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f6e34b4e22c9418ae3da0b1cf33d0a7999244e8a
# Dataset Card for "sq-anli_a3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
niv-al/sq-anli_a3
[ "language:sq", "region:us" ]
2023-02-10T15:20:03+00:00
{"language": ["sq"], "dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 10222003, "num_examples": 30000}, {"name": "validation", "num_bytes": 49754, "num_examples": 144}, {"name": "test", "num_bytes": 48945, "num_examples": 144}], "download_size": 6272043, "dataset_size": 10320702}}
2023-02-18T19:58:08+00:00
[]
[ "sq" ]
TAGS #language-Albanian #region-us
# Dataset Card for "sq-anli_a3" More Information needed
[ "# Dataset Card for \"sq-anli_a3\"\n\nMore Information needed" ]
[ "TAGS\n#language-Albanian #region-us \n", "# Dataset Card for \"sq-anli_a3\"\n\nMore Information needed" ]
[ 11, 18 ]
[ "passage: TAGS\n#language-Albanian #region-us \n# Dataset Card for \"sq-anli_a3\"\n\nMore Information needed" ]
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10b9eeb03ec04f574dad8f1e75a58484ea7e689d
# Dataset Card for "Europarl-ST-processed-mt-fr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tj-solergibert/Europarl-ST-processed-mt-fr
[ "region:us" ]
2023-02-10T15:27:34+00:00
{"dataset_info": {"features": [{"name": "source_text", "dtype": "string"}, {"name": "dest_text", "dtype": "string"}, {"name": "dest_lang", "dtype": {"class_label": {"names": {"0": "de", "1": "en", "2": "es", "3": "fr", "4": "it", "5": "nl", "6": "pl", "7": "pt", "8": "ro"}}}}], "splits": [{"name": "train", "num_bytes": 199700180, "num_examples": 560866}, {"name": "valid", "num_bytes": 27382683, "num_examples": 74712}, {"name": "test", "num_bytes": 28363822, "num_examples": 77906}], "download_size": 95095990, "dataset_size": 255446685}}
2023-02-10T16:20:20+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Europarl-ST-processed-mt-fr" More Information needed
[ "# Dataset Card for \"Europarl-ST-processed-mt-fr\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Europarl-ST-processed-mt-fr\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Europarl-ST-processed-mt-fr\"\n\nMore Information needed" ]
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d81aa1666c63fd3d14d0e47b3d403e878496a92b
# Dataset Card for "Europarl-ST-processed-mt-it" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tj-solergibert/Europarl-ST-processed-mt-it
[ "region:us" ]
2023-02-10T15:36:06+00:00
{"dataset_info": {"features": [{"name": "source_text", "dtype": "string"}, {"name": "dest_text", "dtype": "string"}, {"name": "dest_lang", "dtype": {"class_label": {"names": {"0": "de", "1": "en", "2": "es", "3": "fr", "4": "it", "5": "nl", "6": "pl", "7": "pt", "8": "ro"}}}}], "splits": [{"name": "train", "num_bytes": 179019467, "num_examples": 504773}, {"name": "valid", "num_bytes": 24674767, "num_examples": 67701}, {"name": "test", "num_bytes": 25588641, "num_examples": 70814}], "download_size": 89055953, "dataset_size": 229282875}}
2023-02-10T16:27:42+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Europarl-ST-processed-mt-it" More Information needed
[ "# Dataset Card for \"Europarl-ST-processed-mt-it\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Europarl-ST-processed-mt-it\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Europarl-ST-processed-mt-it\"\n\nMore Information needed" ]
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95e7e2b07c05c3065782a53d04362040af31ecad
# Dataset Card for "Europarl-ST-processed-mt-nl" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tj-solergibert/Europarl-ST-processed-mt-nl
[ "region:us" ]
2023-02-10T15:44:38+00:00
{"dataset_info": {"features": [{"name": "source_text", "dtype": "string"}, {"name": "dest_text", "dtype": "string"}, {"name": "dest_lang", "dtype": {"class_label": {"names": {"0": "de", "1": "en", "2": "es", "3": "fr", "4": "it", "5": "nl", "6": "pl", "7": "pt", "8": "ro"}}}}], "splits": [{"name": "train", "num_bytes": 138469976, "num_examples": 384704}, {"name": "valid", "num_bytes": 17984502, "num_examples": 48280}, {"name": "test", "num_bytes": 19576114, "num_examples": 53360}], "download_size": 66327284, "dataset_size": 176030592}}
2023-02-10T18:13:50+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Europarl-ST-processed-mt-nl" More Information needed
[ "# Dataset Card for \"Europarl-ST-processed-mt-nl\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Europarl-ST-processed-mt-nl\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Europarl-ST-processed-mt-nl\"\n\nMore Information needed" ]
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9e086906606038acfa1a33247876d546f83dc09b
# Dataset Card for "dataset-balanced-affecthqnet-fer2013" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Piro17/balancednumber-affecthqnet-fer2013
[ "region:us" ]
2023-02-10T15:46:56+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "anger", "1": "disgust", "2": "fear", "3": "happy", "4": "neutral", "5": "sad", "6": "surprise"}}}}], "splits": [{"name": "train", "num_bytes": 40414185.188, "num_examples": 21343}], "download_size": 1835629540, "dataset_size": 40414185.188}}
2023-02-10T15:48:19+00:00
[]
[]
TAGS #region-us
# Dataset Card for "dataset-balanced-affecthqnet-fer2013" More Information needed
[ "# Dataset Card for \"dataset-balanced-affecthqnet-fer2013\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"dataset-balanced-affecthqnet-fer2013\"\n\nMore Information needed" ]
[ 6, 24 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"dataset-balanced-affecthqnet-fer2013\"\n\nMore Information needed" ]
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9f3f868611dcaec554fdef4771f5a811545b945e
# Dataset Card for "sq-babi_nli_counting" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
niv-al/sq-babi_nli_counting
[ "language:sq", "region:us" ]
2023-02-10T15:56:45+00:00
{"language": ["sq"], "dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "not-entailed", "1": "entailed"}}}}], "splits": [{"name": "train", "num_bytes": 250135, "num_examples": 1000}, {"name": "validation", "num_bytes": 34065, "num_examples": 144}, {"name": "test", "num_bytes": 37455, "num_examples": 144}], "download_size": 60218, "dataset_size": 321655}}
2023-02-18T19:58:32+00:00
[]
[ "sq" ]
TAGS #language-Albanian #region-us
# Dataset Card for "sq-babi_nli_counting" More Information needed
[ "# Dataset Card for \"sq-babi_nli_counting\"\n\nMore Information needed" ]
[ "TAGS\n#language-Albanian #region-us \n", "# Dataset Card for \"sq-babi_nli_counting\"\n\nMore Information needed" ]
[ 11, 21 ]
[ "passage: TAGS\n#language-Albanian #region-us \n# Dataset Card for \"sq-babi_nli_counting\"\n\nMore Information needed" ]
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05c787b56ea336fdd2addfaca1a1d47d1b3f8c3a
# Dataset Card for "sq-babi_nli_positional-reasoning" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
niv-al/sq-babi_nli_positional-reasoning
[ "language:sq", "region:us" ]
2023-02-10T16:21:18+00:00
{"language": ["sq"], "dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "not-entailed", "1": "entailed"}}}}], "splits": [{"name": "train", "num_bytes": 152195, "num_examples": 1000}, {"name": "validation", "num_bytes": 21191, "num_examples": 144}, {"name": "test", "num_bytes": 21022, "num_examples": 144}], "download_size": 17282, "dataset_size": 194408}}
2023-02-18T19:59:07+00:00
[]
[ "sq" ]
TAGS #language-Albanian #region-us
# Dataset Card for "sq-babi_nli_positional-reasoning" More Information needed
[ "# Dataset Card for \"sq-babi_nli_positional-reasoning\"\n\nMore Information needed" ]
[ "TAGS\n#language-Albanian #region-us \n", "# Dataset Card for \"sq-babi_nli_positional-reasoning\"\n\nMore Information needed" ]
[ 11, 25 ]
[ "passage: TAGS\n#language-Albanian #region-us \n# Dataset Card for \"sq-babi_nli_positional-reasoning\"\n\nMore Information needed" ]
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a9240eca6217d5d14ef73d2645e0de5cdcf1ca76
# Dataset Card for "sq-babi_nli_size-reasoning" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
niv-al/sq-babi_nli_size-reasoning
[ "language:sq", "region:us" ]
2023-02-10T17:03:37+00:00
{"language": ["sq"], "dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "not-entailed", "1": "entailed"}}}}], "splits": [{"name": "train", "num_bytes": 276956, "num_examples": 1000}, {"name": "validation", "num_bytes": 38395, "num_examples": 144}, {"name": "test", "num_bytes": 38898, "num_examples": 144}], "download_size": 32189, "dataset_size": 354249}}
2023-02-18T19:59:16+00:00
[]
[ "sq" ]
TAGS #language-Albanian #region-us
# Dataset Card for "sq-babi_nli_size-reasoning" More Information needed
[ "# Dataset Card for \"sq-babi_nli_size-reasoning\"\n\nMore Information needed" ]
[ "TAGS\n#language-Albanian #region-us \n", "# Dataset Card for \"sq-babi_nli_size-reasoning\"\n\nMore Information needed" ]
[ 11, 24 ]
[ "passage: TAGS\n#language-Albanian #region-us \n# Dataset Card for \"sq-babi_nli_size-reasoning\"\n\nMore Information needed" ]
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46b273656c822dfacc92ce28419c9f83c5693ebd
# Dataset Card for "Europarl-ST-processed-mt-pt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tj-solergibert/Europarl-ST-processed-mt-pt
[ "region:us" ]
2023-02-10T17:17:09+00:00
{"dataset_info": {"features": [{"name": "source_text", "dtype": "string"}, {"name": "dest_text", "dtype": "string"}, {"name": "dest_lang", "dtype": {"class_label": {"names": {"0": "de", "1": "en", "2": "es", "3": "fr", "4": "it", "5": "nl", "6": "pl", "7": "pt", "8": "ro"}}}}], "splits": [{"name": "train", "num_bytes": 138047666, "num_examples": 384704}, {"name": "valid", "num_bytes": 17922979, "num_examples": 48280}, {"name": "test", "num_bytes": 19562527, "num_examples": 53360}], "download_size": 67380711, "dataset_size": 175533172}}
2023-02-10T18:04:10+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Europarl-ST-processed-mt-pt" More Information needed
[ "# Dataset Card for \"Europarl-ST-processed-mt-pt\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Europarl-ST-processed-mt-pt\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Europarl-ST-processed-mt-pt\"\n\nMore Information needed" ]
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d0aee2f08211c56141cd0fb1e3d942469a7c0d55
# Dataset Card for "VQAv2_sample_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/VQAv2_sample_train
[ "region:us" ]
2023-02-10T17:59:46+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": "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": 158100925.0, "num_examples": 1000}], "download_size": 155253264, "dataset_size": 158100925.0}}
2023-02-12T00:01:41+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_sample_train" More Information needed
[ "# Dataset Card for \"VQAv2_sample_train\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_sample_train\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_train\"\n\nMore Information needed" ]
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99487d2651df3799002b2fb3e455741744514a02
# Dataset Card for "VQAv2_sample_validation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/VQAv2_sample_validation
[ "region:us" ]
2023-02-10T17:59:57+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": "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": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_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", "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": "new_info_captions3", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": {"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", "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": "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": "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": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "blip_caption_beam_5_Salesforce_blip2_flan_t5_xxl", "dtype": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_all_patches", "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_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_all_patches", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "blip_caption_topk_50_Salesforce_blip_image_captioning_base_multiple", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_clip_caption_all_patches_Salesforce_blip_image_captioning_large__ViT_L_14", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_all_patches", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "blip_caption_Salesforce_blip_image_captioning_large_intensive", "sequence": "string"}, {"name": "blip_caption_Salesforce_blip_image_captioning_base_intensive", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 511357022.0, "num_examples": 1000}], "download_size": 293191811, "dataset_size": 511357022.0}}
