\n\nDisclaimer: As discussed in the paper, some data contains content that includes social biases and stereotypes. The data may also contain other forms of harmful or offensive content. The views expressed in the data do not reflect the views of Anthropic or any of its employees.", "## Bibtex Citation\n\nIf you would like to cite this work or data, you may use the following bibtex citation:" ]
[ "TAGS\n#task_categories-multiple-choice #task_categories-zero-shot-classification #task_categories-question-answering #task_ids-multiple-choice-qa #task_ids-multiple-choice-coreference-resolution #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-4.0 #gender bias #social bias #AI safety #personality #politics #arxiv-2212.09251 #region-us \n", "# Model-Written Evaluation Datasets\n\nThis repository includes datasets written by language models, used in the paper \"Discovering Language Model Behaviors with Model-Written Evaluations.\"\n\nThe evaluations in this dataset were designed for dialogue agents, such as models fine-tuned to respond to user utterances or pretrained language models prompted to simulate a dialogue agent's behavior. However, the data can be adapted to test various other types of models as well.\n\nThe dataset consis of each of the following:\n1. persona: Datasets designed to evaluate models on different aspects of their behavior, such as their political and religious views, personality traits, moral beliefs, and willingness to pursue potentially risky objectives (e.g., self-preservation or power-seeking).\n\n2. sycophancy: Datasets created to assess models based on their tendency to echo a user's perspective when presented with various questions in fields like philosophy, NLP research, and politics.\n\n3. winogenerated: An extended version of the Winogender Dataset (Rudinger et al., 2018) generated by models. The dataset includes occupation titles generated specifically for this dataset, alongside occupation gender statistics from the Bureau of Labor Statistics.\n\n4. advanced-ai-risk: Datasets evaluating models on behaviors associated with potential catastrophic risks posed by advanced AI systems. These datasets were generated in a few-shot manner.\n\n\n\nPlease see the cited paper for additional details on the datasets.\n\nDisclaimer: As discussed in the paper, some data contains content that includes social biases and stereotypes. The data may also contain other forms of harmful or offensive content. The views expressed in the data do not reflect the views of Anthropic or any of its employees.", "## Bibtex Citation\n\nIf you would like to cite this work or data, you may use the following bibtex citation:" ]
[ 166, 416, 28 ]
[ "passage: TAGS\n#task_categories-multiple-choice #task_categories-zero-shot-classification #task_categories-question-answering #task_ids-multiple-choice-qa #task_ids-multiple-choice-coreference-resolution #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-4.0 #gender bias #social bias #AI safety #personality #politics #arxiv-2212.09251 #region-us \n" ]
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5e8626d424ea46e4abc12547aa964e30c7ad63f9
En 2012 Levesque, Davis y Morgenstern propusieron un sistema de evaluación basado en el trabajo de Terry Winograd (1972) y Eugene Charniak (1974) sobre comprensión de lenguaje natural. Levesque et al. (2012) proponen el Desafío del Esquema de Winograd (Winograd Schema Challenge) el cuál consiste de un par de oraciones que difieren en una o dos palabras y que contienen una ambigüedad referencial. El conjunto de datos es hoy conocido como WSC. Las características que se deben considerar para la construcción son: 1. Las oraciones no deben poder resolverse con información estadística (co-ocurrencia de palabras) o gramatical (Google-proof). 2. Dos entidades se mencionan en una oración por frases nominales. Pueden ser dos masculinos, dos femeninos, dos objetos inanimados o dos grupos de personas u objetos (en el ejemplo anterior, the trophy y the suitcase). 3. Se utiliza un pronombre para referirse a uno de ellos ("él", "ella", "eso" o "ellos", según las partes). En el ejemplo anterior el pronombre es it’s. 4. La pregunta es siempre la misma: ¿cuál es el referente del pronombre? La pregunta anterior es: What is too small? o What is too large? 5. En cada oración, hay dos palabras especiales para el esquema. Hay un espacio en el esquema que puede llenarse con cualquiera de las dos palabras. La respuesta correcta depende de qué palabra especial se elija. En lo anterior, la palabra especial utilizada es small, y la otra palabra especial es large. --- license: mit ---
mu1990/Winospan
[ "region:us" ]
2023-03-17T19:36:29+00:00
{}
2023-03-24T23:38:27+00:00
[]
[]
TAGS #region-us
En 2012 Levesque, Davis y Morgenstern propusieron un sistema de evaluación basado en el trabajo de Terry Winograd (1972) y Eugene Charniak (1974) sobre comprensión de lenguaje natural. Levesque et al. (2012) proponen el Desafío del Esquema de Winograd (Winograd Schema Challenge) el cuál consiste de un par de oraciones que difieren en una o dos palabras y que contienen una ambigüedad referencial. El conjunto de datos es hoy conocido como WSC. Las características que se deben considerar para la construcción son: 1. Las oraciones no deben poder resolverse con información estadística (co-ocurrencia de palabras) o gramatical (Google-proof). 2. Dos entidades se mencionan en una oración por frases nominales. Pueden ser dos masculinos, dos femeninos, dos objetos inanimados o dos grupos de personas u objetos (en el ejemplo anterior, the trophy y the suitcase). 3. Se utiliza un pronombre para referirse a uno de ellos ("él", "ella", "eso" o "ellos", según las partes). En el ejemplo anterior el pronombre es it’s. 4. La pregunta es siempre la misma: ¿cuál es el referente del pronombre? La pregunta anterior es: What is too small? o What is too large? 5. En cada oración, hay dos palabras especiales para el esquema. Hay un espacio en el esquema que puede llenarse con cualquiera de las dos palabras. La respuesta correcta depende de qué palabra especial se elija. En lo anterior, la palabra especial utilizada es small, y la otra palabra especial es large. --- license: mit ---
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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5ffcd4d5b0bbcb7884161d88bfd8462ad73aefe5
# Dataset Card for "UA_speech_noisereduced_1c1p" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AravindVadlapudi02/UA_speech_noisereduced_1c1p
[ "region:us" ]
2023-03-17T20:29:04+00:00
{"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "healthy control", "1": "pathology"}}}}, {"name": "input_features", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 384132800, "num_examples": 400}, {"name": "test", "num_bytes": 4983162748, "num_examples": 5189}], "download_size": 620399490, "dataset_size": 5367295548}}
2023-03-17T20:31:40+00:00
[]
[]
TAGS #region-us
# Dataset Card for "UA_speech_noisereduced_1c1p" More Information needed
[ "# Dataset Card for \"UA_speech_noisereduced_1c1p\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"UA_speech_noisereduced_1c1p\"\n\nMore Information needed" ]
[ 6, 23 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"UA_speech_noisereduced_1c1p\"\n\nMore Information needed" ]
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54b3cc446d5138b72ca1b3539bc64fd1848cff70
# Dataset Card for "VizWiz_validation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/VizWiz_validation
[ "region:us" ]
2023-03-17T20:48:48+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "filename", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}]}, {"name": "answer_type", "dtype": "string"}, {"name": "answerable", "dtype": "int32"}, {"name": "id_image", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B", "sequence": "string"}, {"name": "blip_caption_beam_5", "dtype": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 2126280229.0, "num_examples": 4319}], "download_size": 1684405444, "dataset_size": 2126280229.0}}
2023-03-17T20:52:19+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VizWiz_validation" More Information needed
[ "# Dataset Card for \"VizWiz_validation\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VizWiz_validation\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VizWiz_validation\"\n\nMore Information needed" ]
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90de28401a0e884afe9de80586eec47d1c0cd065
# Dataset Card for "VQAv2_minival" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/VQAv2_minival
[ "region:us" ]
2023-03-17T21:01:08+00:00
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "id", "dtype": "int64"}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module_without_filtering", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "clip_tags_LAION_ViT_H_14_2B", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B", "sequence": "string"}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_with_openai", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 1766679196, "num_examples": 25994}], "download_size": 340842185, "dataset_size": 1766679196}}
2023-05-16T11:04:28+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_minival" More Information needed
[ "# Dataset Card for \"VQAv2_minival\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_minival\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_minival\"\n\nMore Information needed" ]
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e0081b640875e5245dc4b9a9549c003c8fd95c7c
# Dataset Card for "VQAv2_validation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/VQAv2_validation
[ "region:us" ]
2023-03-17T21:03:19+00:00
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 44871053126.0, "num_examples": 214354}], "download_size": 9350701013, "dataset_size": 44871053126.0}}
2023-04-26T00:00:37+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_validation" More Information needed
[ "# Dataset Card for \"VQAv2_validation\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_validation\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_validation\"\n\nMore Information needed" ]
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0398414ffdf6ca445b9e90204ce5d0def5d73da2
# Dataset Card for "VizWiz_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/VizWiz_train
[ "region:us" ]
2023-03-17T21:14:38+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "filename", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}]}, {"name": "answer_type", "dtype": "string"}, {"name": "answerable", "dtype": "int32"}, {"name": "id_image", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B", "sequence": "string"}, {"name": "blip_caption_beam_5", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 9906518637.0, "num_examples": 20523}], "download_size": 9880125036, "dataset_size": 9906518637.0}}
2023-03-17T21:22:05+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VizWiz_train" More Information needed
[ "# Dataset Card for \"VizWiz_train\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VizWiz_train\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VizWiz_train\"\n\nMore Information needed" ]
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bfa149029bb1e2975cb0b9bea8ad948db9e9ddb2
# Dataset Card for "COCO_captions_validation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/COCO_captions_validation
[ "region:us" ]
2023-03-17T21:22:06+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "filepath", "dtype": "string"}, {"name": "sentids", "list": "int32"}, {"name": "filename", "dtype": "string"}, {"name": "imgid", "dtype": "int32"}, {"name": "split", "dtype": "string"}, {"name": "sentences_tokens", "list": {"list": "string"}}, {"name": "sentences_raw", "list": "string"}, {"name": "sentences_sentid", "list": "int32"}, {"name": "cocoid", "dtype": "int32"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B", "sequence": "string"}, {"name": "blip_caption_beam_5", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 1047402581.0, "num_examples": 5000}], "download_size": 915680119, "dataset_size": 1047402581.0}}
2023-03-17T21:22:46+00:00
[]
[]
TAGS #region-us
# Dataset Card for "COCO_captions_validation" More Information needed
[ "# Dataset Card for \"COCO_captions_validation\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"COCO_captions_validation\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"COCO_captions_validation\"\n\nMore Information needed" ]
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decdb6bafbe1b2a09f643cc344b9564eec11125a
# Dataset Card for "COCO_captions_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/COCO_captions_test
[ "region:us" ]
2023-03-17T21:22:46+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "filepath", "dtype": "string"}, {"name": "sentids", "list": "int32"}, {"name": "filename", "dtype": "string"}, {"name": "imgid", "dtype": "int32"}, {"name": "split", "dtype": "string"}, {"name": "sentences_tokens", "list": {"list": "string"}}, {"name": "sentences_raw", "list": "string"}, {"name": "sentences_sentid", "list": "int32"}, {"name": "cocoid", "dtype": "int32"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B", "sequence": "string"}, {"name": "blip_caption_beam_5", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 831189492.0, "num_examples": 5000}], "download_size": 823516792, "dataset_size": 831189492.0}}
2023-03-17T21:23:22+00:00
[]
[]
TAGS #region-us
# Dataset Card for "COCO_captions_test" More Information needed
[ "# Dataset Card for \"COCO_captions_test\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"COCO_captions_test\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"COCO_captions_test\"\n\nMore Information needed" ]
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f2d740b23743d7fa951ba80579cffacb1819d1fe
# Dataset Card for "apps_partial_sorted_0_300" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
minimario/apps_partial_sorted_0_300
[ "region:us" ]
2023-03-17T21:35:52+00:00
{"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "full_sample", "dtype": "string"}, {"name": "where_from", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 159597742, "num_examples": 127289}], "download_size": 5498713, "dataset_size": 159597742}}
2023-03-18T18:13:34+00:00
[]
[]
TAGS #region-us
# Dataset Card for "apps_partial_sorted_0_300" More Information needed
[ "# Dataset Card for \"apps_partial_sorted_0_300\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"apps_partial_sorted_0_300\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"apps_partial_sorted_0_300\"\n\nMore Information needed" ]
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3509698f6c95bc29895ed9f6805beab187aec09c
# Dataset Card for "apps_partial_sorted_300_350" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
minimario/apps_partial_sorted_300_350
[ "region:us" ]
2023-03-17T21:36:07+00:00
{"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "full_sample", "dtype": "string"}, {"name": "where_from", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25695567, "num_examples": 20543}], "download_size": 923285, "dataset_size": 25695567}}
2023-03-18T18:13:38+00:00
[]
[]
TAGS #region-us
# Dataset Card for "apps_partial_sorted_300_350" More Information needed
[ "# Dataset Card for \"apps_partial_sorted_300_350\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"apps_partial_sorted_300_350\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"apps_partial_sorted_300_350\"\n\nMore Information needed" ]
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8f34d65d796513839ab61e17a9dc14b01dd55697
# Dataset Card for "apps_partial_full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
minimario/apps_partial_full
[ "region:us" ]
2023-03-17T21:39:28+00:00
{"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "full_sample", "dtype": "string"}, {"name": "where_from", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1202649204, "num_examples": 908222}], "download_size": 40307483, "dataset_size": 1202649204}}
2023-03-18T18:13:30+00:00
[]
[]
TAGS #region-us
# Dataset Card for "apps_partial_full" More Information needed
[ "# Dataset Card for \"apps_partial_full\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"apps_partial_full\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"apps_partial_full\"\n\nMore Information needed" ]
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c70dceb7af7f46432d217af498cda44bf28431bf
# Dataset Card for "VQAv2_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/VQAv2_test
[ "region:us" ]
2023-03-17T21:59:25+00:00
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_with_openai", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "answers", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 92151870512.0, "num_examples": 447793}], "download_size": 18737258554, "dataset_size": 92151870512.0}}
2023-05-13T20:54:43+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_test" More Information needed
[ "# Dataset Card for \"VQAv2_test\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_test\"\n\nMore Information needed" ]
[ 6, 16 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_test\"\n\nMore Information needed" ]
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6de3ff2103743051521ac6cc001025a9b399a1f1
# Dataset Card for "VizWiz_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/VizWiz_test
[ "region:us" ]
2023-03-17T22:21:10+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "filename", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}]}, {"name": "answer_type", "dtype": "string"}, {"name": "answerable", "dtype": "int32"}, {"name": "id_image", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B", "sequence": "string"}, {"name": "blip_caption_beam_5", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 3995437282.0, "num_examples": 8000}], "download_size": 3977376350, "dataset_size": 3995437282.0}}
2023-03-17T22:23:31+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VizWiz_test" More Information needed
[ "# Dataset Card for \"VizWiz_test\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VizWiz_test\"\n\nMore Information needed" ]
[ 6, 16 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VizWiz_test\"\n\nMore Information needed" ]
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99a2a14c7400e0efb8cc6e215776b58f3e958481
**NOTE:** This is a machine translated version of the [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) dataset. # Dataset Card for Alpaca-Cleaned - **Repository:** https://github.com/gururise/AlpacaDataCleaned ## Dataset Description This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset: 1. **Hallucinations:** Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer. ``` "instruction":"Summarize the given article in 200 Words.", "input": "https://www.bbc.com/news/world-51461830", "output": "The recent protests in Belarus are part of a growing wave of discontent against the rule of Alexander Lukashenko, who has led the country since 1994. Demonstrations against his government have been ongoing since August, and come in the wake of an election widely seen as rigged. As police and security forces have cracked down on protesters, the movement has only grown, with hundreds of thousands of people taking to the streets to call for new leadership. Human rights activists have accused the government of using extreme violence in order prosecute protesters, leading to an international outcry and sanctions from the European Union. As the situation in Belarus continues to unfold, the international community is monitoring the situation closely - hoping for a peaceful resolution, while also standing firm against the oppressive tactics employed by the Belarusian state." "instruction": "Classify the following shape according to simple geometry.", "input": "<Picture Attached>", "output": "Triangle" "instruction": "Given a recent news article, write a headline for it.", "input": "https://www.news.example.com/article-1", "output": "\"NASA Launches a Mars 2020 Mission to Search for Signs of Ancient Life\"" ``` 2. **Merged Instructions:** There were many instructions that were merged together in the original dataset for some reason. ``` "instruction": "Should I invest in stocks?", "input": "", "output": "It depends on your individual situation. Investing in stocks carries a certain amount of risk but can also offer potential rewards. Make sure to research the different types of stocks, understand the risks and rewards, and develop a plan that works for you.\n##\n18. Instruction: Identify the noun in the following sentence:\n18. Input:\nThe large apple was delicious.\n18. Output:\nApple" ``` 3. **Empty outputs:** Some entries in the original dataset had empty outputs. ``` "instruction": "Calculate the area of the following shape in square centimeters.", "input": "rectangle of size 4 cm x 5 cm", "output": "" ``` 4. **Empty code examples:** Some descriptions in the original dataset were missing code examples, making it difficult to understand the intended behavior of the code. 5. **Instructions to generate images:** Some descriptions in the original dataset included instructions to generate images, something obviously not possible. ``` "instruction": "Create a graphic or logo that visually represents the word \"courage\".", "input": "", "output": "<No Output>" ``` 6. **N/A outputs:** Some code snippets in the original dataset had N/A outputs. 7. **Inconsistent input field:** The original dataset had inconsistent usage of the input field when it was supposed to be empty. ``` "input":"<no input>" "input":"No input" "input":"noinput" "input":"<noinput>" ``` 8. **Wrong answers:** Some instructions/questions in the original dataset had incorrect answers. About 80% of the math problems are estimated to have incorrect answers. ``` "instruction": "Calculate the median of the following data set.", "input": "1, 2, 4, 5, 8, 9", "output": "5" "instruction": "Convert 25m to km.", "input": "", "output": "25km" ``` 9. **Non-Sensical/Unclear instructions:** Many instructions are unclear, we try to clarify (or re-write) if instructions are non-sensical. Instructions that are slightly unclear, but where one could deduce the meaning are not altered. ``` "instruction": "Freeze the following sample of yogurt for 10 minutes.", "input": "Yogurt sample", "output": "<noinput>" "instruction": "Increase the font size to 12 points.", "input": "", "output": "The font size has been increased to 12 points." ``` 10. **Extraneous escape and control characters:** The original dataset had several entries with extraneous escape and control characters. ### Original Alpaca Dataset Summary Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications: - The `text-davinci-003` engine to generate the instruction data instead of `davinci`. - A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`. - Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation. - The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions. - Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct. This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500). In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by [Self-Instruct](https://github.com/yizhongw/self-instruct/blob/main/data/seed_tasks.jsonl). ### Supported Tasks and Leaderboards The Alpaca dataset designed for instruction training pretrained language models. ### Languages The data in Alpaca are in English (BCP-47 en). ## Dataset Structure ### Data Instances An example of "train" looks as follows: ```json { "instruction": "Create a classification task by clustering the given list of items.", "input": "Apples, oranges, bananas, strawberries, pineapples", "output": "Class 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples", "text": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nCreate a classification task by clustering the given list of items.\n\n### Input:\nApples, oranges, bananas, strawberries, pineapples\n\n### Response:\nClass 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples", } ``` ### Data Fields The data fields are as follows: * `instruction`: describes the task the model should perform. Each of the 52K instructions is unique. * `input`: optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input. * `output`: the answer to the instruction as generated by `text-davinci-003`. * `text`: the `instruction`, `input` and `output` formatted with the [prompt template](https://github.com/tatsu-lab/stanford_alpaca#data-release) used by the authors for fine-tuning their models. ### Data Splits | | train | |---------------|------:| | alpaca | 52002 | ## 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 Excerpt the [blog post](https://crfm.stanford.edu/2023/03/13/alpaca.html) accompanying the release of this dataset: > We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models. ### Discussion of Biases [More Information Needed] ### Other Known Limitations The `alpaca` data is generated by a language model (`text-davinci-003`) and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset is available under the [Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). ### Citation Information ``` @misc{alpaca, author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, title = {Stanford Alpaca: An Instruction-following LLaMA model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, } ``` ### Contributions [More Information Needed]
dominguesm/alpaca-data-pt-br
[ "task_categories:text-generation", "language:pt", "license:cc-by-nc-4.0", "instruction-finetuning", "region:us" ]
2023-03-17T22:48:45+00:00
{"language": ["pt"], "license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "pretty_name": "Alpaca-Cleaned-PTBR", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18324606, "num_examples": 51759}], "download_size": 11508709, "dataset_size": 18324606}, "tags": ["instruction-finetuning"]}
2023-11-17T08:51:52+00:00
[]
[ "pt" ]
TAGS #task_categories-text-generation #language-Portuguese #license-cc-by-nc-4.0 #instruction-finetuning #region-us
NOTE: This is a machine translated version of the yahma/alpaca-cleaned dataset. Dataset Card for Alpaca-Cleaned =============================== * Repository: URL Dataset Description ------------------- This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset: 1. Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer. 2. Merged Instructions: There were many instructions that were merged together in the original dataset for some reason. 3. Empty outputs: Some entries in the original dataset had empty outputs. 4. Empty code examples: Some descriptions in the original dataset were missing code examples, making it difficult to understand the intended behavior of the code. 5. Instructions to generate images: Some descriptions in the original dataset included instructions to generate images, something obviously not possible. 6. N/A outputs: Some code snippets in the original dataset had N/A outputs. 7. Inconsistent input field: The original dataset had inconsistent usage of the input field when it was supposed to be empty. 8. Wrong answers: Some instructions/questions in the original dataset had incorrect answers. About 80% of the math problems are estimated to have incorrect answers. 9. Non-Sensical/Unclear instructions: Many instructions are unclear, we try to clarify (or re-write) if instructions are non-sensical. Instructions that are slightly unclear, but where one could deduce the meaning are not altered. 10. Extraneous escape and control characters: The original dataset had several entries with extraneous escape and control characters. ### Original Alpaca Dataset Summary Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications: * The 'text-davinci-003' engine to generate the instruction data instead of 'davinci'. * A new prompt was written that explicitly gave the requirement of instruction generation to 'text-davinci-003'. * Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation. * The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions. * Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct. This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500). In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by Self-Instruct. ### Supported Tasks and Leaderboards The Alpaca dataset designed for instruction training pretrained language models. ### Languages The data in Alpaca are in English (BCP-47 en). Dataset Structure ----------------- ### Data Instances An example of "train" looks as follows: ### Data Fields The data fields are as follows: * 'instruction': describes the task the model should perform. Each of the 52K instructions is unique. * 'input': optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input. * 'output': the answer to the instruction as generated by 'text-davinci-003'. * 'text': the 'instruction', 'input' and 'output' formatted with the prompt template used by the authors for fine-tuning their models. ### Data Splits Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset Excerpt the blog post accompanying the release of this dataset: > > We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models. > > > ### Discussion of Biases ### Other Known Limitations The 'alpaca' data is generated by a language model ('text-davinci-003') and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections. Additional Information ---------------------- ### Dataset Curators ### Licensing Information The dataset is available under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). ### Contributions