2023-06-08T23:06:10+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_sample_validation" More Information needed
[ "# Dataset Card for \"VQAv2_sample_validation\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_sample_validation\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation\"\n\nMore Information needed" ]
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d8448eca07d1505b6c9da24eddad0b2bc6a24e08
# Dataset Card for "VQAv2_sample_testdev" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/VQAv2_sample_testdev
[ "region:us" ]
2023-02-10T18:00:08+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": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "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": "blip_caption_beam_5", "dtype": "string"}], "splits": [{"name": "testdev", "num_bytes": 159593151.0, "num_examples": 1000}], "download_size": 156894337, "dataset_size": 159593151.0}}
2023-05-14T21:08:48+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_sample_testdev" More Information needed
[ "# Dataset Card for \"VQAv2_sample_testdev\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_sample_testdev\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_testdev\"\n\nMore Information needed" ]
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a03bae0c44445a77a6579d801ca3124fb0d023ca
# Dataset Card for "VQAv2_sample_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/VQAv2_sample_test
[ "region:us" ]
2023-02-10T18:00:19+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": "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": 158160937.0, "num_examples": 1000}], "download_size": 155874093, "dataset_size": 158160937.0}}
2023-02-12T00:03:37+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_sample_test" More Information needed
[ "# Dataset Card for \"VQAv2_sample_test\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_sample_test\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_test\"\n\nMore Information needed" ]
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fa32e5077dd9580d0d9b0102ebdf36f41eb52981
# Dataset Card for "climate_fever_fixed" ### Dataset Summary This dataset was created to aid our team in developing a model to more accurately perform climate change-related fact checking. We approach this task from a perspective heavily impacted by the work of the [ClimateBERT](https://climatebert.ai/about) team. With that in mind, our team likewise leveraged a BERT Language model to solve this task. This dataset presents an edited version of the [Climate_Fever](https://huggingface.co/datasets/climate_fever) dataset, hosted by HuggingFace. Climate_Fever is composed of climate-related documents that have been annotated with labels related to fact-checking and misinformation. However, in the climate-plus project, we decided to modify the dataset to remove redundancy and keep only the essentials of a text-entailment problem: claim as the premise and evidence as the hypothesis. ### Data Fields This dataset contains 7675 records, each of which is composed of several attributes: - `claim_id`: a `integer` feature, which serves as a unique identifier for each record/row. - `claim`: a `string` feature, containes the raw text of a given climate-related claim. - `evidence`: a `string` feature, which provides free text evidence that relates to the previously established claim. - `label`: a `class label` feature representing an assigned class, where values can either be 0: "supports", 1: "refutes" and 2: "not enough info". - `category`: a `string` feature, which provides additional detail about the particular focus of a given claim. <br> This dataset was then broken into train, test and validation sets to enable proper evaluation of our model. These splits contain the following amount of data: - `Train`: 4300 Records - `Test`: 1540 Records - `Val`: 1840 Records ### Source Data This dataset represents an evolved version of the original [Climate_Fever](https://huggingface.co/datasets/climate_fever) dataset, hosted by HuggingFace. It was adapted to meet the needs of our team, as we attempted to solve a specific climate change-related task. The original dataset adopted the FEVER methodology, discussed in more detail [here](https://www.amazon.science/blog/the-fever-data-set-what-doesnt-kill-it-will-make-it-stronger). Their original dataset consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs. ### Methodology This dataset was curated by our team to reduce redundancy and keep only the essentials of a text-entailment problem: claim as the premise and evidence as the hypothesis. For each given claim, there are multiple sentences of evidence. We decided to expand the one-to-many relation to one-to-one. This resulted in a modified version of the climate_fever dataset that includes only one evidence sentence per claim. ### Languages The text contained in the dataset is entirely in English, as found in the real-world financial disclosures identified by the TCFD. The associated BCP-47 code is [`en`](https://www.techonthenet.com/js/language_tags.php), to ensure clear labeling of language usage for downstream tasks and other future applications. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rexarski/climate_fever_fixed
[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:en", "license:mit", "climate", "region:us" ]
2023-02-10T18:01:46+00:00
{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "climate_fever dataset with one-to-one claim-evidence pair", "dataset_info": {"features": [{"name": "claim_id", "dtype": "int64"}, {"name": "claim", "dtype": "string"}, {"name": "evidence", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "SUPPORTS", "1": "REFUTES", "2": "NOT_ENOUGH_INFO"}}}}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1467456, "num_examples": 4298}, {"name": "test", "num_bytes": 526276, "num_examples": 1535}, {"name": "valid", "num_bytes": 635174, "num_examples": 1842}], "download_size": 1372892, "dataset_size": 2628906}, "tags": ["climate"]}
2023-04-30T02:46:52+00:00
[]
[ "en" ]
TAGS #task_categories-text-classification #size_categories-1K<n<10K #language-English #license-mit #climate #region-us
# Dataset Card for "climate_fever_fixed" ### Dataset Summary This dataset was created to aid our team in developing a model to more accurately perform climate change-related fact checking. We approach this task from a perspective heavily impacted by the work of the ClimateBERT team. With that in mind, our team likewise leveraged a BERT Language model to solve this task. This dataset presents an edited version of the Climate_Fever dataset, hosted by HuggingFace. Climate_Fever is composed of climate-related documents that have been annotated with labels related to fact-checking and misinformation. However, in the climate-plus project, we decided to modify the dataset to remove redundancy and keep only the essentials of a text-entailment problem: claim as the premise and evidence as the hypothesis. ### Data Fields This dataset contains 7675 records, each of which is composed of several attributes: - 'claim_id': a 'integer' feature, which serves as a unique identifier for each record/row. - 'claim': a 'string' feature, containes the raw text of a given climate-related claim. - 'evidence': a 'string' feature, which provides free text evidence that relates to the previously established claim. - 'label': a 'class label' feature representing an assigned class, where values can either be 0: "supports", 1: "refutes" and 2: "not enough info". - 'category': a 'string' feature, which provides additional detail about the particular focus of a given claim. <br> This dataset was then broken into train, test and validation sets to enable proper evaluation of our model. These splits contain the following amount of data: - 'Train': 4300 Records - 'Test': 1540 Records - 'Val': 1840 Records ### Source Data This dataset represents an evolved version of the original Climate_Fever dataset, hosted by HuggingFace. It was adapted to meet the needs of our team, as we attempted to solve a specific climate change-related task. The original dataset adopted the FEVER methodology, discussed in more detail here. Their original dataset consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs. ### Methodology This dataset was curated by our team to reduce redundancy and keep only the essentials of a text-entailment problem: claim as the premise and evidence as the hypothesis. For each given claim, there are multiple sentences of evidence. We decided to expand the one-to-many relation to one-to-one. This resulted in a modified version of the climate_fever dataset that includes only one evidence sentence per claim. ### Languages The text contained in the dataset is entirely in English, as found in the real-world financial disclosures identified by the TCFD. The associated BCP-47 code is 'en', to ensure clear labeling of language usage for downstream tasks and other future applications. More Information needed
[ "# Dataset Card for \"climate_fever_fixed\"", "### Dataset Summary\n\nThis dataset was created to aid our team in developing a model to more accurately perform climate change-related fact checking. We approach this task from a perspective heavily impacted \nby the work of the ClimateBERT team. With that in mind, our team likewise leveraged a BERT Language model to solve this task. This dataset presents an \nedited version of the Climate_Fever dataset, hosted by HuggingFace. Climate_Fever is composed of climate-related documents \nthat have been annotated with labels related to fact-checking and misinformation. However, in the climate-plus project, we decided to modify the dataset to remove redundancy\nand keep only the essentials of a text-entailment problem: claim as the premise and evidence as the hypothesis.", "### Data Fields\n\nThis dataset contains 7675 records, each of which is composed of several attributes:\n\n- 'claim_id': a 'integer' feature, which serves as a unique identifier for each record/row. \n- 'claim': a 'string' feature, containes the raw text of a given climate-related claim. \n- 'evidence': a 'string' feature, which provides free text evidence that relates to the previously established claim.\n- 'label': a 'class label' feature representing an assigned class, where values can either be 0: \"supports\", 1: \"refutes\" and 2: \"not enough info\".\n- 'category': a 'string' feature, which provides additional detail about the particular focus of a given claim.\n\n<br>\nThis dataset was then broken into train, test and validation sets to enable proper evaluation of our model. These splits contain the following amount of data:\n\n- 'Train': 4300 Records\n- 'Test': 1540 Records\n- 'Val': 1840 Records", "### Source Data\n\nThis dataset represents an evolved version of the original Climate_Fever dataset, hosted by HuggingFace. It was adapted to meet\nthe needs of our team, as we attempted to solve a specific climate change-related task. The original dataset adopted the FEVER methodology, discussed in more detail here.\nTheir original dataset consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence \nsentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs.", "### Methodology\n\nThis dataset was curated by our team to reduce redundancy and keep only the essentials of a text-entailment problem: claim as the premise and evidence as the hypothesis. \nFor each given claim, there are multiple sentences of evidence. We decided to expand the one-to-many relation to one-to-one. \nThis resulted in a modified version of the climate_fever dataset that includes only one evidence sentence per claim.", "### Languages\n\nThe text contained in the dataset is entirely in English, as found in the real-world financial disclosures identified by the TCFD. The associated BCP-47 code is 'en', to ensure clear labeling of language usage for downstream tasks and other future applications.\n\nMore Information needed" ]
[ "TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-mit #climate #region-us \n", "# Dataset Card for \"climate_fever_fixed\"", "### Dataset Summary\n\nThis dataset was created to aid our team in developing a model to more accurately perform climate change-related fact checking. We approach this task from a perspective heavily impacted \nby the work of the ClimateBERT team. With that in mind, our team likewise leveraged a BERT Language model to solve this task. This dataset presents an \nedited version of the Climate_Fever dataset, hosted by HuggingFace. Climate_Fever is composed of climate-related documents \nthat have been annotated with labels related to fact-checking and misinformation. However, in the climate-plus project, we decided to modify the dataset to remove redundancy\nand keep only the essentials of a text-entailment problem: claim as the premise and evidence as the hypothesis.", "### Data Fields\n\nThis dataset contains 7675 records, each of which is composed of several attributes:\n\n- 'claim_id': a 'integer' feature, which serves as a unique identifier for each record/row. \n- 'claim': a 'string' feature, containes the raw text of a given climate-related claim. \n- 'evidence': a 'string' feature, which provides free text evidence that relates to the previously established claim.\n- 'label': a 'class label' feature representing an assigned class, where values can either be 0: \"supports\", 1: \"refutes\" and 2: \"not enough info\".\n- 'category': a 'string' feature, which provides additional detail about the particular focus of a given claim.\n\n<br>\nThis dataset was then broken into train, test and validation sets to enable proper evaluation of our model. These splits contain the following amount of data:\n\n- 'Train': 4300 Records\n- 'Test': 1540 Records\n- 'Val': 1840 Records", "### Source Data\n\nThis dataset represents an evolved version of the original Climate_Fever dataset, hosted by HuggingFace. It was adapted to meet\nthe needs of our team, as we attempted to solve a specific climate change-related task. The original dataset adopted the FEVER methodology, discussed in more detail here.\nTheir original dataset consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence \nsentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs.", "### Methodology\n\nThis dataset was curated by our team to reduce redundancy and keep only the essentials of a text-entailment problem: claim as the premise and evidence as the hypothesis. \nFor each given claim, there are multiple sentences of evidence. We decided to expand the one-to-many relation to one-to-one. \nThis resulted in a modified version of the climate_fever dataset that includes only one evidence sentence per claim.", "### Languages\n\nThe text contained in the dataset is entirely in English, as found in the real-world financial disclosures identified by the TCFD. The associated BCP-47 code is 'en', to ensure clear labeling of language usage for downstream tasks and other future applications.\n\nMore Information needed" ]
[ 42, 16, 184, 234, 155, 102, 66 ]
[ "passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-mit #climate #region-us \n# Dataset Card for \"climate_fever_fixed\"### Dataset Summary\n\nThis dataset was created to aid our team in developing a model to more accurately perform climate change-related fact checking. We approach this task from a perspective heavily impacted \nby the work of the ClimateBERT team. With that in mind, our team likewise leveraged a BERT Language model to solve this task. This dataset presents an \nedited version of the Climate_Fever dataset, hosted by HuggingFace. Climate_Fever is composed of climate-related documents \nthat have been annotated with labels related to fact-checking and misinformation. However, in the climate-plus project, we decided to modify the dataset to remove redundancy\nand keep only the essentials of a text-entailment problem: claim as the premise and evidence as the hypothesis.### Data Fields\n\nThis dataset contains 7675 records, each of which is composed of several attributes:\n\n- 'claim_id': a 'integer' feature, which serves as a unique identifier for each record/row. \n- 'claim': a 'string' feature, containes the raw text of a given climate-related claim. \n- 'evidence': a 'string' feature, which provides free text evidence that relates to the previously established claim.\n- 'label': a 'class label' feature representing an assigned class, where values can either be 0: \"supports\", 1: \"refutes\" and 2: \"not enough info\".\n- 'category': a 'string' feature, which provides additional detail about the particular focus of a given claim.\n\n<br>\nThis dataset was then broken into train, test and validation sets to enable proper evaluation of our model. These splits contain the following amount of data:\n\n- 'Train': 4300 Records\n- 'Test': 1540 Records\n- 'Val': 1840 Records" ]
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12d4b26f6ae25ce7ca6639c458bbc02e7f552fdc
# Dataset Card for "Imagenet1k_sample_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Imagenet1k_sample_train
[ "region:us" ]
2023-02-10T18:05:04+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "tench, Tinca tinca", "1": "goldfish, Carassius auratus", "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3": "tiger shark, Galeocerdo cuvieri", "4": "hammerhead, hammerhead shark", "5": "electric ray, crampfish, numbfish, torpedo", "6": "stingray", "7": "cock", "8": "hen", "9": "ostrich, Struthio camelus", "10": "brambling, Fringilla montifringilla", "11": "goldfinch, Carduelis carduelis", "12": "house finch, linnet, Carpodacus mexicanus", "13": "junco, snowbird", "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15": "robin, American robin, Turdus migratorius", "16": "bulbul", "17": "jay", "18": "magpie", "19": "chickadee", "20": "water ouzel, dipper", "21": "kite", "22": "bald eagle, American eagle, Haliaeetus leucocephalus", "23": "vulture", "24": "great grey owl, great gray owl, Strix nebulosa", "25": "European fire salamander, Salamandra salamandra", "26": "common newt, Triturus vulgaris", "27": "eft", "28": "spotted salamander, Ambystoma maculatum", "29": "axolotl, mud puppy, Ambystoma mexicanum", "30": "bullfrog, Rana catesbeiana", "31": "tree frog, tree-frog", "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33": "loggerhead, loggerhead turtle, Caretta caretta", "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35": "mud turtle", "36": "terrapin", "37": "box turtle, box tortoise", "38": "banded gecko", "39": "common iguana, iguana, Iguana iguana", "40": "American chameleon, anole, Anolis carolinensis", "41": "whiptail, whiptail lizard", "42": "agama", "43": "frilled lizard, Chlamydosaurus kingi", "44": "alligator lizard", "45": "Gila monster, Heloderma suspectum", "46": "green lizard, Lacerta viridis", "47": "African chameleon, Chamaeleo chamaeleon", "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49": "African crocodile, Nile crocodile, Crocodylus niloticus", "50": "American alligator, Alligator mississipiensis", "51": "triceratops", "52": "thunder snake, worm snake, Carphophis amoenus", "53": "ringneck snake, ring-necked snake, ring snake", "54": "hognose snake, puff adder, sand viper", "55": "green snake, grass snake", "56": "king snake, kingsnake", "57": "garter snake, grass snake", "58": "water snake", "59": "vine snake", "60": "night snake, Hypsiglena torquata", "61": "boa constrictor, Constrictor constrictor", "62": "rock python, rock snake, Python sebae", "63": "Indian cobra, Naja naja", "64": "green mamba", "65": "sea snake", "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", "68": "sidewinder, horned rattlesnake, Crotalus cerastes", "69": "trilobite", "70": "harvestman, daddy longlegs, Phalangium opilio", "71": "scorpion", "72": "black and gold garden spider, Argiope aurantia", "73": "barn spider, Araneus cavaticus", "74": "garden spider, Aranea diademata", "75": "black widow, Latrodectus mactans", "76": "tarantula", "77": "wolf spider, hunting spider", "78": "tick", "79": "centipede", "80": "black grouse", "81": "ptarmigan", "82": "ruffed grouse, partridge, Bonasa umbellus", "83": "prairie chicken, prairie grouse, prairie fowl", "84": "peacock", "85": "quail", "86": "partridge", "87": "African grey, African gray, Psittacus erithacus", "88": "macaw", "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90": "lorikeet", "91": "coucal", "92": "bee eater", "93": "hornbill", "94": "hummingbird", "95": "jacamar", "96": "toucan", "97": "drake", "98": "red-breasted merganser, Mergus serrator", "99": "goose", "100": "black swan, Cygnus atratus", "101": "tusker", "102": "echidna, spiny anteater, anteater", "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104": "wallaby, brush kangaroo", "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106": "wombat", "107": "jellyfish", "108": "sea anemone, anemone", "109": "brain coral", "110": "flatworm, platyhelminth", "111": "nematode, nematode worm, roundworm", "112": "conch", "113": "snail", "114": "slug", "115": "sea slug, nudibranch", "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", "117": "chambered nautilus, pearly nautilus, nautilus", "118": "Dungeness crab, Cancer magister", "119": "rock crab, Cancer irroratus", "120": "fiddler crab", "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124": "crayfish, crawfish, crawdad, crawdaddy", "125": "hermit crab", "126": "isopod", "127": "white stork, Ciconia ciconia", "128": "black stork, Ciconia nigra", "129": "spoonbill", "130": "flamingo", "131": "little blue heron, Egretta caerulea", "132": "American egret, great white heron, Egretta albus", "133": "bittern", "134": "crane", "135": "limpkin, Aramus pictus", "136": "European gallinule, Porphyrio porphyrio", "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", "138": "bustard", "139": "ruddy turnstone, Arenaria interpres", "140": "red-backed sandpiper, dunlin, Erolia alpina", "141": "redshank, Tringa totanus", "142": "dowitcher", "143": "oystercatcher, oyster catcher", "144": "pelican", "145": "king penguin, Aptenodytes patagonica", "146": "albatross, mollymawk", "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149": "dugong, Dugong dugon", "150": "sea lion", "151": "Chihuahua", "152": "Japanese spaniel", "153": "Maltese dog, Maltese terrier, Maltese", "154": "Pekinese, Pekingese, Peke", "155": "Shih-Tzu", "156": "Blenheim spaniel", "157": "papillon", "158": "toy terrier", "159": "Rhodesian ridgeback", "160": "Afghan hound, Afghan", "161": "basset, basset hound", "162": "beagle", "163": "bloodhound, sleuthhound", "164": "bluetick", "165": "black-and-tan coonhound", "166": "Walker hound, Walker foxhound", "167": "English foxhound", "168": "redbone", "169": "borzoi, Russian wolfhound", "170": "Irish wolfhound", "171": "Italian greyhound", "172": "whippet", "173": "Ibizan hound, Ibizan Podenco", "174": "Norwegian elkhound, elkhound", "175": "otterhound, otter hound", "176": "Saluki, gazelle hound", "177": "Scottish deerhound, deerhound", "178": "Weimaraner", "179": "Staffordshire bullterrier, Staffordshire bull terrier", "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181": "Bedlington terrier", "182": "Border terrier", "183": "Kerry blue terrier", "184": "Irish terrier", "185": "Norfolk terrier", "186": "Norwich terrier", "187": "Yorkshire terrier", "188": "wire-haired fox terrier", "189": "Lakeland terrier", "190": "Sealyham terrier, Sealyham", "191": "Airedale, Airedale terrier", "192": "cairn, cairn terrier", "193": "Australian terrier", "194": "Dandie Dinmont, Dandie Dinmont terrier", "195": "Boston bull, Boston terrier", "196": "miniature schnauzer", "197": "giant schnauzer", "198": "standard schnauzer", "199": "Scotch terrier, Scottish terrier, Scottie", "200": "Tibetan terrier, chrysanthemum