[ "### Original Alpaca Dataset Summary\n\n\nAlpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.\n\n\nThe authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:\n\n\n* The 'text-davinci-003' engine to generate the instruction data instead of 'davinci'.\n* A new prompt was written that explicitly gave the requirement of instruction generation to 'text-davinci-003'.\n* Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.\n* The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.\n* Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.\n\n\nThis produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).\nIn a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by Self-Instruct.", "### Supported Tasks and Leaderboards\n\n\nThe Alpaca dataset designed for instruction training pretrained language models.", "### Languages\n\n\nThe data in Alpaca are in English (BCP-47 en).\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of \"train\" looks as follows:", "### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'instruction': describes the task the model should perform. Each of the 52K instructions is unique.\n* 'input': optional context or input for the task. For example, when the instruction is \"Summarize the following article\", the input is the article. Around 40% of the examples have an input.\n* 'output': the answer to the instruction as generated by 'text-davinci-003'.\n* 'text': the 'instruction', 'input' and 'output' formatted with the prompt template used by the authors for fine-tuning their models.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nExcerpt the blog post accompanying the release of this dataset:\n\n\n\n> \n> We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models.\n> \n> \n>", "### Discussion of Biases", "### Other Known Limitations\n\n\nThe 'alpaca' data is generated by a language model ('text-davinci-003') and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThe dataset is available under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).", "### Contributions" ]
[ "TAGS\n#task_categories-text-generation #language-Portuguese #license-cc-by-nc-4.0 #instruction-finetuning #region-us \n", "### Original Alpaca Dataset Summary\n\n\nAlpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.\n\n\nThe authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:\n\n\n* The 'text-davinci-003' engine to generate the instruction data instead of 'davinci'.\n* A new prompt was written that explicitly gave the requirement of instruction generation to 'text-davinci-003'.\n* Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.\n* The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.\n* Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.\n\n\nThis produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).\nIn a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by Self-Instruct.", "### Supported Tasks and Leaderboards\n\n\nThe Alpaca dataset designed for instruction training pretrained language models.", "### Languages\n\n\nThe data in Alpaca are in English (BCP-47 en).\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of \"train\" looks as follows:", "### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'instruction': describes the task the model should perform. Each of the 52K instructions is unique.\n* 'input': optional context or input for the task. For example, when the instruction is \"Summarize the following article\", the input is the article. Around 40% of the examples have an input.\n* 'output': the answer to the instruction as generated by 'text-davinci-003'.\n* 'text': the 'instruction', 'input' and 'output' formatted with the prompt template used by the authors for fine-tuning their models.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nExcerpt the blog post accompanying the release of this dataset:\n\n\n\n> \n> We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models.\n> \n> \n>", "### Discussion of Biases", "### Other Known Limitations\n\n\nThe 'alpaca' data is generated by a language model ('text-davinci-003') and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThe dataset is available under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).", "### Contributions" ]
[ 40, 289, 26, 25, 18, 149, 11, 7, 4, 10, 10, 5, 5, 9, 18, 543, 8, 73, 6, 28, 5 ]
[ "passage: TAGS\n#task_categories-text-generation #language-Portuguese #license-cc-by-nc-4.0 #instruction-finetuning #region-us \n### Original Alpaca Dataset Summary\n\n\nAlpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.\n\n\nThe authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:\n\n\n* The 'text-davinci-003' engine to generate the instruction data instead of 'davinci'.\n* A new prompt was written that explicitly gave the requirement of instruction generation to 'text-davinci-003'.\n* Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.\n* The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.\n* Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.\n\n\nThis produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).\nIn a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by Self-Instruct.### Supported Tasks and Leaderboards\n\n\nThe Alpaca dataset designed for instruction training pretrained language models.### Languages\n\n\nThe data in Alpaca are in English (BCP-47 en).\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example of \"train\" looks as follows:", "passage: ### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'instruction': describes the task the model should perform. Each of the 52K instructions is unique.\n* 'input': optional context or input for the task. For example, when the instruction is \"Summarize the following article\", the input is the article. Around 40% of the examples have an input.\n* 'output': the answer to the instruction as generated by 'text-davinci-003'.\n* 'text': the 'instruction', 'input' and 'output' formatted with the prompt template used by the authors for fine-tuning their models.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------" ]
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f3b7a74bb8df8337dbb7c15d08c948c9de9c2c66
# Dataset Card for "logos" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AngelUrq/logos
[ "region:us" ]
2023-03-17T23:30:21+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16715737.0, "num_examples": 300}], "download_size": 16684650, "dataset_size": 16715737.0}}
2023-03-17T23:30:37+00:00
[]
[]
TAGS #region-us
# Dataset Card for "logos" More Information needed
[ "# Dataset Card for \"logos\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"logos\"\n\nMore Information needed" ]
[ 6, 12 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"logos\"\n\nMore Information needed" ]
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66f5d804d239493f4ea2c25c2bbc6f088bf2e745
# Dataset Card for Dataset Name This dataset inherits from [mnli](https://huggingface.co/datasets/glue/viewer/mnli/train), and the only changes is to downsample to the same size as mednli. It is created via ```python with open("/path/to/mednli/mli_train_v1.jsonl") as f: data = [json.loads(line) for line in f.readlines()] raw_datasets = load_dataset("glue", "mnli") raw_datasets["train"] = raw_datasets["train"].shuffle(seed=42).select(range(len(data))) raw_datasets.push_to_hub("cnut1648/mnli_resampled_as_mednli") ```
cnut1648/mnli_resampled_as_mednli
[ "nli", "region:us" ]
2023-03-18T02:58:10+00:00
{"tags": ["nli"]}
2023-03-18T03:02:18+00:00
[]
[]
TAGS #nli #region-us
# Dataset Card for Dataset Name This dataset inherits from mnli, and the only changes is to downsample to the same size as mednli. It is created via
[ "# Dataset Card for Dataset Name\n\nThis dataset inherits from mnli, and the only changes is to downsample to the same size as mednli. It is created via" ]
[ "TAGS\n#nli #region-us \n", "# Dataset Card for Dataset Name\n\nThis dataset inherits from mnli, and the only changes is to downsample to the same size as mednli. It is created via" ]
[ 9, 41 ]
[ "passage: TAGS\n#nli #region-us \n# Dataset Card for Dataset Name\n\nThis dataset inherits from mnli, and the only changes is to downsample to the same size as mednli. It is created via" ]
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2c4bae1f82214872cf90d3e8f0e5c81360f5c019
# Dataset Card for "cup-it-ds" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/cup-it-ds
[ "region:us" ]
2023-03-18T03:34:59+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "comments", "list": [{"name": "score", "dtype": "int64"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 216623319.65051585, "num_examples": 79296}, {"name": "test", "num_bytes": 39763692, "num_examples": 14004}, {"name": "validation", "num_bytes": 24070168.34948415, "num_examples": 8811}], "download_size": 177572976, "dataset_size": 280457180.0}}
2023-03-18T03:35:11+00:00
[]
[]
TAGS #region-us
# Dataset Card for "cup-it-ds" More Information needed
[ "# Dataset Card for \"cup-it-ds\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"cup-it-ds\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"cup-it-ds\"\n\nMore Information needed" ]
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7ab4aa2b13f4e4bea0aaa9d0d05bbb7437147dab
# Dataset Card for "cup-it-ds-ranked" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/cup-it-ds-ranked
[ "region:us" ]
2023-03-18T04:11:46+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "0", "dtype": "string"}, {"name": "1", "dtype": "string"}, {"name": "2", "dtype": "string"}, {"name": "3", "dtype": "string"}, {"name": "4", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 212938901, "num_examples": 79296}, {"name": "validation", "num_bytes": 23877879, "num_examples": 8811}], "download_size": 157015815, "dataset_size": 236816780}}
2023-03-18T04:11:54+00:00
[]
[]
TAGS #region-us
# Dataset Card for "cup-it-ds-ranked" More Information needed
[ "# Dataset Card for \"cup-it-ds-ranked\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"cup-it-ds-ranked\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"cup-it-ds-ranked\"\n\nMore Information needed" ]
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0ab322f39a3e6d7da5867b805edfd3b0735b9402
# Dataset Card for "cup-it-ds-ranked-small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/cup-it-ds-ranked-small
[ "region:us" ]
2023-03-18T05:07:35+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "0", "dtype": "string"}, {"name": "1", "dtype": "string"}, {"name": "2", "dtype": "string"}, {"name": "3", "dtype": "string"}, {"name": "4", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10582267, "num_examples": 3965}, {"name": "validation", "num_bytes": 1157743, "num_examples": 441}], "download_size": 7786012, "dataset_size": 11740010}}
2023-03-18T05:07:38+00:00
[]
[]
TAGS #region-us
# Dataset Card for "cup-it-ds-ranked-small" More Information needed
[ "# Dataset Card for \"cup-it-ds-ranked-small\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"cup-it-ds-ranked-small\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"cup-it-ds-ranked-small\"\n\nMore Information needed" ]
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ef91de7514c2096df05865d38537aeae5416b053
# Dataset Card for "cup-it-ds-pairwise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/cup-it-ds-pairwise
[ "region:us" ]
2023-03-18T06:16:13+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 339351915, "num_examples": 317184}, {"name": "validation", "num_bytes": 38030463, "num_examples": 35244}], "download_size": 238355872, "dataset_size": 377382378}}
2023-03-18T06:16:24+00:00
[]
[]
TAGS #region-us
# Dataset Card for "cup-it-ds-pairwise" More Information needed
[ "# Dataset Card for \"cup-it-ds-pairwise\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"cup-it-ds-pairwise\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"cup-it-ds-pairwise\"\n\nMore Information needed" ]
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91b9e90397690d9e7953ccdd78da34bd27dd84e6
# Dataset Card for "cup-it-ds-pairwise-small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/cup-it-ds-pairwise-small
[ "region:us" ]
2023-03-18T06:16:40+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16773882, "num_examples": 15859}, {"name": "validation", "num_bytes": 1849922, "num_examples": 1762}], "download_size": 11756382, "dataset_size": 18623804}}
2023-03-18T06:16:44+00:00
[]
[]
TAGS #region-us
# Dataset Card for "cup-it-ds-pairwise-small" More Information needed
[ "# Dataset Card for \"cup-it-ds-pairwise-small\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"cup-it-ds-pairwise-small\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"cup-it-ds-pairwise-small\"\n\nMore Information needed" ]
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4f9ae86d94bfd89dd0b7bfe33987ef70e9d58fa6
# Dataset Card for "little_dataset_celeb" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jlbaker361/little_dataset_celeb
[ "region:us" ]
2023-03-18T06:26:20+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1764150.0, "num_examples": 5}], "download_size": 355276, "dataset_size": 1764150.0}}
2023-03-18T06:26:22+00:00
[]
[]
TAGS #region-us
# Dataset Card for "little_dataset_celeb" More Information needed
[ "# Dataset Card for \"little_dataset_celeb\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"little_dataset_celeb\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"little_dataset_celeb\"\n\nMore Information needed" ]
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879f1683e45abb7baffc5eb3997fd2ffc858ece6