dog", "201": "silky terrier, Sydney silky", "202": "soft-coated wheaten terrier", "203": "West Highland white terrier", "204": "Lhasa, Lhasa apso", "205": "flat-coated retriever", "206": "curly-coated retriever", "207": "golden retriever", "208": "Labrador retriever", "209": "Chesapeake Bay retriever", "210": "German short-haired pointer", "211": "vizsla, Hungarian pointer", "212": "English setter", "213": "Irish setter, red setter", "214": "Gordon setter", "215": "Brittany spaniel", "216": "clumber, clumber spaniel", "217": "English springer, English springer spaniel", "218": "Welsh springer spaniel", "219": "cocker spaniel, English cocker spaniel, cocker", "220": "Sussex spaniel", "221": "Irish water spaniel", "222": "kuvasz", "223": "schipperke", "224": "groenendael", "225": "malinois", "226": "briard", "227": "kelpie", "228": "komondor", "229": "Old English sheepdog, bobtail", "230": "Shetland sheepdog, Shetland sheep dog, Shetland", "231": "collie", "232": "Border collie", "233": "Bouvier des Flandres, Bouviers des Flandres", "234": "Rottweiler", "235": "German shepherd, German shepherd dog, German police dog, alsatian", "236": "Doberman, Doberman pinscher", "237": "miniature pinscher", "238": "Greater Swiss Mountain dog", "239": "Bernese mountain dog", "240": "Appenzeller", "241": "EntleBucher", "242": "boxer", "243": "bull mastiff", "244": "Tibetan mastiff", "245": "French bulldog", "246": "Great Dane", "247": "Saint Bernard, St Bernard", "248": "Eskimo dog, husky", "249": "malamute, malemute, Alaskan malamute", "250": "Siberian husky", "251": "dalmatian, coach dog, carriage dog", "252": "affenpinscher, monkey pinscher, monkey dog", "253": "basenji", "254": "pug, pug-dog", "255": "Leonberg", "256": "Newfoundland, Newfoundland dog", "257": "Great Pyrenees", "258": "Samoyed, Samoyede", "259": "Pomeranian", "260": "chow, chow chow", "261": "keeshond", "262": "Brabancon griffon", "263": "Pembroke, Pembroke Welsh corgi", "264": "Cardigan, Cardigan Welsh corgi", "265": "toy poodle", "266": "miniature poodle", "267": "standard poodle", "268": "Mexican hairless", "269": "timber wolf, grey wolf, gray wolf, Canis lupus", "270": "white wolf, Arctic wolf, Canis lupus tundrarum", "271": "red wolf, maned wolf, Canis rufus, Canis niger", "272": "coyote, prairie wolf, brush wolf, Canis latrans", "273": "dingo, warrigal, warragal, Canis dingo", "274": "dhole, Cuon alpinus", "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276": "hyena, hyaena", "277": "red fox, Vulpes vulpes", "278": "kit fox, Vulpes macrotis", "279": "Arctic fox, white fox, Alopex lagopus", "280": "grey fox, gray fox, Urocyon cinereoargenteus", "281": "tabby, tabby cat", "282": "tiger cat", "283": "Persian cat", "284": "Siamese cat, Siamese", "285": "Egyptian cat", "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287": "lynx, catamount", "288": "leopard, Panthera pardus", "289": "snow leopard, ounce, Panthera uncia", "290": "jaguar, panther, Panthera onca, Felis onca", "291": "lion, king of beasts, Panthera leo", "292": "tiger, Panthera tigris", "293": "cheetah, chetah, Acinonyx jubatus", "294": "brown bear, bruin, Ursus arctos", "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297": "sloth bear, Melursus ursinus, Ursus ursinus", "298": "mongoose", "299": "meerkat, mierkat", "300": "tiger beetle", "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302": "ground beetle, carabid beetle", "303": "long-horned beetle, longicorn, longicorn beetle", "304": "leaf beetle, chrysomelid", "305": "dung beetle", "306": "rhinoceros beetle", "307": "weevil", "308": "fly", "309": "bee", "310": "ant, emmet, pismire", "311": "grasshopper, hopper", "312": "cricket", "313": "walking stick, walkingstick, stick insect", "314": "cockroach, roach", "315": "mantis, mantid", "316": "cicada, cicala", "317": "leafhopper", "318": "lacewing, lacewing fly", "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "320": "damselfly", "321": "admiral", "322": "ringlet, ringlet butterfly", "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324": "cabbage butterfly", "325": "sulphur butterfly, sulfur butterfly", "326": "lycaenid, lycaenid butterfly", "327": "starfish, sea star", "328": "sea urchin", "329": "sea cucumber, holothurian", "330": "wood rabbit, cottontail, cottontail rabbit", "331": "hare", "332": "Angora, Angora rabbit", "333": "hamster", "334": "porcupine, hedgehog", "335": "fox squirrel, eastern fox squirrel, Sciurus niger", "336": "marmot", "337": "beaver", "338": "guinea pig, Cavia cobaya", "339": "sorrel", "340": "zebra", "341": "hog, pig, grunter, squealer, Sus scrofa", "342": "wild boar, boar, Sus scrofa", "343": "warthog", "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", "345": "ox", "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347": "bison", "348": "ram, tup", "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350": "ibex, Capra ibex", "351": "hartebeest", "352": "impala, Aepyceros melampus", "353": "gazelle", "354": "Arabian camel, dromedary, Camelus dromedarius", "355": "llama", "356": "weasel", "357": "mink", "358": "polecat, fitch, foulmart, foumart, Mustela putorius", "359": "black-footed ferret, ferret, Mustela nigripes", "360": "otter", "361": "skunk, polecat, wood pussy", "362": "badger", "363": "armadillo", "364": "three-toed sloth, ai, Bradypus tridactylus", "365": "orangutan, orang, orangutang, Pongo pygmaeus", "366": "gorilla, Gorilla gorilla", "367": "chimpanzee, chimp, Pan troglodytes", "368": "gibbon, Hylobates lar", "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", "370": "guenon, guenon monkey", "371": "patas, hussar monkey, Erythrocebus patas", "372": "baboon", "373": "macaque", "374": "langur", "375": "colobus, colobus monkey", "376": "proboscis monkey, Nasalis larvatus", "377": "marmoset", "378": "capuchin, ringtail, Cebus capucinus", "379": "howler monkey, howler", "380": "titi, titi monkey", "381": "spider monkey, Ateles geoffroyi", "382": "squirrel monkey, Saimiri sciureus", "383": "Madagascar cat, ring-tailed lemur, Lemur catta", "384": "indri, indris, Indri indri, Indri brevicaudatus", "385": "Indian elephant, Elephas maximus", "386": "African elephant, Loxodonta africana", "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389": "barracouta, snoek", "390": "eel", "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392": "rock beauty, Holocanthus tricolor", "393": "anemone fish", "394": "sturgeon", "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", "396": "lionfish", "397": "puffer, pufferfish, blowfish, globefish", "398": "abacus", "399": "abaya", "400": "academic gown, academic robe, judge's robe", "401": "accordion, piano accordion, squeeze box", "402": "acoustic guitar", "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", "404": "airliner", "405": "airship, dirigible", "406": "altar", "407": "ambulance", "408": "amphibian, amphibious vehicle", "409": "analog clock", "410": "apiary, bee house", "411": "apron", "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413": "assault rifle, assault gun", "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", "415": "bakery, bakeshop, bakehouse", "416": "balance beam, beam", "417": "balloon", "418": "ballpoint, ballpoint pen, ballpen, Biro", "419": "Band Aid", "420": "banjo", "421": "bannister, banister, balustrade, balusters, handrail", "422": "barbell", "423": "barber chair", "424": "barbershop", "425": "barn", "426": "barometer", "427": "barrel, cask", "428": "barrow, garden cart, lawn cart, wheelbarrow", "429": "baseball", "430": "basketball", "431": "bassinet", "432": "bassoon", "433": "bathing cap, swimming cap", "434": "bath towel", "435": "bathtub, bathing tub, bath, tub", "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437": "beacon, lighthouse, beacon light, pharos", "438": "beaker", "439": "bearskin, busby, shako", "440": "beer bottle", "441": "beer glass", "442": "bell cote, bell cot", "443": "bib", "444": "bicycle-built-for-two, tandem bicycle, tandem", "445": "bikini, two-piece", "446": "binder, ring-binder", "447": "binoculars, field glasses, opera glasses", "448": "birdhouse", "449": "boathouse", "450": "bobsled, bobsleigh, bob", "451": "bolo tie, bolo, bola tie, bola", "452": "bonnet, poke bonnet", "453": "bookcase", "454": "bookshop, bookstore, bookstall", "455": "bottlecap", "456": "bow", "457": "bow tie, bow-tie, bowtie", "458": "brass, memorial tablet, plaque", "459": "brassiere, bra, bandeau", "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461": "breastplate, aegis, egis", "462": "broom", "463": "bucket, pail", "464": "buckle", "465": "bulletproof vest", "466": "bullet train, bullet", "467": "butcher shop, meat market", "468": "cab, hack, taxi, taxicab", "469": "caldron, cauldron", "470": "candle, taper, wax light", "471": "cannon", "472": "canoe", "473": "can opener, tin opener", "474": "cardigan", "475": "car mirror", "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", "477": "carpenter's kit, tool kit", "478": "carton", "479": "car wheel", "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481": "cassette", "482": "cassette player", "483": "castle", "484": "catamaran", "485": "CD player", "486": "cello, violoncello", "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", "488": "chain", "489": "chainlink fence", "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491": "chain saw, chainsaw", "492": "chest", "493": "chiffonier, commode", "494": "chime, bell, gong", "495": "china cabinet, china closet", "496": "Christmas stocking", "497": "church, church building", "498": "cinema, movie theater, movie theatre, movie house, picture palace", "499": "cleaver, meat cleaver, chopper", "500": "cliff dwelling", "501": "cloak", "502": "clog, geta, patten, sabot", "503": "cocktail shaker", "504": "coffee mug", "505": "coffeepot", "506": "coil, spiral, volute, whorl, helix", "507": "combination lock", "508": "computer keyboard, keypad", "509": "confectionery, confectionary, candy store", "510": "container ship, containership, container vessel", "511": "convertible", "512": "corkscrew, bottle screw", "513": "cornet, horn, trumpet, trump", "514": "cowboy boot", "515": "cowboy hat, ten-gallon hat", "516": "cradle", "517": "crane2", "518": "crash helmet", "519": "crate", "520": "crib, cot", "521": "Crock Pot", "522": "croquet ball", "523": "crutch", "524": "cuirass", "525": "dam, dike, dyke", "526": "desk", "527": "desktop computer", "528": "dial telephone, dial phone", "529": "diaper, nappy, napkin", "530": "digital clock", "531": "digital watch", "532": "dining table, board", "533": "dishrag, dishcloth", "534": "dishwasher, dish washer, dishwashing machine", "535": "disk brake, disc brake", "536": "dock, dockage, docking facility", "537": "dogsled, dog sled, dog sleigh", "538": "dome", "539": "doormat, welcome mat", "540": "drilling platform, offshore rig", "541": "drum, membranophone, tympan", "542": "drumstick", "543": "dumbbell", "544": "Dutch oven", "545": "electric fan, blower", "546": "electric guitar", "547": "electric locomotive", "548": "entertainment center", "549": "envelope", "550": "espresso maker", "551": "face powder", "552": "feather boa, boa", "553": "file, file cabinet, filing cabinet", "554": "fireboat", "555": "fire engine, fire truck", "556": "fire screen, fireguard", "557": "flagpole, flagstaff", "558": "flute, transverse flute", "559": "folding chair", "560": "football helmet", "561": "forklift", "562": "fountain", "563": "fountain pen", "564": "four-poster", "565": "freight car", "566": "French horn, horn", "567": "frying pan, frypan, skillet", "568": "fur coat", "569": "garbage truck, dustcart", "570": "gasmask, respirator, gas helmet", "571": "gas pump, gasoline pump, petrol pump, island dispenser", "572": "goblet", "573": "go-kart", "574": "golf ball", "575": "golfcart, golf cart", "576": "gondola", "577": "gong, tam-tam", "578": "gown", "579": "grand piano, grand", "580": "greenhouse, nursery, glasshouse", "581": "grille, radiator grille", "582": "grocery store, grocery, food market, market", "583": "guillotine", "584": "hair slide", "585": "hair spray", "586": "half track", "587": "hammer", "588": "hamper", "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", "590": "hand-held computer, hand-held microcomputer", "591": "handkerchief, hankie, hanky, hankey", "592": "hard disc, hard disk, fixed disk", "593": "harmonica, mouth organ, harp, mouth harp", "594": "harp", "595": "harvester, reaper", "596": "hatchet", "597": "holster", "598": "home theater, home theatre", "599": "honeycomb", "600": "hook, claw", "601": "hoopskirt, crinoline", "602": "horizontal bar, high bar", "603": "horse cart, horse-cart", "604": "hourglass", "605": "iPod", "606": "iron, smoothing iron", "607": "jack-o'-lantern", "608": "jean, blue jean, denim", "609": "jeep, landrover", "610": "jersey, T-shirt, tee shirt", "611": "jigsaw puzzle", "612": "jinrikisha, ricksha, rickshaw", "613": "joystick", "614": "kimono", "615": "knee pad", "616": "knot", "617": "lab coat, laboratory coat", "618": "ladle", "619": "lampshade, lamp shade", "620": "laptop, laptop computer", "621": "lawn mower, mower", "622": "lens cap, lens cover", "623": "letter opener, paper knife, paperknife", "624": "library", "625": "lifeboat", "626": "lighter, light, igniter, ignitor", "627": "limousine, limo", "628": "liner, ocean liner", "629": "lipstick, lip rouge", "630": "Loafer", "631": "lotion", "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633": "loupe, jeweler's loupe", "634": "lumbermill, sawmill", "635": "magnetic compass", "636": "mailbag, postbag", "637": "mailbox, letter box", "638": "maillot", "639": "maillot, tank suit", "640": "manhole cover", "641": "maraca", "642": "marimba, xylophone", "643": "mask", "644": "matchstick", "645": "maypole", "646": "maze, labyrinth", "647": "measuring cup", "648": "medicine chest, medicine cabinet", "649": "megalith, megalithic structure", "650": "microphone, mike", "651": "microwave, microwave oven", "652": "military uniform", "653": "milk can", "654": "minibus", "655": "miniskirt, mini", "656": "minivan", "657": "missile", "658": "mitten", "659": "mixing bowl", "660": "mobile home, manufactured home", "661": "Model T", "662": "modem", "663": "monastery", "664": "monitor", "665": "moped", "666": "mortar", "667": "mortarboard", "668": "mosque", "669": "mosquito net", "670": "motor scooter, scooter", "671": "mountain bike, all-terrain bike, off-roader", "672": "mountain tent", "673": "mouse, computer mouse", "674": "mousetrap", "675": "moving van", "676": "muzzle", "677": "nail", "678": "neck brace", "679": "necklace", "680": "nipple", "681": "notebook, notebook computer", "682": "obelisk", "683": "oboe, hautboy, hautbois", "684": "ocarina, sweet potato", "685": "odometer, hodometer, mileometer, milometer", "686": "oil filter", "687": "organ, pipe organ", "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", "689": "overskirt", "690": "oxcart", "691": "oxygen mask", "692": "packet", "693": "paddle, boat paddle", "694": "paddlewheel, paddle wheel", "695": "padlock", "696": "paintbrush", "697": "pajama, pyjama, pj's, jammies", "698": "palace", "699": "panpipe, pandean pipe, syrinx", "700": "paper towel", "701": "parachute, chute", "702": "parallel bars, bars", "703": "park bench", "704": "parking meter", "705": "passenger car, coach, carriage", "706": "patio, terrace", "707": "pay-phone, pay-station", "708": "pedestal, plinth, footstall", "709": "pencil box, pencil case", "710": "pencil sharpener", "711": "perfume, essence", "712": "Petri dish", "713": "photocopier", "714": "pick, plectrum, plectron", "715": "pickelhaube", "716": "picket fence, paling", "717": "pickup, pickup truck", "718": "pier", "719": "piggy bank, penny bank", "720": "pill bottle", "721": "pillow", "722": "ping-pong ball", "723": "pinwheel", "724": "pirate, pirate ship", "725": "pitcher, ewer", "726": "plane, carpenter's plane, woodworking plane", "727": "planetarium", "728": "plastic bag", "729": "plate rack", "730": "plow, plough", "731": "plunger, plumber's helper", "732": "Polaroid camera, Polaroid Land camera", "733": "pole", "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735": "poncho", "736": "pool table, billiard table, snooker table", "737": "pop bottle, soda bottle", "738": "pot, flowerpot", "739": "potter's wheel", "740": "power drill", "741": "prayer rug, prayer mat", "742": "printer", "743": "prison, prison house", "744": "projectile, missile", "745": "projector", "746": "puck, hockey puck", "747": "punching bag, punch bag, punching ball, punchball", "748": "purse", "749": "quill, quill pen", "750": "quilt, comforter, comfort, puff", "751": "racer, race car, racing car", "752": "racket, racquet", "753": "radiator", "754": "radio, wireless", "755": "radio telescope, radio reflector", "756": "rain barrel", "757": "recreational vehicle, RV, R.V.", "758": "reel", "759": "reflex camera", "760": "refrigerator, icebox", "761": "remote control, remote", "762": "restaurant, eating house, eating place, eatery", "763": "revolver, six-gun, six-shooter", "764": "rifle", "765": "rocking chair, rocker", "766": "rotisserie", "767": "rubber eraser, rubber, pencil eraser", "768": "rugby ball", "769": "rule, ruler", "770": "running shoe", "771": "safe", "772": "safety pin", "773": "saltshaker, salt shaker", "774": "sandal", "775": "sarong", "776": "sax, saxophone", "777": "scabbard", "778": "scale, weighing machine", "779": "school bus", "780": "schooner", "781": "scoreboard", "782": "screen, CRT screen", "783": "screw", "784": "screwdriver", "785": "seat belt, seatbelt", "786": "sewing machine", "787": "shield, buckler", "788": "shoe shop, shoe-shop, shoe store", "789": "shoji", "790": "shopping basket", "791": "shopping cart", "792": "shovel", "793": "shower cap", "794": "shower curtain", "795": "ski", "796": "ski mask", "797": "sleeping bag", "798": "slide rule, slipstick", "799": "sliding door", "800": "slot, one-armed bandit", "801": "snorkel", "802": "snowmobile", "803": "snowplow, snowplough", "804": "soap dispenser", "805": "soccer ball", "806": "sock", "807": "solar dish, solar collector, solar furnace", "808": "sombrero", "809": "soup bowl", "810": "space bar", "811": "space heater", "812": "space shuttle", "813": "spatula", "814": "speedboat", "815": "spider web, spider's web", "816": "spindle", "817": "sports car, sport car", "818": "spotlight, spot", "819": "stage", "820": "steam locomotive", "821": "steel arch bridge", "822": "steel drum", "823": "stethoscope", "824": "stole", "825": "stone wall", "826": "stopwatch, stop watch", "827": "stove", "828": "strainer", "829": "streetcar, tram, tramcar, trolley, trolley car", "830": "stretcher", "831": "studio couch, day bed", "832": "stupa, tope", "833": "submarine, pigboat, sub, U-boat", "834": "suit, suit of clothes", "835": "sundial", "836": "sunglass", "837": "sunglasses, dark glasses, shades", "838": "sunscreen, sunblock, sun blocker", "839": "suspension bridge", "840": "swab, swob, mop", "841": "sweatshirt", "842": "swimming trunks, bathing trunks", "843": "swing", "844": "switch, electric switch, electrical switch", "845": "syringe", "846": "table lamp", "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", "848": "tape player", "849": "teapot", "850": "teddy, teddy bear", "851": "television, television system", "852": "tennis ball", "853": "thatch, thatched roof", "854": "theater curtain, theatre curtain", "855": "thimble", "856": "thresher, thrasher, threshing machine", "857": "throne", "858": "tile roof", "859": "toaster", "860": "tobacco shop, tobacconist shop, tobacconist", "861": "toilet seat", "862": "torch", "863": "totem pole", "864": "tow truck, tow car, wrecker", "865": "toyshop", "866": "tractor", "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868": "tray", "869": "trench coat", "870": "tricycle, trike, velocipede", "871": "trimaran", "872": "tripod", "873": "triumphal arch", "874": "trolleybus, trolley coach, trackless trolley", "875": "trombone", "876": "tub, vat", "877": "turnstile", "878": "typewriter keyboard", "879": "umbrella", "880": "unicycle, monocycle", "881": "upright, upright piano", "882": "vacuum, vacuum cleaner", "883": "vase", "884": "vault", "885": "velvet", "886": "vending machine", "887": "vestment", "888": "viaduct", "889": "violin, fiddle", "890": "volleyball", "891": "waffle iron", "892": "wall clock", "893": "wallet, billfold, notecase, pocketbook", "894": "wardrobe, closet, press", "895": "warplane, military plane", "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897": "washer, automatic washer, washing machine", "898": "water bottle", "899": "water jug", "900": "water tower", "901": "whiskey jug", "902": "whistle", "903": "wig", "904": "window screen", "905": "window shade", "906": "Windsor tie", "907": "wine bottle", "908": "wing", "909": "wok", "910": "wooden spoon", "911": "wool, woolen, woollen", "912": "worm fence, snake fence, snake-rail fence, Virginia fence", "913": "wreck", "914": "yawl", "915": "yurt", "916": "web site, website, internet site, site", "917": "comic book", "918": "crossword puzzle, crossword", "919": "street sign", "920": "traffic light, traffic signal, stoplight", "921": "book jacket, dust cover, dust jacket, dust wrapper", "922": "menu", "923": "plate", "924": "guacamole", "925": "consomme", "926": "hot pot, hotpot", "927": "trifle", "928": "ice cream, icecream", "929": "ice lolly, lolly, lollipop, popsicle", "930": "French loaf", "931": "bagel, beigel", "932": "pretzel", "933": "cheeseburger", "934": "hotdog, hot dog, red hot", "935": "mashed potato", "936": "head cabbage", "937": "broccoli", "938": "cauliflower", "939": "zucchini, courgette", "940": "spaghetti squash", "941": "acorn squash", "942": "butternut squash", "943": "cucumber, cuke", "944": "artichoke, globe artichoke", "945": "bell pepper", "946": "cardoon", "947": "mushroom", "948": "Granny Smith", "949": "strawberry", "950": "orange", "951": "lemon", "952": "fig", "953": "pineapple, ananas", "954": "banana", "955": "jackfruit, jak, jack", "956": "custard apple", "957": "pomegranate", "958": "hay", "959": "carbonara", "960": "chocolate sauce, chocolate syrup", "961": "dough", "962": "meat loaf, meatloaf", "963": "pizza, pizza pie", "964": "potpie", "965": "burrito", "966": "red wine", "967": "espresso", "968": "cup", "969": "eggnog", "970": "alp", "971": "bubble", "972": "cliff, drop, drop-off", "973": "coral reef", "974": "geyser", "975": "lakeside, lakeshore", "976": "promontory, headland, head, foreland", "977": "sandbar, sand bar", "978": "seashore, coast, seacoast, sea-coast", "979": "valley, vale", "980": "volcano", "981": "ballplayer, baseball player", "982": "groom, bridegroom", "983": "scuba diver", "984": "rapeseed", "985": "daisy", "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987": "corn", "988": "acorn", "989": "hip, rose hip, rosehip", "990": "buckeye, horse chestnut, conker", "991": "coral fungus", "992": "agaric", "993": "gyromitra", "994": "stinkhorn, carrion fungus", "995": "earthstar", "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997": "bolete", "998": "ear, spike, capitulum", "999": "toilet tissue, toilet paper, bathroom tissue"}}}}, {"name": "lexicon", "sequence": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 349126026.0, "num_examples": 3000}], "download_size": 340943693, "dataset_size": 349126026.0}}
2023-02-10T18:05:32+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Imagenet1k_sample_train" More Information needed
[ "# Dataset Card for \"Imagenet1k_sample_train\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Imagenet1k_sample_train\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Imagenet1k_sample_train\"\n\nMore Information needed" ]
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4fdd6d26a7b3f2466aa7e12dd9de3bfa4dd69a82
# Dataset Card for "Imagenet1k_sample_validation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Imagenet1k_sample_validation
[ "region:us" ]