# Dataset Card for "stable-icons-128" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
naxalpha/stable-icons-128
[ "region:us" ]
2023-03-18T06:58:54+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "tags", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16579464.375, "num_examples": 5525}], "download_size": 16290486, "dataset_size": 16579464.375}}
2023-03-18T08:21:49+00:00
[]
[]
TAGS #region-us
# Dataset Card for "stable-icons-128" More Information needed
[ "# Dataset Card for \"stable-icons-128\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"stable-icons-128\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"stable-icons-128\"\n\nMore Information needed" ]
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6eabb05a6a61e38f40f6d8072c9cef530fc33f2b
# Dataset Card for "cup-it-ds-classification" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/cup-it-ds-classification
[ "region:us" ]
2023-03-18T07:38:37+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 231123609, "num_examples": 396480}, {"name": "validation", "num_bytes": 25903277, "num_examples": 44055}], "download_size": 155582258, "dataset_size": 257026886}}
2023-03-18T18:13:41+00:00
[]
[]
TAGS #region-us
# Dataset Card for "cup-it-ds-classification" More Information needed
[ "# Dataset Card for \"cup-it-ds-classification\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"cup-it-ds-classification\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"cup-it-ds-classification\"\n\nMore Information needed" ]
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dfab53c276d0b7beaa9ed2bb6a5f1de3c73f1ebd
# Dataset Card for "avatar-extra-lite_captioned-augmented" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jlbaker361/avatar-extra-lite_captioned-augmented
[ "region:us" ]
2023-03-18T08:15:56+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32361041.0, "num_examples": 135}], "download_size": 32349531, "dataset_size": 32361041.0}}
2023-03-18T08:16:04+00:00
[]
[]
TAGS #region-us
# Dataset Card for "avatar-extra-lite_captioned-augmented" More Information needed
[ "# Dataset Card for \"avatar-extra-lite_captioned-augmented\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"avatar-extra-lite_captioned-augmented\"\n\nMore Information needed" ]
[ 6, 24 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"avatar-extra-lite_captioned-augmented\"\n\nMore Information needed" ]
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138b40b25771da0c9631a0948cb676c0823d83a0
# Dataset Card for "CROHME_selected_Train_2014_png" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zhixiaoni/CROHME_selected_Train_2014_png
[ "region:us" ]
2023-03-18T09:17:22+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 15733272.15, "num_examples": 5618}], "download_size": 14207546, "dataset_size": 15733272.15}}
2023-03-18T09:17:38+00:00
[]
[]
TAGS #region-us
# Dataset Card for "CROHME_selected_Train_2014_png" More Information needed
[ "# Dataset Card for \"CROHME_selected_Train_2014_png\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"CROHME_selected_Train_2014_png\"\n\nMore Information needed" ]
[ 6, 23 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"CROHME_selected_Train_2014_png\"\n\nMore Information needed" ]
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915b3de09a80979e0faf00561ee1c9908de34a9b
# Dataset Card for "indian_food_images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MiteshRege/indian_food_images
[ "region:us" ]
2023-03-18T09:27:58+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "burger", "1": "butter_naan", "2": "chai", "3": "chapati", "4": "chole_bhature", "5": "dal_makhani", "6": "dhokla", "7": "fried_rice", "8": "idli", "9": "jalebi", "10": "kaathi_rolls", "11": "kadai_paneer", "12": "kulfi", "13": "masala_dosa", "14": "momos", "15": "paani_puri", "16": "pakode", "17": "pav_bhaji", "18": "pizza", "19": "samosa"}}}}], "splits": [{"name": "train", "num_bytes": 1478423639.5674334, "num_examples": 5328}, {"name": "test", "num_bytes": 224186839.3925666, "num_examples": 941}], "download_size": 1592823695, "dataset_size": 1702610478.96}}
2023-03-18T09:55:42+00:00
[]
[]
TAGS #region-us
# Dataset Card for "indian_food_images" More Information needed
[ "# Dataset Card for \"indian_food_images\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"indian_food_images\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"indian_food_images\"\n\nMore Information needed" ]
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f8b8a866b317c8e0bcb226ae6c3eb3a2b5a538d1
# Dataset Card for DWD ICON-EU Forecast This dataset is comprised of forecasts from the German Weather Service's (DWD) ICON-EU model. From 2020-March 2023 the forecasts contain variables that are relevant to solar and wind forecasting. From March 2023 to the present, all variables are included. Each forecast runs up to 5 days into the future, and the model is ran 4 times per day. This data is an archive of the publicly available data at https://opendata.dwd.de/weather/nwp/, converted to Zarr format with Xarray. No other processing of the data is performed. ## Dataset Details - **Curated by:** Jacob Bieker, Open Climate Fix - **License:** German Government Open Data License ### Dataset Sources [optional] - **Raw files:** https://opendata.dwd.de/weather/nwp/ Note: The raw files are deleted after 24 hours, and there is no long-term archive available publicly. ## Uses This data is intended for use in renewable energy forecasting, weather forecasting, and anything that can use high-quality weather forecasts over Europe. ## Dataset Structure The dataset is comprised of one Zarr file per forecast initialization time, and each forecast goes out between 48-120 hours. The files are located at data/year/month/day/YYYYMMDDHH.zarr.zip. ## Dataset Creation ### Curation Rationale The DWD ICON-EU model provides high-quality, high-resolution forecasts for European weather that is also publicly available and free of charge. The model should generally outperform NOAA's GFS forecast model, and has a higher temporal and spatial resolution. The main downside of this model is that the files are only available for a short period publicly, so this dataset was setup to provide a public archive of the forecasts for use by researchers in many fields, but especially renewable energy forecasting and weather forecasting. ### Source Data The source data is the grib2 files from the DWD Open Data Server. #### Data Collection and Processing The data is collected every day, around 6-8 hours after forecast initialization time to ensure the forecast is finished running before the data is pulled. The grib2 files are opened with Xarray and collated into a single Xarray Dataset, with one data variable per ICON variable. Surface variables have "_s" appended to their names to differentiate them from multi-level variables. The Dataset is then written to Zarr using "ocf_blosc2" to encode and compress the variables. No scaling or changing of the variables values is performed. #### Who are the source data producers? German Weather Service (DWD) ### Recommendations These files can be opened directly from HuggingFace, and streamed in with Xarray. HuggingFace is fairly slow though, so the recommended way would be to download the files you want and open them locally. In either case, to access the data you can do the following ```python import ocf_blosc2 import xarray as xr data = xr.open_zarr("path/to/zarr/file") print(data) ``` Alternatively, for using the data in forecasting, there is the `ocf_datapipes` package for loading and training renewable energy forecasting models with multi-modal inputs, including ICON, but also satellite data, PV readings, etc. ## Dataset Card Contact Jacob Bieker: [email protected]
openclimatefix/dwd-icon-eu
[ "size_categories:1K<n<10K", "license:mit", "climate", "doi:10.57967/hf/0879", "region:us" ]
2023-03-18T09:42:30+00:00
{"license": "mit", "size_categories": ["1K<n<10K"], "pretty_name": "DWD ICON-EU Forecasts", "tags": ["climate"]}
2024-02-17T14:45:25+00:00
[]
[]
TAGS #size_categories-1K<n<10K #license-mit #climate #doi-10.57967/hf/0879 #region-us
# Dataset Card for DWD ICON-EU Forecast This dataset is comprised of forecasts from the German Weather Service's (DWD) ICON-EU model. From 2020-March 2023 the forecasts contain variables that are relevant to solar and wind forecasting. From March 2023 to the present, all variables are included. Each forecast runs up to 5 days into the future, and the model is ran 4 times per day. This data is an archive of the publicly available data at URL converted to Zarr format with Xarray. No other processing of the data is performed. ## Dataset Details - Curated by: Jacob Bieker, Open Climate Fix - License: German Government Open Data License ### Dataset Sources [optional] - Raw files: URL Note: The raw files are deleted after 24 hours, and there is no long-term archive available publicly. ## Uses This data is intended for use in renewable energy forecasting, weather forecasting, and anything that can use high-quality weather forecasts over Europe. ## Dataset Structure The dataset is comprised of one Zarr file per forecast initialization time, and each forecast goes out between 48-120 hours. The files are located at data/year/month/day/URL. ## Dataset Creation ### Curation Rationale The DWD ICON-EU model provides high-quality, high-resolution forecasts for European weather that is also publicly available and free of charge. The model should generally outperform NOAA's GFS forecast model, and has a higher temporal and spatial resolution. The main downside of this model is that the files are only available for a short period publicly, so this dataset was setup to provide a public archive of the forecasts for use by researchers in many fields, but especially renewable energy forecasting and weather forecasting. ### Source Data The source data is the grib2 files from the DWD Open Data Server. #### Data Collection and Processing The data is collected every day, around 6-8 hours after forecast initialization time to ensure the forecast is finished running before the data is pulled. The grib2 files are opened with Xarray and collated into a single Xarray Dataset, with one data variable per ICON variable. Surface variables have "_s" appended to their names to differentiate them from multi-level variables. The Dataset is then written to Zarr using "ocf_blosc2" to encode and compress the variables. No scaling or changing of the variables values is performed. #### Who are the source data producers? German Weather Service (DWD) ### Recommendations These files can be opened directly from HuggingFace, and streamed in with Xarray. HuggingFace is fairly slow though, so the recommended way would be to download the files you want and open them locally. In either case, to access the data you can do the following Alternatively, for using the data in forecasting, there is the 'ocf_datapipes' package for loading and training renewable energy forecasting models with multi-modal inputs, including ICON, but also satellite data, PV readings, etc. ## Dataset Card Contact Jacob Bieker: jacob@URL
[ "# Dataset Card for DWD ICON-EU Forecast\n\nThis dataset is comprised of forecasts from the German Weather Service's (DWD) ICON-EU model. From 2020-March 2023 the forecasts contain variables that are relevant to solar and wind\nforecasting. From March 2023 to the present, all variables are included. Each forecast runs up to 5 days into the future, and the model is ran 4 times per day. This data is an archive of \nthe publicly available data at URL converted to Zarr format with Xarray. No other processing of the data is performed.", "## Dataset Details\n\n- Curated by: Jacob Bieker, Open Climate Fix\n- License: German Government Open Data License", "### Dataset Sources [optional]\n\n\n- Raw files: URL\n\nNote: The raw files are deleted after 24 hours, and there is no long-term archive available publicly.", "## Uses\n\nThis data is intended for use in renewable energy forecasting, weather forecasting, and anything that can use high-quality weather forecasts over Europe.", "## Dataset Structure\n\nThe dataset is comprised of one Zarr file per forecast initialization time, and each forecast goes out between 48-120 hours. The files are located at data/year/month/day/URL.", "## Dataset Creation", "### Curation Rationale\n\nThe DWD ICON-EU model provides high-quality, high-resolution forecasts for European weather that is also publicly available and free of charge. The model should generally outperform\nNOAA's GFS forecast model, and has a higher temporal and spatial resolution. The main downside of this model is that the files are only available for a short period publicly, so this dataset\nwas setup to provide a public archive of the forecasts for use by researchers in many fields, but especially renewable energy forecasting and weather forecasting.", "### Source Data\n\nThe source data is the grib2 files from the DWD Open Data Server.", "#### Data Collection and Processing\n\nThe data is collected every day, around 6-8 hours after forecast initialization time to ensure the forecast is finished running before the data is pulled. The grib2 files are opened\nwith Xarray and collated into a single Xarray Dataset, with one data variable per ICON variable. Surface variables have \"_s\" appended to their names to differentiate them from multi-level variables.\nThe Dataset is then written to Zarr using \"ocf_blosc2\" to encode and compress the variables. No scaling or changing of the variables values is performed.", "#### Who are the source data producers?\n\nGerman Weather Service (DWD)", "### Recommendations\n\nThese files can be opened directly from HuggingFace, and streamed in with Xarray. HuggingFace is fairly slow though, so the recommended way would be to download the files you want\nand open them locally. In either case, to access the data you can do the following\n\n\n\nAlternatively, for using the data in forecasting, there is the 'ocf_datapipes' package for loading and training renewable energy forecasting models with multi-modal inputs, including\nICON, but also satellite data, PV readings, etc.", "## Dataset Card Contact\n\nJacob Bieker: jacob@URL" ]
[ "TAGS\n#size_categories-1K<n<10K #license-mit #climate #doi-10.57967/hf/0879 #region-us \n", "# Dataset Card for DWD ICON-EU Forecast\n\nThis dataset is comprised of forecasts from the German Weather Service's (DWD) ICON-EU model. From 2020-March 2023 the forecasts contain variables that are relevant to solar and wind\nforecasting. From March 2023 to the present, all variables are included. Each forecast runs up to 5 days into the future, and the model is ran 4 times per day. This data is an archive of \nthe publicly available data at URL converted to Zarr format with Xarray. No other processing of the data is performed.", "## Dataset Details\n\n- Curated by: Jacob Bieker, Open Climate Fix\n- License: German Government Open Data License", "### Dataset Sources [optional]\n\n\n- Raw files: URL\n\nNote: The raw files are deleted after 24 hours, and there is no long-term archive available publicly.", "## Uses\n\nThis data is intended for use in renewable energy forecasting, weather forecasting, and anything that can use high-quality weather forecasts over Europe.", "## Dataset Structure\n\nThe dataset is comprised of one Zarr file per forecast initialization time, and each forecast goes out between 48-120 hours. The files are located at data/year/month/day/URL.", "## Dataset Creation", "### Curation Rationale\n\nThe DWD ICON-EU model provides high-quality, high-resolution forecasts for European weather that is also publicly available and free of charge. The model should generally outperform\nNOAA's GFS forecast model, and has a higher temporal and spatial resolution. The main downside of this model is that the files are only available for a short period publicly, so this dataset\nwas setup to provide a public archive of the forecasts for use by researchers in many fields, but especially renewable energy forecasting and weather forecasting.", "### Source Data\n\nThe source data is the grib2 files from the DWD Open Data Server.", "#### Data Collection and Processing\n\nThe data is collected every day, around 6-8 hours after forecast initialization time to ensure the forecast is finished running before the data is pulled. The grib2 files are opened\nwith Xarray and collated into a single Xarray Dataset, with one data variable per ICON variable. Surface variables have \"_s\" appended to their names to differentiate them from multi-level variables.\nThe Dataset is then written to Zarr using \"ocf_blosc2\" to encode and compress the variables. No scaling or changing of the variables values is performed.", "#### Who are the source data producers?\n\nGerman Weather Service (DWD)", "### Recommendations\n\nThese files can be opened directly from HuggingFace, and streamed in with Xarray. HuggingFace is fairly slow though, so the recommended way would be to download the files you want\nand open them locally. In either case, to access the data you can do the following\n\n\n\nAlternatively, for using the data in forecasting, there is the 'ocf_datapipes' package for loading and training renewable energy forecasting models with multi-modal inputs, including\nICON, but also satellite data, PV readings, etc.", "## Dataset Card Contact\n\nJacob Bieker: jacob@URL" ]
[ 39, 131, 26, 40, 35, 50, 5, 127, 20, 133, 17, 126, 15 ]
[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #climate #doi-10.57967/hf/0879 #region-us \n# Dataset Card for DWD ICON-EU Forecast\n\nThis dataset is comprised of forecasts from the German Weather Service's (DWD) ICON-EU model. From 2020-March 2023 the forecasts contain variables that are relevant to solar and wind\nforecasting. From March 2023 to the present, all variables are included. Each forecast runs up to 5 days into the future, and the model is ran 4 times per day. This data is an archive of \nthe publicly available data at URL converted to Zarr format with Xarray. No other processing of the data is performed.## Dataset Details\n\n- Curated by: Jacob Bieker, Open Climate Fix\n- License: German Government Open Data License### Dataset Sources [optional]\n\n\n- Raw files: URL\n\nNote: The raw files are deleted after 24 hours, and there is no long-term archive available publicly.## Uses\n\nThis data is intended for use in renewable energy forecasting, weather forecasting, and anything that can use high-quality weather forecasts over Europe.## Dataset Structure\n\nThe dataset is comprised of one Zarr file per forecast initialization time, and each forecast goes out between 48-120 hours. The files are located at data/year/month/day/URL.## Dataset Creation### Curation Rationale\n\nThe DWD ICON-EU model provides high-quality, high-resolution forecasts for European weather that is also publicly available and free of charge. The model should generally outperform\nNOAA's GFS forecast model, and has a higher temporal and spatial resolution. The main downside of this model is that the files are only available for a short period publicly, so this dataset\nwas setup to provide a public archive of the forecasts for use by researchers in many fields, but especially renewable energy forecasting and weather forecasting.### Source Data\n\nThe source data is the grib2 files from the DWD Open Data Server." ]
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1e4365044b583730245443ac77b4d8a246c13836
# Dataset Card for DWD ICON Global Forecast This dataset is comprised of forecasts from the German Weather Service's (DWD) ICON-EU model from March 2023 to the present with all variables included. Each forecast runs up to 4 days into the future, and the model is ran 4 times per day. This data is an archive of the publicly available data at https://opendata.dwd.de/weather/nwp/, converted to Zarr format with Xarray. No other processing of the data is performed. ## Dataset Details - **Curated by:** Jacob Bieker, Open Climate Fix - **License:** German Government Open Data License ### Dataset Sources [optional] - **Raw files:** https://opendata.dwd.de/weather/nwp/ Note: The raw files are deleted after 24 hours, and there is no long-term archive available publicly. ## Uses This data is intended for use in renewable energy forecasting, weather forecasting, and anything that can use high-quality weather forecasts over Europe. ## Dataset Structure The dataset is comprised of one Zarr file per forecast initialization time, and each forecast goes out between 48-96 hours. The files are located at data/year/month/day/YYYYMMDDHH.zarr.zip. ## Dataset Creation ### Curation Rationale The DWD ICON Global model provides high-quality, high-resolution forecasts for Global weather that is also publicly available and free of charge. The model should generally outperform NOAA's GFS forecast model, and has a higher temporal and spatial resolution. The main downside of this model is that the files are only available for a short period publicly, so this dataset was setup to provide a public archive of the forecasts for use by researchers in many fields, but especially renewable energy forecasting and weather forecasting. ### Source Data The source data is the grib2 files from the DWD Open Data Server. #### Data Collection and Processing The data is collected every day, around 6-8 hours after forecast initialization time to ensure the forecast is finished running before the data is pulled. The grib2 files are opened with Xarray and collated into a single Xarray Dataset, with one data variable per ICON variable. Surface variables have "_s" appended to their names to differentiate them from multi-level variables. The Dataset is then written to Zarr using "ocf_blosc2" to encode and compress the variables. No scaling or changing of the variables values is performed. This does mean that the data is not in a regular lat/lon grid and is instead in the icosohedral grid of the model. To obtain a regular grid, the data will need to be regridded. #### Who are the source data producers? German Weather Service (DWD) ### Recommendations These files can be opened directly from HuggingFace, and streamed in with Xarray. HuggingFace is fairly slow though, so the recommended way would be to download the files you want and open them locally. In either case, to access the data you can do the following ```python import ocf_blosc2 import xarray as xr data = xr.open_zarr("path/to/zarr/file") print(data) ``` Alternatively, for using the data in forecasting, there is the `ocf_datapipes` package for loading and training renewable energy forecasting models with multi-modal inputs, including ICON, but also satellite data, PV readings, etc. ## Dataset Card Contact Jacob Bieker: [email protected]
openclimatefix/dwd-icon-global
[ "size_categories:1K<n<10K", "license:mit", "climate", "doi:10.57967/hf/0880", "region:us" ]
2023-03-18T09:42:45+00:00
{"license": "mit", "size_categories": ["1K<n<10K"], "pretty_name": "DWD ICON Global Forecasts", "tags": ["climate"]}
2024-02-17T13:39:34+00:00
[]
[]
TAGS #size_categories-1K<n<10K #license-mit #climate #doi-10.57967/hf/0880 #region-us
# Dataset Card for DWD ICON Global Forecast This dataset is comprised of forecasts from the German Weather Service's (DWD) ICON-EU model from March 2023 to the present with all variables included. Each forecast runs up to 4 days into the future, and the model is ran 4 times per day. This data is an archive of the publicly available data at URL converted to Zarr format with Xarray. No other processing of the data is performed. ## Dataset Details - Curated by: Jacob Bieker, Open Climate Fix - License: German Government Open Data License ### Dataset Sources [optional] - Raw files: URL Note: The raw files are deleted after 24 hours, and there is no long-term archive available publicly. ## Uses This data is intended for use in renewable energy forecasting, weather forecasting, and anything that can use high-quality weather forecasts over Europe. ## Dataset Structure The dataset is comprised of one Zarr file per forecast initialization time, and each forecast goes out between 48-96 hours. The files are located at data/year/month/day/URL. ## Dataset Creation ### Curation Rationale The DWD ICON Global model provides high-quality, high-resolution forecasts for Global weather that is also publicly available and free of charge. The model should generally outperform NOAA's GFS forecast model, and has a higher temporal and spatial resolution. The main downside of this model is that the files are only available for a short period publicly, so this dataset was setup to provide a public archive of the forecasts for use by researchers in many fields, but especially renewable energy forecasting and weather forecasting. ### Source Data The source data is the grib2 files from the DWD Open Data Server. #### Data Collection and Processing The data is collected every day, around 6-8 hours after forecast initialization time to ensure the forecast is finished running before the data is pulled. The grib2 files are opened with Xarray and collated into a single Xarray Dataset, with one data variable per ICON variable. Surface variables have "_s" appended to their names to differentiate them from multi-level variables. The Dataset is then written to Zarr using "ocf_blosc2" to encode and compress the variables. No scaling or changing of the variables values is performed. This does mean that the data is not in a regular lat/lon grid and is instead in the icosohedral grid of the model. To obtain a regular grid, the data will need to be regridded. #### Who are the source data producers? German Weather Service (DWD) ### Recommendations These files can be opened directly from HuggingFace, and streamed in with Xarray. HuggingFace is fairly slow though, so the recommended way would be to download the files you want and open them locally. In either case, to access the data you can do the following Alternatively, for using the data in forecasting, there is the 'ocf_datapipes' package for loading and training renewable energy forecasting models with multi-modal inputs, including ICON, but also satellite data, PV readings, etc. ## Dataset Card Contact Jacob Bieker: jacob@URL
[ "# Dataset Card for DWD ICON Global Forecast\n\nThis dataset is comprised of forecasts from the German Weather Service's (DWD) ICON-EU model from March 2023 to the present with all variables included. Each forecast runs up to 4 days into the future, and the model is ran 4 times per day. This data is an archive of \nthe publicly available data at URL converted to Zarr format with Xarray. No other processing of the data is performed.", "## Dataset Details\n\n- Curated by: Jacob Bieker, Open Climate Fix\n- License: German Government Open Data License", "### Dataset Sources [optional]\n\n\n- Raw files: URL\n\nNote: The raw files are deleted after 24 hours, and there is no long-term archive available publicly.", "## Uses\n\nThis data is intended for use in renewable energy forecasting, weather forecasting, and anything that can use high-quality weather forecasts over Europe.", "## Dataset Structure\n\nThe dataset is comprised of one Zarr file per forecast initialization time, and each forecast goes out between 48-96 hours. The files are located at data/year/month/day/URL.", "## Dataset Creation", "### Curation Rationale\n\nThe DWD ICON Global model provides high-quality, high-resolution forecasts for Global weather that is also publicly available and free of charge. The model should generally outperform\nNOAA's GFS forecast model, and has a higher temporal and spatial resolution. The main downside of this model is that the files are only available for a short period publicly, so this dataset\nwas setup to provide a public archive of the forecasts for use by researchers in many fields, but especially renewable energy forecasting and weather forecasting.", "### Source Data\n\nThe source data is the grib2 files from the DWD Open Data Server.", "#### Data Collection and Processing\n\nThe data is collected every day, around 6-8 hours after forecast initialization time to ensure the forecast is finished running before the data is pulled. The grib2 files are opened\nwith Xarray and collated into a single Xarray Dataset, with one data variable per ICON variable. Surface variables have \"_s\" appended to their names to differentiate them from multi-level variables.\nThe Dataset is then written to Zarr using \"ocf_blosc2\" to encode and compress the variables. No scaling or changing of the variables values is performed. This does mean that the data is not in a regular lat/lon grid\nand is instead in the icosohedral grid of the model. To obtain a regular grid, the data will need to be regridded.", "#### Who are the source data producers?\n\nGerman Weather Service (DWD)", "### Recommendations\n\nThese files can be opened directly from HuggingFace, and streamed in with Xarray. HuggingFace is fairly slow though, so the recommended way would be to download the files you want\nand open them locally. In either case, to access the data you can do the following\n\n\n\nAlternatively, for using the data in forecasting, there is the 'ocf_datapipes' package for loading and training renewable energy forecasting models with multi-modal inputs, including\nICON, but also satellite data, PV readings, etc.", "## Dataset Card Contact\n\nJacob Bieker: jacob@URL" ]