2023-02-10T18:05:33+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "tench, Tinca tinca", "1": "goldfish, Carassius auratus", "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3": "tiger shark, Galeocerdo cuvieri", "4": "hammerhead, hammerhead shark", "5": "electric ray, crampfish, numbfish, torpedo", "6": "stingray", "7": "cock", "8": "hen", "9": "ostrich, Struthio camelus", "10": "brambling, Fringilla montifringilla", "11": "goldfinch, Carduelis carduelis", "12": "house finch, linnet, Carpodacus mexicanus", "13": "junco, snowbird", "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15": "robin, American robin, Turdus migratorius", "16": "bulbul", "17": "jay", "18": "magpie", "19": "chickadee", "20": "water ouzel, dipper", "21": "kite", "22": "bald eagle, American eagle, Haliaeetus leucocephalus", "23": "vulture", "24": "great grey owl, great gray owl, Strix nebulosa", "25": "European fire salamander, Salamandra salamandra", "26": "common newt, Triturus vulgaris", "27": "eft", "28": "spotted salamander, Ambystoma maculatum", "29": "axolotl, mud puppy, Ambystoma mexicanum", "30": "bullfrog, Rana catesbeiana", "31": "tree frog, tree-frog", "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33": "loggerhead, loggerhead turtle, Caretta caretta", "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35": "mud turtle", "36": "terrapin", "37": "box turtle, box tortoise", "38": "banded gecko", "39": "common iguana, iguana, Iguana iguana", "40": "American chameleon, anole, Anolis carolinensis", "41": "whiptail, whiptail lizard", "42": "agama", "43": "frilled lizard, Chlamydosaurus kingi", "44": "alligator lizard", "45": "Gila monster, Heloderma suspectum", "46": "green lizard, Lacerta viridis", "47": "African chameleon, Chamaeleo chamaeleon", "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49": "African crocodile, Nile crocodile, Crocodylus niloticus", "50": "American alligator, Alligator mississipiensis", "51": "triceratops", "52": "thunder snake, worm snake, Carphophis amoenus", "53": "ringneck snake, ring-necked snake, ring snake", "54": "hognose snake, puff adder, sand viper", "55": "green snake, grass snake", "56": "king snake, kingsnake", "57": "garter snake, grass snake", "58": "water snake", "59": "vine snake", "60": "night snake, Hypsiglena torquata", "61": "boa constrictor, Constrictor constrictor", "62": "rock python, rock snake, Python sebae", "63": "Indian cobra, Naja naja", "64": "green mamba", "65": "sea snake", "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", "68": "sidewinder, horned rattlesnake, Crotalus cerastes", "69": "trilobite", "70": "harvestman, daddy longlegs, Phalangium opilio", "71": "scorpion", "72": "black and gold garden spider, Argiope aurantia", "73": "barn spider, Araneus cavaticus", "74": "garden spider, Aranea diademata", "75": "black widow, Latrodectus mactans", "76": "tarantula", "77": "wolf spider, hunting spider", "78": "tick", "79": "centipede", "80": "black grouse", "81": "ptarmigan", "82": "ruffed grouse, partridge, Bonasa umbellus", "83": "prairie chicken, prairie grouse, prairie fowl", "84": "peacock", "85": "quail", "86": "partridge", "87": "African grey, African gray, Psittacus erithacus", "88": "macaw", "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90": "lorikeet", "91": "coucal", "92": "bee eater", "93": "hornbill", "94": "hummingbird", "95": "jacamar", "96": "toucan", "97": "drake", "98": "red-breasted merganser, Mergus serrator", "99": "goose", "100": "black swan, Cygnus atratus", "101": "tusker", "102": "echidna, spiny anteater, anteater", "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104": "wallaby, brush kangaroo", "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106": "wombat", "107": "jellyfish", "108": "sea anemone, anemone", "109": "brain coral", "110": "flatworm, platyhelminth", "111": "nematode, nematode worm, roundworm", "112": "conch", "113": "snail", "114": "slug", "115": "sea slug, nudibranch", "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", "117": "chambered nautilus, pearly nautilus, nautilus", "118": "Dungeness crab, Cancer magister", "119": "rock crab, Cancer irroratus", "120": "fiddler crab", "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124": "crayfish, crawfish, crawdad, crawdaddy", "125": "hermit crab", "126": "isopod", "127": "white stork, Ciconia ciconia", "128": "black stork, Ciconia nigra", "129": "spoonbill", "130": "flamingo", "131": "little blue heron, Egretta caerulea", "132": "American egret, great white heron, Egretta albus", "133": "bittern", "134": "crane", "135": "limpkin, Aramus pictus", "136": "European gallinule, Porphyrio porphyrio", "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", "138": "bustard", "139": "ruddy turnstone, Arenaria interpres", "140": "red-backed sandpiper, dunlin, Erolia alpina", "141": "redshank, Tringa totanus", "142": "dowitcher", "143": "oystercatcher, oyster catcher", "144": "pelican", "145": "king penguin, Aptenodytes patagonica", "146": "albatross, mollymawk", "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149": "dugong, Dugong dugon", "150": "sea lion", "151": "Chihuahua", "152": "Japanese spaniel", "153": "Maltese dog, Maltese terrier, Maltese", "154": "Pekinese, Pekingese, Peke", "155": "Shih-Tzu", "156": "Blenheim spaniel", "157": "papillon", "158": "toy terrier", "159": "Rhodesian ridgeback", "160": "Afghan hound, Afghan", "161": "basset, basset hound", "162": "beagle", "163": "bloodhound, sleuthhound", "164": "bluetick", "165": "black-and-tan coonhound", "166": "Walker hound, Walker foxhound", "167": "English foxhound", "168": "redbone", "169": "borzoi, Russian wolfhound", "170": "Irish wolfhound", "171": "Italian greyhound", "172": "whippet", "173": "Ibizan hound, Ibizan Podenco", "174": "Norwegian elkhound, elkhound", "175": "otterhound, otter hound", "176": "Saluki, gazelle hound", "177": "Scottish deerhound, deerhound", "178": "Weimaraner", "179": "Staffordshire bullterrier, Staffordshire bull terrier", "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181": "Bedlington terrier", "182": "Border terrier", "183": "Kerry blue terrier", "184": "Irish terrier", "185": "Norfolk terrier", "186": "Norwich terrier", "187": "Yorkshire terrier", "188": "wire-haired fox terrier", "189": "Lakeland terrier", "190": "Sealyham terrier, Sealyham", "191": "Airedale, Airedale terrier", "192": "cairn, cairn terrier", "193": "Australian terrier", "194": "Dandie Dinmont, Dandie Dinmont terrier", "195": "Boston bull, Boston terrier", "196": "miniature schnauzer", "197": "giant schnauzer", "198": "standard schnauzer", "199": "Scotch terrier, Scottish terrier, Scottie", "200": "Tibetan terrier, chrysanthemum dog", "201": "silky terrier, Sydney silky", "202": "soft-coated wheaten terrier", "203": "West Highland white terrier", "204": "Lhasa, Lhasa apso", "205": "flat-coated retriever", "206": "curly-coated retriever", "207": "golden retriever", "208": "Labrador retriever", "209": "Chesapeake Bay retriever", "210": "German short-haired pointer", "211": "vizsla, Hungarian pointer", "212": "English setter", "213": "Irish setter, red setter", "214": "Gordon setter", "215": "Brittany spaniel", "216": "clumber, clumber spaniel", "217": "English springer, English springer spaniel", "218": "Welsh springer spaniel", "219": "cocker spaniel, English cocker spaniel, cocker", "220": "Sussex spaniel", "221": "Irish water spaniel", "222": "kuvasz", "223": "schipperke", "224": "groenendael", "225": "malinois", "226": "briard", "227": "kelpie", "228": "komondor", "229": "Old English sheepdog, bobtail", "230": "Shetland sheepdog, Shetland sheep dog, Shetland", "231": "collie", "232": "Border collie", "233": "Bouvier des Flandres, Bouviers des Flandres", "234": "Rottweiler", "235": "German shepherd, German shepherd dog, German police dog, alsatian", "236": "Doberman, Doberman pinscher", "237": "miniature pinscher", "238": "Greater Swiss Mountain dog", "239": "Bernese mountain dog", "240": "Appenzeller", "241": "EntleBucher", "242": "boxer", "243": "bull mastiff", "244": "Tibetan mastiff", "245": "French bulldog", "246": "Great Dane", "247": "Saint Bernard, St Bernard", "248": "Eskimo dog, husky", "249": "malamute, malemute, Alaskan malamute", "250": "Siberian husky", "251": "dalmatian, coach dog, carriage dog", "252": "affenpinscher, monkey pinscher, monkey dog", "253": "basenji", "254": "pug, pug-dog", "255": "Leonberg", "256": "Newfoundland, Newfoundland dog", "257": "Great Pyrenees", "258": "Samoyed, Samoyede", "259": "Pomeranian", "260": "chow, chow chow", "261": "keeshond", "262": "Brabancon griffon", "263": "Pembroke, Pembroke Welsh corgi", "264": "Cardigan, Cardigan Welsh corgi", "265": "toy poodle", "266": "miniature poodle", "267": "standard poodle", "268": "Mexican hairless", "269": "timber wolf, grey wolf, gray wolf, Canis lupus", "270": "white wolf, Arctic wolf, Canis lupus tundrarum", "271": "red wolf, maned wolf, Canis rufus, Canis niger", "272": "coyote, prairie wolf, brush wolf, Canis latrans", "273": "dingo, warrigal, warragal, Canis dingo", "274": "dhole, Cuon alpinus", "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276": "hyena, hyaena", "277": "red fox, Vulpes vulpes", "278": "kit fox, Vulpes macrotis", "279": "Arctic fox, white fox, Alopex lagopus", "280": "grey fox, gray fox, Urocyon cinereoargenteus", "281": "tabby, tabby cat", "282": "tiger cat", "283": "Persian cat", "284": "Siamese cat, Siamese", "285": "Egyptian cat", "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287": "lynx, catamount", "288": "leopard, Panthera pardus", "289": "snow leopard, ounce, Panthera uncia", "290": "jaguar, panther, Panthera onca, Felis onca", "291": "lion, king of beasts, Panthera leo", "292": "tiger, Panthera tigris", "293": "cheetah, chetah, Acinonyx jubatus", "294": "brown bear, bruin, Ursus arctos", "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297": "sloth bear, Melursus ursinus, Ursus ursinus", "298": "mongoose", "299": "meerkat, mierkat", "300": "tiger beetle", "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302": "ground beetle, carabid beetle", "303": "long-horned beetle, longicorn, longicorn beetle", "304": "leaf beetle, chrysomelid", "305": "dung beetle", "306": "rhinoceros beetle", "307": "weevil", "308": "fly", "309": "bee", "310": "ant, emmet, pismire", "311": "grasshopper, hopper", "312": "cricket", "313": "walking stick, walkingstick, stick insect", "314": "cockroach, roach", "315": "mantis, mantid", "316": "cicada, cicala", "317": "leafhopper", "318": "lacewing, lacewing fly", "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "320": "damselfly", "321": "admiral", "322": "ringlet, ringlet butterfly", "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324": "cabbage butterfly", "325": "sulphur butterfly, sulfur butterfly", "326": "lycaenid, lycaenid butterfly", "327": "starfish, sea star", "328": "sea urchin", "329": "sea cucumber, holothurian", "330": "wood rabbit, cottontail, cottontail rabbit", "331": "hare", "332": "Angora, Angora rabbit", "333": "hamster", "334": "porcupine, hedgehog", "335": "fox squirrel, eastern fox squirrel, Sciurus niger", "336": "marmot", "337": "beaver", "338": "guinea pig, Cavia cobaya", "339": "sorrel", "340": "zebra", "341": "hog, pig, grunter, squealer, Sus scrofa", "342": "wild boar, boar, Sus scrofa", "343": "warthog", "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", "345": "ox", "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347": "bison", "348": "ram, tup", "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350": "ibex, Capra ibex", "351": "hartebeest", "352": "impala, Aepyceros melampus", "353": "gazelle", "354": "Arabian camel, dromedary, Camelus dromedarius", "355": "llama", "356": "weasel", "357": "mink", "358": "polecat, fitch, foulmart, foumart, Mustela putorius", "359": "black-footed ferret, ferret, Mustela nigripes", "360": "otter", "361": "skunk, polecat, wood pussy", "362": "badger", "363": "armadillo", "364": "three-toed sloth, ai, Bradypus tridactylus", "365": "orangutan, orang, orangutang, Pongo pygmaeus", "366": "gorilla, Gorilla gorilla", "367": "chimpanzee, chimp, Pan troglodytes", "368": "gibbon, Hylobates lar", "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", "370": "guenon, guenon monkey", "371": "patas, hussar monkey, Erythrocebus patas", "372": "baboon", "373": "macaque", "374": "langur", "375": "colobus, colobus monkey", "376": "proboscis monkey, Nasalis larvatus", "377": "marmoset", "378": "capuchin, ringtail, Cebus