[ "TAGS\n#size_categories-1K<n<10K #license-mit #climate #doi-10.57967/hf/0880 #region-us \n", "# Dataset Card for DWD ICON Global Forecast\n\nThis dataset is comprised of forecasts from the German Weather Service's (DWD) ICON-EU model from March 2023 to the present with all variables included. Each forecast runs up to 4 days into the future, and the model is ran 4 times per day. This data is an archive of \nthe publicly available data at URL converted to Zarr format with Xarray. No other processing of the data is performed.", "## Dataset Details\n\n- Curated by: Jacob Bieker, Open Climate Fix\n- License: German Government Open Data License", "### Dataset Sources [optional]\n\n\n- Raw files: URL\n\nNote: The raw files are deleted after 24 hours, and there is no long-term archive available publicly.", "## Uses\n\nThis data is intended for use in renewable energy forecasting, weather forecasting, and anything that can use high-quality weather forecasts over Europe.", "## Dataset Structure\n\nThe dataset is comprised of one Zarr file per forecast initialization time, and each forecast goes out between 48-96 hours. The files are located at data/year/month/day/URL.", "## Dataset Creation", "### Curation Rationale\n\nThe DWD ICON Global model provides high-quality, high-resolution forecasts for Global weather that is also publicly available and free of charge. The model should generally outperform\nNOAA's GFS forecast model, and has a higher temporal and spatial resolution. The main downside of this model is that the files are only available for a short period publicly, so this dataset\nwas setup to provide a public archive of the forecasts for use by researchers in many fields, but especially renewable energy forecasting and weather forecasting.", "### Source Data\n\nThe source data is the grib2 files from the DWD Open Data Server.", "#### Data Collection and Processing\n\nThe data is collected every day, around 6-8 hours after forecast initialization time to ensure the forecast is finished running before the data is pulled. The grib2 files are opened\nwith Xarray and collated into a single Xarray Dataset, with one data variable per ICON variable. Surface variables have \"_s\" appended to their names to differentiate them from multi-level variables.\nThe Dataset is then written to Zarr using \"ocf_blosc2\" to encode and compress the variables. No scaling or changing of the variables values is performed. This does mean that the data is not in a regular lat/lon grid\nand is instead in the icosohedral grid of the model. To obtain a regular grid, the data will need to be regridded.", "#### Who are the source data producers?\n\nGerman Weather Service (DWD)", "### Recommendations\n\nThese files can be opened directly from HuggingFace, and streamed in with Xarray. HuggingFace is fairly slow though, so the recommended way would be to download the files you want\nand open them locally. In either case, to access the data you can do the following\n\n\n\nAlternatively, for using the data in forecasting, there is the 'ocf_datapipes' package for loading and training renewable energy forecasting models with multi-modal inputs, including\nICON, but also satellite data, PV readings, etc.", "## Dataset Card Contact\n\nJacob Bieker: jacob@URL" ]
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[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #climate #doi-10.57967/hf/0880 #region-us \n# Dataset Card for DWD ICON Global Forecast\n\nThis dataset is comprised of forecasts from the German Weather Service's (DWD) ICON-EU model from March 2023 to the present with all variables included. Each forecast runs up to 4 days into the future, and the model is ran 4 times per day. This data is an archive of \nthe publicly available data at URL converted to Zarr format with Xarray. No other processing of the data is performed.## Dataset Details\n\n- Curated by: Jacob Bieker, Open Climate Fix\n- License: German Government Open Data License### Dataset Sources [optional]\n\n\n- Raw files: URL\n\nNote: The raw files are deleted after 24 hours, and there is no long-term archive available publicly.## Uses\n\nThis data is intended for use in renewable energy forecasting, weather forecasting, and anything that can use high-quality weather forecasts over Europe.## Dataset Structure\n\nThe dataset is comprised of one Zarr file per forecast initialization time, and each forecast goes out between 48-96 hours. The files are located at data/year/month/day/URL.## Dataset Creation### Curation Rationale\n\nThe DWD ICON Global model provides high-quality, high-resolution forecasts for Global weather that is also publicly available and free of charge. The model should generally outperform\nNOAA's GFS forecast model, and has a higher temporal and spatial resolution. The main downside of this model is that the files are only available for a short period publicly, so this dataset\nwas setup to provide a public archive of the forecasts for use by researchers in many fields, but especially renewable energy forecasting and weather forecasting.### Source Data\n\nThe source data is the grib2 files from the DWD Open Data Server." ]
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1d4265cd4dc880f81493c83824eda8ed487f321c
# Kosovo-Parliament-Transcriptions [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Kushtrimvisoka/Kosovo-Parliament-Transcriptions/blob/main/Kosovo_Parliament_Transcriptions.ipynb) The dataset comprises transcripts of speeches delivered by members of the Kosovo Assembly during parliamentary sessions spanning from 2007. The goal of this repository is to provide a valuable resource for researchers and professionals interested in natural language processing, or political discourse analysis. # Data source The dataset was compiled from publicly available transcripts published on the current and old official website of the Kosovo Assembly (https://kuvendikosoves.org/). # Data Preperation The dataset was compiled by downloading PDF files and converting them to a text format using OCR. The resulting text was then cleaned to fix punctuation and spelling errors. It's important to note that due to the complexity of the PDF-to-text conversion process, the dataset may still contain typos and other errors. As a result, the dataset is provided "as is". Additionally, it should be noted that the dataset includes speeches given in languages other than Albanian. # To do - [ ] Conduct additional quality assurance checks to identify and correct any remaining errors in the dataset. - [ ] Add a column for the language of the speech. - [ ] Add a column for the party of the speaker. # Dataset structure The dataset contains the following fields: text, speaker, date, id, num_tokens. # Usage ```python from datasets import load_dataset dataset = load_dataset('Kushtrim/Kosovo-Parliament-Transcriptions') ``` # License The dataset is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). # Citation If you use this dataset in your research, please consider citing this repository.
Kushtrim/Kosovo-Parliament-Transcriptions
[ "size_categories:100K<n<1M", "source_datasets:Kuvendi i Kosovës", "language:sq", "license:cc-by-4.0", "region:us" ]
2023-03-18T11:10:30+00:00
{"language": "sq", "license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "source_datasets": "Kuvendi i Kosov\u00ebs", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "speaker", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "num_tokens", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 171918647, "num_examples": 129104}], "download_size": 86022919, "dataset_size": 171918647}}
2024-01-24T11:39:43+00:00
[]
[ "sq" ]
TAGS #size_categories-100K<n<1M #source_datasets-Kuvendi i Kosovës #language-Albanian #license-cc-by-4.0 #region-us
# Kosovo-Parliament-Transcriptions ![Open In Colab](URL The dataset comprises transcripts of speeches delivered by members of the Kosovo Assembly during parliamentary sessions spanning from 2007. The goal of this repository is to provide a valuable resource for researchers and professionals interested in natural language processing, or political discourse analysis. # Data source The dataset was compiled from publicly available transcripts published on the current and old official website of the Kosovo Assembly (URL # Data Preperation The dataset was compiled by downloading PDF files and converting them to a text format using OCR. The resulting text was then cleaned to fix punctuation and spelling errors. It's important to note that due to the complexity of the PDF-to-text conversion process, the dataset may still contain typos and other errors. As a result, the dataset is provided "as is". Additionally, it should be noted that the dataset includes speeches given in languages other than Albanian. # To do - [ ] Conduct additional quality assurance checks to identify and correct any remaining errors in the dataset. - [ ] Add a column for the language of the speech. - [ ] Add a column for the party of the speaker. # Dataset structure The dataset contains the following fields: text, speaker, date, id, num_tokens. # Usage # License The dataset is licensed under the Creative Commons Attribution 4.0 International License (URL If you use this dataset in your research, please consider citing this repository.
[ "# Kosovo-Parliament-Transcriptions\n\n![Open In Colab](URL\n\nThe dataset comprises transcripts of speeches delivered by members of the Kosovo Assembly during parliamentary sessions spanning from 2007. The goal of this repository is to provide a valuable resource for researchers and professionals interested in natural language processing, or political discourse analysis.", "# Data source\n\nThe dataset was compiled from publicly available transcripts published on the current and old official website of the Kosovo Assembly (URL", "# Data Preperation\n\nThe dataset was compiled by downloading PDF files and converting them to a text format using OCR. The resulting text was then cleaned to fix punctuation and spelling errors. It's important to note that due to the complexity of the PDF-to-text conversion process, the dataset may still contain typos and other errors. As a result, the dataset is provided \"as is\". Additionally, it should be noted that the dataset includes speeches given in languages other than Albanian.", "# To do\n\n- [ ] Conduct additional quality assurance checks to identify and correct any remaining errors in the dataset.\n- [ ] Add a column for the language of the speech.\n- [ ] Add a column for the party of the speaker.", "# Dataset structure\n\nThe dataset contains the following fields: text, speaker, date, id, num_tokens.", "# Usage", "# License\n\nThe dataset is licensed under the Creative Commons Attribution 4.0 International License (URL\n\nIf you use this dataset in your research, please consider citing this repository." ]
[ "TAGS\n#size_categories-100K<n<1M #source_datasets-Kuvendi i Kosovës #language-Albanian #license-cc-by-4.0 #region-us \n", "# Kosovo-Parliament-Transcriptions\n\n![Open In Colab](URL\n\nThe dataset comprises transcripts of speeches delivered by members of the Kosovo Assembly during parliamentary sessions spanning from 2007. The goal of this repository is to provide a valuable resource for researchers and professionals interested in natural language processing, or political discourse analysis.", "# Data source\n\nThe dataset was compiled from publicly available transcripts published on the current and old official website of the Kosovo Assembly (URL", "# Data Preperation\n\nThe dataset was compiled by downloading PDF files and converting them to a text format using OCR. The resulting text was then cleaned to fix punctuation and spelling errors. It's important to note that due to the complexity of the PDF-to-text conversion process, the dataset may still contain typos and other errors. As a result, the dataset is provided \"as is\". Additionally, it should be noted that the dataset includes speeches given in languages other than Albanian.", "# To do\n\n- [ ] Conduct additional quality assurance checks to identify and correct any remaining errors in the dataset.\n- [ ] Add a column for the language of the speech.\n- [ ] Add a column for the party of the speaker.", "# Dataset structure\n\nThe dataset contains the following fields: text, speaker, date, id, num_tokens.", "# Usage", "# License\n\nThe dataset is licensed under the Creative Commons Attribution 4.0 International License (URL\n\nIf you use this dataset in your research, please consider citing this repository." ]
[ 44, 80, 32, 122, 58, 27, 3, 37 ]
[ "passage: TAGS\n#size_categories-100K<n<1M #source_datasets-Kuvendi i Kosovës #language-Albanian #license-cc-by-4.0 #region-us \n# Kosovo-Parliament-Transcriptions\n\n![Open In Colab](URL\n\nThe dataset comprises transcripts of speeches delivered by members of the Kosovo Assembly during parliamentary sessions spanning from 2007. The goal of this repository is to provide a valuable resource for researchers and professionals interested in natural language processing, or political discourse analysis.# Data source\n\nThe dataset was compiled from publicly available transcripts published on the current and old official website of the Kosovo Assembly (URL# Data Preperation\n\nThe dataset was compiled by downloading PDF files and converting them to a text format using OCR. The resulting text was then cleaned to fix punctuation and spelling errors. It's important to note that due to the complexity of the PDF-to-text conversion process, the dataset may still contain typos and other errors. As a result, the dataset is provided \"as is\". Additionally, it should be noted that the dataset includes speeches given in languages other than Albanian.# To do\n\n- [ ] Conduct additional quality assurance checks to identify and correct any remaining errors in the dataset.\n- [ ] Add a column for the language of the speech.\n- [ ] Add a column for the party of the speaker.# Dataset structure\n\nThe dataset contains the following fields: text, speaker, date, id, num_tokens.# Usage# License\n\nThe dataset is licensed under the Creative Commons Attribution 4.0 International License (URL\n\nIf you use this dataset in your research, please consider citing this repository." ]