capucinus", "379": "howler monkey, howler", "380": "titi, titi monkey", "381": "spider monkey, Ateles geoffroyi", "382": "squirrel monkey, Saimiri sciureus", "383": "Madagascar cat, ring-tailed lemur, Lemur catta", "384": "indri, indris, Indri indri, Indri brevicaudatus", "385": "Indian elephant, Elephas maximus", "386": "African elephant, Loxodonta africana", "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389": "barracouta, snoek", "390": "eel", "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392": "rock beauty, Holocanthus tricolor", "393": "anemone fish", "394": "sturgeon", "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", "396": "lionfish", "397": "puffer, pufferfish, blowfish, globefish", "398": "abacus", "399": "abaya", "400": "academic gown, academic robe, judge's robe", "401": "accordion, piano accordion, squeeze box", "402": "acoustic guitar", "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", "404": "airliner", "405": "airship, dirigible", "406": "altar", "407": "ambulance", "408": "amphibian, amphibious vehicle", "409": "analog clock", "410": "apiary, bee house", "411": "apron", "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413": "assault rifle, assault gun", "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", "415": "bakery, bakeshop, bakehouse", "416": "balance beam, beam", "417": "balloon", "418": "ballpoint, ballpoint pen, ballpen, Biro", "419": "Band Aid", "420": "banjo", "421": "bannister, banister, balustrade, balusters, handrail", "422": "barbell", "423": "barber chair", "424": "barbershop", "425": "barn", "426": "barometer", "427": "barrel, cask", "428": "barrow, garden cart, lawn cart, wheelbarrow", "429": "baseball", "430": "basketball", "431": "bassinet", "432": "bassoon", "433": "bathing cap, swimming cap", "434": "bath towel", "435": "bathtub, bathing tub, bath, tub", "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437": "beacon, lighthouse, beacon light, pharos", "438": "beaker", "439": "bearskin, busby, shako", "440": "beer bottle", "441": "beer glass", "442": "bell cote, bell cot", "443": "bib", "444": "bicycle-built-for-two, tandem bicycle, tandem", "445": "bikini, two-piece", "446": "binder, ring-binder", "447": "binoculars, field glasses, opera glasses", "448": "birdhouse", "449": "boathouse", "450": "bobsled, bobsleigh, bob", "451": "bolo tie, bolo, bola tie, bola", "452": "bonnet, poke bonnet", "453": "bookcase", "454": "bookshop, bookstore, bookstall", "455": "bottlecap", "456": "bow", "457": "bow tie, bow-tie, bowtie", "458": "brass, memorial tablet, plaque", "459": "brassiere, bra, bandeau", "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461": "breastplate, aegis, egis", "462": "broom", "463": "bucket, pail", "464": "buckle", "465": "bulletproof vest", "466": "bullet train, bullet", "467": "butcher shop, meat market", "468": "cab, hack, taxi, taxicab", "469": "caldron, cauldron", "470": "candle, taper, wax light", "471": "cannon", "472": "canoe", "473": "can opener, tin opener", "474": "cardigan", "475": "car mirror", "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", "477": "carpenter's kit, tool kit", "478": "carton", "479": "car wheel", "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481": "cassette", "482": "cassette player", "483": "castle", "484": "catamaran", "485": "CD player", "486": "cello, violoncello", "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", "488": "chain", "489": "chainlink fence", "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491": "chain saw, chainsaw", "492": "chest", "493": "chiffonier, commode", "494": "chime, bell, gong", "495": "china cabinet, china closet", "496": "Christmas stocking", "497": "church, church building", "498": "cinema, movie theater, movie theatre, movie house, picture palace", "499": "cleaver, meat cleaver, chopper", "500": "cliff dwelling", "501": "cloak", "502": "clog, geta, patten, sabot", "503": "cocktail shaker", "504": "coffee mug", "505": "coffeepot", "506": "coil, spiral, volute, whorl, helix", "507": "combination lock", "508": "computer keyboard, keypad", "509": "confectionery, confectionary, candy store", "510": "container ship, containership, container vessel", "511": "convertible", "512": "corkscrew, bottle screw", "513": "cornet, horn, trumpet, trump", "514": "cowboy boot", "515": "cowboy hat, ten-gallon hat", "516": "cradle", "517": "crane2", "518": "crash helmet", "519": "crate", "520": "crib, cot", "521": "Crock Pot", "522": "croquet ball", "523": "crutch", "524": "cuirass", "525": "dam, dike, dyke", "526": "desk", "527": "desktop computer", "528": "dial telephone, dial phone", "529": "diaper, nappy, napkin", "530": "digital clock", "531": "digital watch", "532": "dining table, board", "533": "dishrag, dishcloth", "534": "dishwasher, dish washer, dishwashing machine", "535": "disk brake, disc brake", "536": "dock, dockage, docking facility", "537": "dogsled, dog sled, dog sleigh", "538": "dome", "539": "doormat, welcome mat", "540": "drilling platform, offshore rig", "541": "drum, membranophone, tympan", "542": "drumstick", "543": "dumbbell", "544": "Dutch oven", "545": "electric fan, blower", "546": "electric guitar", "547": "electric locomotive", "548": "entertainment center", "549": "envelope", "550": "espresso maker", "551": "face powder", "552": "feather boa, boa", "553": "file, file cabinet, filing cabinet", "554": "fireboat", "555": "fire engine, fire truck", "556": "fire screen, fireguard", "557": "flagpole, flagstaff", "558": "flute, transverse flute", "559": "folding chair", "560": "football helmet", "561": "forklift", "562": "fountain", "563": "fountain pen", "564": "four-poster", "565": "freight car", "566": "French horn, horn", "567": "frying pan, frypan, skillet", "568": "fur coat", "569": "garbage truck, dustcart", "570": "gasmask, respirator, gas helmet", "571": "gas pump, gasoline pump, petrol pump, island dispenser", "572": "goblet", "573": "go-kart", "574": "golf ball", "575": "golfcart, golf cart", "576": "gondola", "577": "gong, tam-tam", "578": "gown", "579": "grand piano, grand", "580": "greenhouse, nursery, glasshouse", "581": "grille, radiator grille", "582": "grocery store, grocery, food market, market", "583": "guillotine", "584": "hair slide", "585": "hair spray", "586": "half track", "587": "hammer", "588": "hamper", "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", "590": "hand-held computer, hand-held microcomputer", "591": "handkerchief, hankie, hanky, hankey", "592": "hard disc, hard disk, fixed disk", "593": "harmonica, mouth organ, harp, mouth harp", "594": "harp", "595": "harvester, reaper", "596": "hatchet", "597": "holster", "598": "home theater, home theatre", "599": "honeycomb", "600": "hook, claw", "601": "hoopskirt, crinoline", "602": "horizontal bar, high bar", "603": "horse cart, horse-cart", "604": "hourglass", "605": "iPod", "606": "iron, smoothing iron", "607": "jack-o'-lantern", "608": "jean, blue jean, denim", "609": "jeep, landrover", "610": "jersey, T-shirt, tee shirt", "611": "jigsaw puzzle", "612": "jinrikisha, ricksha, rickshaw", "613": "joystick", "614": "kimono", "615": "knee pad", "616": "knot", "617": "lab coat, laboratory coat", "618": "ladle", "619": "lampshade, lamp shade", "620": "laptop, laptop computer", "621": "lawn mower, mower", "622": "lens cap, lens cover", "623": "letter opener, paper knife, paperknife", "624": "library", "625": "lifeboat", "626": "lighter, light, igniter, ignitor", "627": "limousine, limo", "628": "liner, ocean liner", "629": "lipstick, lip rouge", "630": "Loafer", "631": "lotion", "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633": "loupe, jeweler's loupe", "634": "lumbermill, sawmill", "635": "magnetic compass", "636": "mailbag, postbag", "637": "mailbox, letter box", "638": "maillot", "639": "maillot, tank suit", "640": "manhole cover", "641": "maraca", "642": "marimba, xylophone", "643": "mask", "644": "matchstick", "645": "maypole", "646": "maze, labyrinth", "647": "measuring cup", "648": "medicine chest, medicine cabinet", "649": "megalith, megalithic structure", "650": "microphone, mike", "651": "microwave, microwave oven", "652": "military uniform", "653": "milk can", "654": "minibus", "655": "miniskirt, mini", "656": "minivan", "657": "missile", "658": "mitten", "659": "mixing bowl", "660": "mobile home, manufactured home", "661": "Model T", "662": "modem", "663": "monastery", "664": "monitor", "665": "moped", "666": "mortar", "667": "mortarboard", "668": "mosque", "669": "mosquito net", "670": "motor scooter, scooter", "671": "mountain bike, all-terrain bike, off-roader", "672": "mountain tent", "673": "mouse, computer mouse", "674": "mousetrap", "675": "moving van", "676": "muzzle", "677": "nail", "678": "neck brace", "679": "necklace", "680": "nipple", "681": "notebook, notebook computer", "682": "obelisk", "683": "oboe, hautboy, hautbois", "684": "ocarina, sweet potato", "685": "odometer, hodometer, mileometer, milometer", "686": "oil filter", "687": "organ, pipe organ", "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", "689": "overskirt", "690": "oxcart", "691": "oxygen mask", "692": "packet", "693": "paddle, boat paddle", "694": "paddlewheel, paddle wheel", "695": "padlock", "696": "paintbrush", "697": "pajama, pyjama, pj's, jammies", "698": "palace", "699": "panpipe, pandean pipe, syrinx", "700": "paper towel", "701": "parachute, chute", "702": "parallel bars, bars", "703": "park bench", "704": "parking meter", "705": "passenger car, coach, carriage", "706": "patio, terrace", "707": "pay-phone, pay-station", "708": "pedestal, plinth, footstall", "709": "pencil box, pencil case", "710": "pencil sharpener", "711": "perfume, essence", "712": "Petri dish", "713": "photocopier", "714": "pick, plectrum, plectron", "715": "pickelhaube", "716": "picket fence, paling", "717": "pickup, pickup truck", "718": "pier", "719": "piggy bank, penny bank", "720": "pill bottle", "721": "pillow", "722": "ping-pong ball", "723": "pinwheel", "724": "pirate, pirate ship", "725": "pitcher, ewer", "726": "plane, carpenter's plane, woodworking plane", "727": "planetarium", "728": "plastic bag", "729": "plate rack", "730": "plow, plough", "731": "plunger, plumber's helper", "732": "Polaroid camera, Polaroid Land camera", "733": "pole", "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735": "poncho", "736": "pool table, billiard table, snooker table", "737": "pop bottle, soda bottle", "738": "pot, flowerpot", "739": "potter's wheel", "740": "power drill", "741": "prayer rug, prayer mat", "742": "printer", "743": "prison, prison house", "744": "projectile, missile", "745": "projector", "746": "puck, hockey puck", "747": "punching bag, punch bag, punching ball, punchball", "748": "purse", "749": "quill, quill pen", "750": "quilt, comforter, comfort, puff", "751": "racer, race car, racing car", "752": "racket, racquet", "753": "radiator", "754": "radio, wireless", "755": "radio telescope, radio reflector", "756": "rain barrel", "757": "recreational vehicle, RV, R.V.", "758": "reel", "759": "reflex camera", "760": "refrigerator, icebox", "761": "remote control, remote", "762": "restaurant, eating house, eating place, eatery", "763": "revolver, six-gun, six-shooter", "764": "rifle", "765": "rocking chair, rocker", "766": "rotisserie", "767": "rubber eraser, rubber, pencil eraser", "768": "rugby ball", "769": "rule, ruler", "770": "running shoe", "771": "safe", "772": "safety pin", "773": "saltshaker, salt shaker", "774": "sandal", "775": "sarong", "776": "sax, saxophone", "777": "scabbard", "778": "scale, weighing machine", "779": "school bus", "780": "schooner", "781": "scoreboard", "782": "screen, CRT screen", "783": "screw", "784": "screwdriver", "785": "seat belt, seatbelt", "786": "sewing machine", "787": "shield, buckler", "788": "shoe shop, shoe-shop, shoe store", "789": "shoji", "790": "shopping basket", "791": "shopping cart", "792": "shovel", "793": "shower cap", "794": "shower curtain", "795": "ski", "796": "ski mask", "797": "sleeping bag", "798": "slide rule, slipstick", "799": "sliding door", "800": "slot, one-armed bandit", "801": "snorkel", "802": "snowmobile", "803": "snowplow, snowplough", "804": "soap