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c3c9f056669607abe69df3a3dfa3952d5f95f76d
# Dataset Card for "UA_speech_noisereduced_3c3p" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AravindVadlapudi02/UA_speech_noisereduced_3c3p
[ "region:us" ]
2023-03-18T11:11:56+00:00
{"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "healthy control", "1": "pathology"}}}}, {"name": "input_features", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 1152398400, "num_examples": 1200}, {"name": "test", "num_bytes": 4214897148, "num_examples": 4389}], "download_size": 621018100, "dataset_size": 5367295548}}
2023-03-18T11:13:14+00:00
[]
[]
TAGS #region-us
# Dataset Card for "UA_speech_noisereduced_3c3p" More Information needed
[ "# Dataset Card for \"UA_speech_noisereduced_3c3p\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"UA_speech_noisereduced_3c3p\"\n\nMore Information needed" ]
[ 6, 24 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"UA_speech_noisereduced_3c3p\"\n\nMore Information needed" ]
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a36959abd4494c05e36134fb77f8a089a1ea6b18
## Dataset description This dataset was used to fine-tune this [model](https://huggingface.co/keras-dreambooth/dreambooth_diffusion_hokusai) ## Demo You can try with this [demo](https://huggingface.co/spaces/keras-dreambooth/dreambooth_diffusion_hokusai) ## Intended uses & limitations Image of Hokusai artist
keras-dreambooth/hokusai-style
[ "size_categories:n<1K", "license:apache-2.0", "keras-dreambooth", "consentful", "diffusers", "text-to-image", "region:us" ]
2023-03-18T11:37:15+00:00
{"license": "apache-2.0", "size_categories": ["n<1K"], "tags": ["keras-dreambooth", "consentful", "diffusers", "text-to-image"]}
2023-03-18T13:48:37+00:00
[]
[]
TAGS #size_categories-n<1K #license-apache-2.0 #keras-dreambooth #consentful #diffusers #text-to-image #region-us
## Dataset description This dataset was used to fine-tune this model ## Demo You can try with this demo ## Intended uses & limitations Image of Hokusai artist
[ "## Dataset description\nThis dataset was used to fine-tune this model", "## Demo \nYou can try with this demo", "## Intended uses & limitations\nImage of Hokusai artist" ]
[ "TAGS\n#size_categories-n<1K #license-apache-2.0 #keras-dreambooth #consentful #diffusers #text-to-image #region-us \n", "## Dataset description\nThis dataset was used to fine-tune this model", "## Demo \nYou can try with this demo", "## Intended uses & limitations\nImage of Hokusai artist" ]
[ 45, 15, 8, 15 ]
[ "passage: TAGS\n#size_categories-n<1K #license-apache-2.0 #keras-dreambooth #consentful #diffusers #text-to-image #region-us \n## Dataset description\nThis dataset was used to fine-tune this model## Demo \nYou can try with this demo## Intended uses & limitations\nImage of Hokusai artist" ]
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fcc26d2223443bb6aaeff6de903e9a9745b73e11
# Dataset Card for "tib_002" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gigant/tib_002
[ "region:us" ]
2023-03-18T11:55:33+00:00
{"dataset_info": {"features": [{"name": "doi", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "video_url", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "genre", "dtype": "string"}, {"name": "release_year", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "contributors", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "transcript", "dtype": "string"}, {"name": "transcript_segments", "sequence": [{"name": "id", "dtype": "int32"}, {"name": "seek", "dtype": "int32"}, {"name": "start", "dtype": "float32"}, {"name": "end", "dtype": "float32"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "int32"}, {"name": "temperature", "dtype": "float32"}, {"name": "avg_logprob", "dtype": "float32"}, {"name": "compression_ratio", "dtype": "float32"}, {"name": "no_speech_prob", "dtype": "float32"}]}, {"name": "keyframes", "sequence": [{"name": "slide", "dtype": "string"}, {"name": "frames", "sequence": "int32"}, {"name": "timestamp", "sequence": "float32"}]}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1011381643.8712058, "num_examples": 8831}], "download_size": 486130872, "dataset_size": 1011381643.8712058}}
2023-03-18T11:56:02+00:00
[]
[]
TAGS #region-us
# Dataset Card for "tib_002" More Information needed
[ "# Dataset Card for \"tib_002\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"tib_002\"\n\nMore Information needed" ]
[ 6, 13 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"tib_002\"\n\nMore Information needed" ]
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185171ecfe5bf0fbcccb6050f75018757c5834f7
# Dataset Card for "eli5-modified" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
0x70DA/eli5-modified
[ "region:us" ]
2023-03-18T14:02:29+00:00
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "text", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 507700481, "num_examples": 272634}, {"name": "test", "num_bytes": 46615755, "num_examples": 24512}, {"name": "validation", "num_bytes": 18567879, "num_examples": 9812}], "download_size": 354225553, "dataset_size": 572884115}}
2023-03-18T14:02:56+00:00
[]
[]
TAGS #region-us
# Dataset Card for "eli5-modified" More Information needed
[ "# Dataset Card for \"eli5-modified\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"eli5-modified\"\n\nMore Information needed" ]
[ 6, 14 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"eli5-modified\"\n\nMore Information needed" ]
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afecf8fe77dfa0c3309a8eb55cdde5617257db24
Sadhguru Quotes collected from https://isha.sadhguru.org/au/en/wisdom/type/quotes
Crapp/sadQuotes
[ "license:cc", "doi:10.57967/hf/0454", "region:us" ]
2023-03-18T14:02:46+00:00
{"license": "cc"}
2023-03-18T15:02:11+00:00
[]
[]
TAGS #license-cc #doi-10.57967/hf/0454 #region-us
Sadhguru Quotes collected from URL
[]
[ "TAGS\n#license-cc #doi-10.57967/hf/0454 #region-us \n" ]
[ 23 ]
[ "passage: TAGS\n#license-cc #doi-10.57967/hf/0454 #region-us \n" ]
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342d5c6de828f4227cf7d5afa5ffd4ce70a7bc65
# Dataset Card for "VQAv2_sample_validation_text_davinci_002_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/VQAv2_sample_validation_text_davinci_002_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_2
[ "region:us" ]
2023-03-18T14:48:13+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 2443, "num_examples": 2}], "download_size": 10579, "dataset_size": 2443}}
2023-03-18T15:02:01+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_sample_validation_text_davinci_002_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_2" More Information needed
[ "# Dataset Card for \"VQAv2_sample_validation_text_davinci_002_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_2\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_sample_validation_text_davinci_002_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_2\"\n\nMore Information needed" ]
[ 6, 56 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_text_davinci_002_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_2\"\n\nMore Information needed" ]
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f47432f994d515497d02011b1a46bad13f2314eb
# Dataset Card for "VQAv2_sample_validation_text_davinci_002_mode_T_A_C_Q_rices_ns_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/VQAv2_sample_validation_text_davinci_002_mode_T_A_C_Q_rices_ns_2
[ "region:us" ]
2023-03-18T14:56:16+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 910, "num_examples": 2}, {"name": "fewshot_1", "num_bytes": 1467, "num_examples": 2}], "download_size": 12244, "dataset_size": 2377}}
2023-03-18T14:56:54+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_sample_validation_text_davinci_002_mode_T_A_C_Q_rices_ns_2" More Information needed
[ "# Dataset Card for \"VQAv2_sample_validation_text_davinci_002_mode_T_A_C_Q_rices_ns_2\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_sample_validation_text_davinci_002_mode_T_A_C_Q_rices_ns_2\"\n\nMore Information needed" ]
[ 6, 45 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_text_davinci_002_mode_T_A_C_Q_rices_ns_2\"\n\nMore Information needed" ]
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1c687f3519ce282fa571b8cb13cf1281c1e1b7b7
# Dataset Card for "VQAv2_sample_validation_text_davinci_002_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/VQAv2_sample_validation_text_davinci_002_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_10
[ "region:us" ]
2023-03-18T15:03:43+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 12880, "num_examples": 10}], "download_size": 13332, "dataset_size": 12880}}
2023-03-18T15:08:57+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_sample_validation_text_davinci_002_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_10" More Information needed
[ "# Dataset Card for \"VQAv2_sample_validation_text_davinci_002_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_10\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_sample_validation_text_davinci_002_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_10\"\n\nMore Information needed" ]
[ 6, 56 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_text_davinci_002_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_10\"\n\nMore Information needed" ]
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fec06f38f5fdf5e3a63424ddb66e6b56dfe5016c
# Dataset Card for "VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_10
[ "region:us" ]
2023-03-18T15:11:55+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 12831, "num_examples": 10}], "download_size": 13218, "dataset_size": 12831}}
2023-03-18T15:11:57+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_10" More Information needed
[ "# Dataset Card for \"VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_10\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_10\"\n\nMore Information needed" ]
[ 6, 56 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_10\"\n\nMore Information needed" ]
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b7e466e5fbac2cc1184df86532dc553e1563ebd5
# Dataset Card for "VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_100
[ "region:us" ]
2023-03-18T15:13:59+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 131343, "num_examples": 100}], "download_size": 66640, "dataset_size": 131343}}
2023-03-18T15:28:47+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_100" More Information needed
[ "# Dataset Card for \"VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_100\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_100\"\n\nMore Information needed" ]
[ 6, 56 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_100\"\n\nMore Information needed" ]
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959b08ae5be0e4d2caac8b5126e492d639817df6
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_100
[ "region:us" ]
2023-03-18T15:17:41+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 229695, "num_examples": 100}], "download_size": 64707, "dataset_size": 229695}}
2023-03-18T15:20:10+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_100" More Information needed
[ "# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_100\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_100\"\n\nMore Information needed" ]
[ 6, 59 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_100\"\n\nMore Information needed" ]
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eea61651528e8d3ed76a26de35e78bdf8abfbaf5
# Dataset Card for "VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_200" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_200
[ "region:us" ]
2023-03-18T15:32:52+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 255182, "num_examples": 200}], "download_size": 126747, "dataset_size": 255182}}
2023-03-18T15:32:54+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_200" More Information needed
[ "# Dataset Card for \"VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_200\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_200\"\n\nMore Information needed" ]
[ 6, 56 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_200\"\n\nMore Information needed" ]
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e77c2a447f766b2df1d62f9110df76aca3287f9e
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_200" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_200
[ "region:us" ]
2023-03-18T15:35:41+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 443915, "num_examples": 200}], "download_size": 103291, "dataset_size": 443915}}
2023-03-18T15:35:43+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_200" More Information needed
[ "# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_200\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_200\"\n\nMore Information needed" ]
[ 6, 59 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_200\"\n\nMore Information needed" ]
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db0e00317e4f3c5758ffea62cffd7ebfc1a326cd
# Dataset Card for "philschmid-de-blog" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
philschmid/philschmid-de-blog
[ "region:us" ]
2023-03-18T16:02:59+00:00
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "tags", "sequence": "string"}, {"name": "summary", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1151548, "num_examples": 87}], "download_size": 541717, "dataset_size": 1151548}}
2023-03-29T20:01:21+00:00
[]
[]
TAGS #region-us
# Dataset Card for "philschmid-de-blog" More Information needed
[ "# Dataset Card for \"philschmid-de-blog\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"philschmid-de-blog\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"philschmid-de-blog\"\n\nMore Information needed" ]
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f6193f55ea3a76a9236b88a7c96a29ec7e984735
# Dataset Card for "wikisource-red" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Zombely/wikisource-red
[ "region:us" ]
2023-03-18T16:29:40+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train_1", "num_bytes": 12996760047.0, "num_examples": 10000}, {"name": "train_2", "num_bytes": 10554030726.546, "num_examples": 9998}, {"name": "train_3", "num_bytes": 13696109295.506, "num_examples": 9999}, {"name": "train_4", "num_bytes": 15480963077.0, "num_examples": 10000}, {"name": "train_5", "num_bytes": 13559162557.0, "num_examples": 10000}, {"name": "validation", "num_bytes": 2388915116.642, "num_examples": 1542}], "download_size": 2424783685, "dataset_size": 68675940819.694}}
2023-03-19T17:57:33+00:00
[]
[]
TAGS #region-us
# Dataset Card for "wikisource-red" More Information needed
[ "# Dataset Card for \"wikisource-red\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"wikisource-red\"\n\nMore Information needed" ]
[ 6, 14 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"wikisource-red\"\n\nMore Information needed" ]
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562978f9768f3e4e9c2ad12025868a1a9f47dffb
# Dataset Card for "wikipedia_stage2_coverage_100000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MartinKu/wikipedia_stage2_coverage_100000
[ "region:us" ]
2023-03-18T16:31:57+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "S_V_position", "sequence": "int64"}, {"name": "O_C_position", "sequence": "int64"}, {"name": "start_point_list", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 1989835331, "num_examples": 10000}], "download_size": 700963436, "dataset_size": 1989835331}}
2023-03-21T20:22:08+00:00
[]
[]
TAGS #region-us
# Dataset Card for "wikipedia_stage2_coverage_100000" More Information needed
[ "# Dataset Card for \"wikipedia_stage2_coverage_100000\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"wikipedia_stage2_coverage_100000\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"wikipedia_stage2_coverage_100000\"\n\nMore Information needed" ]
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e9d5fff84b3a028e0c541e02c3d18d4486c8becc
# Dataset Card for "ebmnlp_pico" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
reginaboateng/ebmnlp_pico
[ "region:us" ]
2023-03-18T17:53:53+00:00
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "chunk_tags", "sequence": "string"}, {"name": "pos_tags", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "I-INT", "2": "I-OUT", "3": "I-PAR"}}}}], "splits": [{"name": "train", "num_bytes": 27639457, "num_examples": 23952}, {"name": "test", "num_bytes": 1482781, "num_examples": 2065}, {"name": "dev", "num_bytes": 7446993, "num_examples": 7049}], "download_size": 4095965, "dataset_size": 36569231}}
2023-03-18T17:54:00+00:00
[]
[]
TAGS #region-us
# Dataset Card for "ebmnlp_pico" More Information needed
[ "# Dataset Card for \"ebmnlp_pico\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"ebmnlp_pico\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"ebmnlp_pico\"\n\nMore Information needed" ]
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d64773d764275c4b9d8c3d7bb4a3ab5878d7034c
## <h1>Spongebob Transcripts Dataset 🧽</h1> The Spongebob Transcripts Dataset is a collection of transcripts from the beloved animated television series, Spongebob Squarepants. This dataset includes information on each line of dialogue spoken by a character, including the character's name, their replica, and the episode ID. The number of characters in the dataset: **84** Total number of words in the dataset: **~80,800 words**, **~4000 rows**, **Updated to full Season 1** ## <h3>Dataset Overview 📊</h3> |Column | Description | |------------|-------------------------------------| |**Speaker** | The character speaking the dialogue.| |**Replica** | The line of dialogue spoken. | |**EP_ID** | The episode ID of the transcript. | ## <h3>System Replicas🔍</h3> The system replicas describe the actions and events that occur in each episode. These replicas are written in a specific format, using brackets to indicate actions and events. **<h5>Replica Format</h5>** `{system} : [The episode opens with a bubble transition, and we see a coral reef under the sea. The camera zooms to initiate parallax scrolling, which reveals the city of Bikini Bottom. It continues zooming to show a brown rock, a Moai head, and a pineapple, which each contain inhabitants.]` ## <h3>Sample Data 💬</h3> |Speaker |Replica |EP_ID | |---------------|--------------------------------------------------------------------------------------------------------------------------------------------------|-------| |**Spongebob** | I just met this girl. She wears a hat full of... air. |s1e3_22| |**Patrick** | Do you mean she puts on "airs"? |s1e3_23| |**Spongebob** | I guess so. |s1e3_24| |**Patrick** | That's just fancy talk. If you wanna be fancy, hold your pinky up like this. The higher you hold it, the fancier you are. |s1e3_25| ## <h3>📊 Interactions with Dataset</h3> **<h5>Using Pandas to filter rows</h5>** 1. To find all rows with a specific ep_id, you can use the following code: ``` import pandas as pd #Read the CSV file into a Pandas DataFrame df = pd.read_csv('dataset.csv') #Define the ep_id you want to filter by ep_id = 's1e2' #Filter the DataFrame to get rows with an ep_id that starts with the defined ep_id filtered_df = df[df['ep_id'].str.startswith(ep_id)] #Print the filtered DataFrame print(filtered_df) ``` 2. To find rows where a specific character says a specific word or phrase, you can use the following code: ``` #Filter the DataFrame to get rows where a specific character says a specific word or phrase speaker = 'SpongeBob' word_or_phrase = 'jellyfish' filtered_df = df[df['speaker'] == speaker] filtered_df = filtered_df[filtered_df['replica'].str.contains(word_or_phrase)] #Print the filtered DataFrame print(filtered_df) ``` You can replace `SpongeBob` and `jellyfish` with any other speaker and word/phrase that you want to filter by. ## <h3>Data Sources 📝</h3> The transcripts were sourced *Encyclopedia SpongeBobia*. ## <h3>Potential Uses 🧐</h3> This Dataset could be used for a variety of natural language processing (NLP) tasks, including dialogue generation. It could also be used for educational purposes, such as studying the language and communication styles of different characters.
MarkK/spongebob_transcripts
[ "task_categories:text-generation", "task_categories:conversational", "size_categories:10K<n<100K", "language:en", "license:cc-by-sa-4.0", "cartoons", "region:us" ]
2023-03-18T17:56:59+00:00
{"language": ["en"], "license": "cc-by-sa-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "conversational"], "tags": ["cartoons"]}
2023-03-23T09:39:43+00:00
[]
[ "en" ]
TAGS #task_categories-text-generation #task_categories-conversational #size_categories-10K<n<100K #language-English #license-cc-by-sa-4.0 #cartoons #region-us
Spongebob Transcripts Dataset ----------------------------- The Spongebob Transcripts Dataset is a collection of transcripts from the beloved animated television series, Spongebob Squarepants. This dataset includes information on each line of dialogue spoken by a character, including the character's name, their replica, and the episode ID. The number of characters in the dataset: 84 Total number of words in the dataset: ~80,800 words, ~4000 rows, Updated to full Season 1 Dataset Overview ---------------- System Replicas --------------- The system replicas describe the actions and events that occur in each episode. These replicas are written in a specific format, using brackets to indicate actions and events. ##### Replica Format '{system} : [The episode opens with a bubble transition, and we see a coral reef under the sea. The camera zooms to initiate parallax scrolling, which reveals the city of Bikini Bottom. It continues zooming to show a brown rock, a Moai head, and a pineapple, which each contain inhabitants.]' Sample Data ----------- Speaker: Spongebob, Replica: I just met this girl. She wears a hat full of... air., EP\_ID: s1e3\_22 Speaker: Patrick, Replica: Do you mean she puts on "airs"?, EP\_ID: s1e3\_23 Speaker: Spongebob, Replica: I guess so., EP\_ID: s1e3\_24 Speaker: Patrick, Replica: That's just fancy talk. If you wanna be fancy, hold your pinky up like this. The higher you hold it, the fancier you are., EP\_ID: s1e3\_25 Interactions with Dataset -------------------------- ##### Using Pandas to filter rows 1. To find all rows with a specific ep\_id, you can use the following code: 2. To find rows where a specific character says a specific word or phrase, you can use the following code: You can replace 'SpongeBob' and 'jellyfish' with any other speaker and word/phrase that you want to filter by. Data Sources ------------ The transcripts were sourced *Encyclopedia SpongeBobia*. Potential Uses -------------- This Dataset could be used for a variety of natural language processing (NLP) tasks, including dialogue generation. It could also be used for educational purposes, such as studying the language and communication styles of different characters.
[ "##### Replica Format\n\n\n'{system} : [The episode opens with a bubble transition, and we see a coral reef under the sea. The camera zooms to initiate parallax scrolling, which reveals the city of Bikini Bottom. It continues zooming to show a brown rock, a Moai head, and a pineapple, which each contain inhabitants.]'\n\n\nSample Data\n-----------\n\n\nSpeaker: Spongebob, Replica: I just met this girl. She wears a hat full of... air., EP\\_ID: s1e3\\_22\nSpeaker: Patrick, Replica: Do you mean she puts on \"airs\"?, EP\\_ID: s1e3\\_23\nSpeaker: Spongebob, Replica: I guess so., EP\\_ID: s1e3\\_24\nSpeaker: Patrick, Replica: That's just fancy talk. If you wanna be fancy, hold your pinky up like this. The higher you hold it, the fancier you are., EP\\_ID: s1e3\\_25\n\n\n Interactions with Dataset\n--------------------------", "##### Using Pandas to filter rows\n\n\n1. To find all rows with a specific ep\\_id, you can use the following code:\n2. To find rows where a specific character says a specific word or phrase, you can use the following code:\n\n\nYou can replace 'SpongeBob' and 'jellyfish' with any other speaker and word/phrase that you want to filter by.\n\n\nData Sources\n------------\n\n\nThe transcripts were sourced *Encyclopedia SpongeBobia*.\n\n\nPotential Uses\n--------------\n\n\nThis Dataset could be used for a variety of natural language processing (NLP) tasks, including dialogue generation. It could also be used for educational purposes, such as studying the language and communication styles of different characters." ]
[ "TAGS\n#task_categories-text-generation #task_categories-conversational #size_categories-10K<n<100K #language-English #license-cc-by-sa-4.0 #cartoons #region-us \n", "##### Replica Format\n\n\n'{system} : [The episode opens with a bubble transition, and we see a coral reef under the sea. The camera zooms to initiate parallax scrolling, which reveals the city of Bikini Bottom. It continues zooming to show a brown rock, a Moai head, and a pineapple, which each contain inhabitants.]'\n\n\nSample Data\n-----------\n\n\nSpeaker: Spongebob, Replica: I just met this girl. She wears a hat full of... air., EP\\_ID: s1e3\\_22\nSpeaker: Patrick, Replica: Do you mean she puts on \"airs\"?, EP\\_ID: s1e3\\_23\nSpeaker: Spongebob, Replica: I guess so., EP\\_ID: s1e3\\_24\nSpeaker: Patrick, Replica: That's just fancy talk. If you wanna be fancy, hold your pinky up like this. The higher you hold it, the fancier you are., EP\\_ID: s1e3\\_25\n\n\n Interactions with Dataset\n--------------------------", "##### Using Pandas to filter rows\n\n\n1. To find all rows with a specific ep\\_id, you can use the following code:\n2. To find rows where a specific character says a specific word or phrase, you can use the following code:\n\n\nYou can replace 'SpongeBob' and 'jellyfish' with any other speaker and word/phrase that you want to filter by.\n\n\nData Sources\n------------\n\n\nThe transcripts were sourced *Encyclopedia SpongeBobia*.\n\n\nPotential Uses\n--------------\n\n\nThis Dataset could be used for a variety of natural language processing (NLP) tasks, including dialogue generation. It could also be used for educational purposes, such as studying the language and communication styles of different characters." ]
[ 58, 253, 164 ]
[ "passage: TAGS\n#task_categories-text-generation #task_categories-conversational #size_categories-10K<n<100K #language-English #license-cc-by-sa-4.0 #cartoons #region-us \n##### Replica Format\n\n\n'{system} : [The episode opens with a bubble transition, and we see a coral reef under the sea. The camera zooms to initiate parallax scrolling, which reveals the city of Bikini Bottom. It continues zooming to show a brown rock, a Moai head, and a pineapple, which each contain inhabitants.]'\n\n\nSample Data\n-----------\n\n\nSpeaker: Spongebob, Replica: I just met this girl. She wears a hat full of... air., EP\\_ID: s1e3\\_22\nSpeaker: Patrick, Replica: Do you mean she puts on \"airs\"?, EP\\_ID: s1e3\\_23\nSpeaker: Spongebob, Replica: I guess so., EP\\_ID: s1e3\\_24\nSpeaker: Patrick, Replica: That's just fancy talk. If you wanna be fancy, hold your pinky up like this. The higher you hold it, the fancier you are., EP\\_ID: s1e3\\_25\n\n\n Interactions with Dataset\n--------------------------##### Using Pandas to filter rows\n\n\n1. To find all rows with a specific ep\\_id, you can use the following code:\n2. To find rows where a specific character says a specific word or phrase, you can use the following code:\n\n\nYou can replace 'SpongeBob' and 'jellyfish' with any other speaker and word/phrase that you want to filter by.\n\n\nData Sources\n------------\n\n\nThe transcripts were sourced *Encyclopedia SpongeBobia*.\n\n\nPotential Uses\n--------------\n\n\nThis Dataset could be used for a variety of natural language processing (NLP) tasks, including dialogue generation. It could also be used for educational purposes, such as studying the language and communication styles of different characters." ]
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