dispenser", "805": "soccer ball", "806": "sock", "807": "solar dish, solar collector, solar furnace", "808": "sombrero", "809": "soup bowl", "810": "space bar", "811": "space heater", "812": "space shuttle", "813": "spatula", "814": "speedboat", "815": "spider web, spider's web", "816": "spindle", "817": "sports car, sport car", "818": "spotlight, spot", "819": "stage", "820": "steam locomotive", "821": "steel arch bridge", "822": "steel drum", "823": "stethoscope", "824": "stole", "825": "stone wall", "826": "stopwatch, stop watch", "827": "stove", "828": "strainer", "829": "streetcar, tram, tramcar, trolley, trolley car", "830": "stretcher", "831": "studio couch, day bed", "832": "stupa, tope", "833": "submarine, pigboat, sub, U-boat", "834": "suit, suit of clothes", "835": "sundial", "836": "sunglass", "837": "sunglasses, dark glasses, shades", "838": "sunscreen, sunblock, sun blocker", "839": "suspension bridge", "840": "swab, swob, mop", "841": "sweatshirt", "842": "swimming trunks, bathing trunks", "843": "swing", "844": "switch, electric switch, electrical switch", "845": "syringe", "846": "table lamp", "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", "848": "tape player", "849": "teapot", "850": "teddy, teddy bear", "851": "television, television system", "852": "tennis ball", "853": "thatch, thatched roof", "854": "theater curtain, theatre curtain", "855": "thimble", "856": "thresher, thrasher, threshing machine", "857": "throne", "858": "tile roof", "859": "toaster", "860": "tobacco shop, tobacconist shop, tobacconist", "861": "toilet seat", "862": "torch", "863": "totem pole", "864": "tow truck, tow car, wrecker", "865": "toyshop", "866": "tractor", "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868": "tray", "869": "trench coat", "870": "tricycle, trike, velocipede", "871": "trimaran", "872": "tripod", "873": "triumphal arch", "874": "trolleybus, trolley coach, trackless trolley", "875": "trombone", "876": "tub, vat", "877": "turnstile", "878": "typewriter keyboard", "879": "umbrella", "880": "unicycle, monocycle", "881": "upright, upright piano", "882": "vacuum, vacuum cleaner", "883": "vase", "884": "vault", "885": "velvet", "886": "vending machine", "887": "vestment", "888": "viaduct", "889": "violin, fiddle", "890": "volleyball", "891": "waffle iron", "892": "wall clock", "893": "wallet, billfold, notecase, pocketbook", "894": "wardrobe, closet, press", "895": "warplane, military plane", "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897": "washer, automatic washer, washing machine", "898": "water bottle", "899": "water jug", "900": "water tower", "901": "whiskey jug", "902": "whistle", "903": "wig", "904": "window screen", "905": "window shade", "906": "Windsor tie", "907": "wine bottle", "908": "wing", "909": "wok", "910": "wooden spoon", "911": "wool, woolen, woollen", "912": "worm fence, snake fence, snake-rail fence, Virginia fence", "913": "wreck", "914": "yawl", "915": "yurt", "916": "web site, website, internet site, site", "917": "comic book", "918": "crossword puzzle, crossword", "919": "street sign", "920": "traffic light, traffic signal, stoplight", "921": "book jacket, dust cover, dust jacket, dust wrapper", "922": "menu", "923": "plate", "924": "guacamole", "925": "consomme", "926": "hot pot, hotpot", "927": "trifle", "928": "ice cream, icecream", "929": "ice lolly, lolly, lollipop, popsicle", "930": "French loaf", "931": "bagel, beigel", "932": "pretzel", "933": "cheeseburger", "934": "hotdog, hot dog, red hot", "935": "mashed potato", "936": "head cabbage", "937": "broccoli", "938": "cauliflower", "939": "zucchini, courgette", "940": "spaghetti squash", "941": "acorn squash", "942": "butternut squash", "943": "cucumber, cuke", "944": "artichoke, globe artichoke", "945": "bell pepper", "946": "cardoon", "947": "mushroom", "948": "Granny Smith", "949": "strawberry", "950": "orange", "951": "lemon", "952": "fig", "953": "pineapple, ananas", "954": "banana", "955": "jackfruit, jak, jack", "956": "custard apple", "957": "pomegranate", "958": "hay", "959": "carbonara", "960": "chocolate sauce, chocolate syrup", "961": "dough", "962": "meat loaf, meatloaf", "963": "pizza, pizza pie", "964": "potpie", "965": "burrito", "966": "red wine", "967": "espresso", "968": "cup", "969": "eggnog", "970": "alp", "971": "bubble", "972": "cliff, drop, drop-off", "973": "coral reef", "974": "geyser", "975": "lakeside, lakeshore", "976": "promontory, headland, head, foreland", "977": "sandbar, sand bar", "978": "seashore, coast, seacoast, sea-coast", "979": "valley, vale", "980": "volcano", "981": "ballplayer, baseball player", "982": "groom, bridegroom", "983": "scuba diver", "984": "rapeseed", "985": "daisy", "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987": "corn", "988": "acorn", "989": "hip, rose hip, rosehip", "990": "buckeye, horse chestnut, conker", "991": "coral fungus", "992": "agaric", "993": "gyromitra", "994": "stinkhorn, carrion fungus", "995": "earthstar", "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997": "bolete", "998": "ear, spike, capitulum", "999": "toilet tissue, toilet paper, bathroom tissue"}}}}, {"name": "lexicon", "sequence": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "validation", "num_bytes": 406246742.0, "num_examples": 3000}], "download_size": 398667087, "dataset_size": 406246742.0}}
2023-02-10T18:05:59+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Imagenet1k_sample_validation" More Information needed
[ "# Dataset Card for \"Imagenet1k_sample_validation\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Imagenet1k_sample_validation\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Imagenet1k_sample_validation\"\n\nMore Information needed" ]
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42c9798b0e5d580170c11b3b9c615195bafc1c09
# Dataset Card for "Europarl-ST-processed-mt-ro" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tj-solergibert/Europarl-ST-processed-mt-ro
[ "region:us" ]
2023-02-10T18:08:28+00:00
{"dataset_info": {"features": [{"name": "source_text", "dtype": "string"}, {"name": "dest_text", "dtype": "string"}, {"name": "dest_lang", "dtype": {"class_label": {"names": {"0": "de", "1": "en", "2": "es", "3": "fr", "4": "it", "5": "nl", "6": "pl", "7": "pt", "8": "ro"}}}}], "splits": [{"name": "train", "num_bytes": 139150159, "num_examples": 384704}, {"name": "valid", "num_bytes": 18067165, "num_examples": 48280}, {"name": "test", "num_bytes": 19720811, "num_examples": 53360}], "download_size": 66531208, "dataset_size": 176938135}}
2023-02-10T18:08:43+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Europarl-ST-processed-mt-ro" More Information needed
[ "# Dataset Card for \"Europarl-ST-processed-mt-ro\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Europarl-ST-processed-mt-ro\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Europarl-ST-processed-mt-ro\"\n\nMore Information needed" ]
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3b1cd700203f4a613a4f306270220f32514be2cd
# Dataset Card for MCoNaLa ## Dataset Description - **Homepage:** https://github.com/zorazrw/multilingual-conala - **Repository:** https://github.com/zorazrw/multilingual-conala - **Paper:** https://arxiv.org/pdf/2203.08388.pdf - **Leaderboard:** https://explainaboard.inspiredco.ai/leaderboards?show_mine=false&sort_dir=desc&sort_field=created_at&dataset=mconala ### Dataset Summary MCoNaLa is a Multilingual Code/Natural Language Challenge dataset with 896 NL-Code pairs in three languages: Spanish, Japanese, and Russian. ### Languages Spanish, Japanese, Russian; Python ## Dataset Structure ### How to Use ```bash from datasets import load_dataset # Spanish subset load_dataset("neulab/mconala", "es") DatasetDict({ test: Dataset({ features: ['question_id', 'intent', 'rewritten_intent', 'snippet'], num_rows: 341 }) }) # Japanese subset load_dataset("neulab/mconala", "ja") DatasetDict({ test: Dataset({ features: ['question_id', 'intent', 'rewritten_intent', 'snippet'], num_rows: 210 }) }) # Russian subset load_dataset("neulab/mconala", "ru") DatasetDict({ test: Dataset({ features: ['question_id', 'intent', 'rewritten_intent', 'snippet'], num_rows: 345 }) }) ``` ### Data Fields |Field|Type|Description| |---|---|---| |question_id|int|StackOverflow post id of the sample| |intent|string|Title of the Stackoverflow post as the initial NL intent| |rewritten_intent|string|nl intent rewritten by human annotators| |snippet|string|Python code solution to the NL intent| ### Data Splits The dataset contains 341, 210, and 345 samples in Spanish, Japanese, and Russian. ### Citation Information ``` @article{wang2022mconala, title={MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages}, author={Zhiruo Wang, Grace Cuenca, Shuyan Zhou, Frank F. Xu, Graham Neubig}, journal={arXiv preprint arXiv:2203.08388}, year={2022} } ```
neulab/mconala
[ "task_categories:text-generation", "task_categories:translation", "size_categories:n<1K", "language:es", "language:ja", "language:ru", "license:cc-by-sa-4.0", "code generation", "arxiv:2203.08388", "region:us" ]
2023-02-10T18:08:54+00:00
{"language": ["es", "ja", "ru"], "license": "cc-by-sa-4.0", "size_categories": ["n<1K"], "task_categories": ["text-generation", "translation"], "pretty_name": "mconala", "tags": ["code generation"]}
2023-02-10T19:01:31+00:00
[ "2203.08388" ]
[ "es", "ja", "ru" ]
TAGS #task_categories-text-generation #task_categories-translation #size_categories-n<1K #language-Spanish #language-Japanese #language-Russian #license-cc-by-sa-4.0 #code generation #arxiv-2203.08388 #region-us
Dataset Card for MCoNaLa ======================== Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: URL * Leaderboard: URL ### Dataset Summary MCoNaLa is a Multilingual Code/Natural Language Challenge dataset with 896 NL-Code pairs in three languages: Spanish, Japanese, and Russian. ### Languages Spanish, Japanese, Russian; Python Dataset Structure ----------------- ### How to Use ### Data Fields Field: question\_id, Type: int, Description: StackOverflow post id of the sample Field: intent, Type: string, Description: Title of the Stackoverflow post as the initial NL intent Field: rewritten\_intent, Type: string, Description: nl intent rewritten by human annotators Field: snippet, Type: string, Description: Python code solution to the NL intent ### Data Splits The dataset contains 341, 210, and 345 samples in Spanish, Japanese, and Russian.
[ "### Dataset Summary\n\n\nMCoNaLa is a Multilingual Code/Natural Language Challenge dataset with 896 NL-Code pairs in three languages: Spanish, Japanese, and Russian.", "### Languages\n\n\nSpanish, Japanese, Russian; Python\n\n\nDataset Structure\n-----------------", "### How to Use", "### Data Fields\n\n\nField: question\\_id, Type: int, Description: StackOverflow post id of the sample\nField: intent, Type: string, Description: Title of the Stackoverflow post as the initial NL intent\nField: rewritten\\_intent, Type: string, Description: nl intent rewritten by human annotators\nField: snippet, Type: string, Description: Python code solution to the NL intent", "### Data Splits\n\n\nThe dataset contains 341, 210, and 345 samples in Spanish, Japanese, and Russian." ]
[ "TAGS\n#task_categories-text-generation #task_categories-translation #size_categories-n<1K #language-Spanish #language-Japanese #language-Russian #license-cc-by-sa-4.0 #code generation #arxiv-2203.08388 #region-us \n", "### Dataset Summary\n\n\nMCoNaLa is a Multilingual Code/Natural Language Challenge dataset with 896 NL-Code pairs in three languages: Spanish, Japanese, and Russian.", "### Languages\n\n\nSpanish, Japanese, Russian; Python\n\n\nDataset Structure\n-----------------", "### How to Use", "### Data Fields\n\n\nField: question\\_id, Type: int, Description: StackOverflow post id of the sample\nField: intent, Type: string, Description: Title of the Stackoverflow post as the initial NL intent\nField: rewritten\\_intent, Type: string, Description: nl intent rewritten by human annotators\nField: snippet, Type: string, Description: Python code solution to the NL intent", "### Data Splits\n\n\nThe dataset contains 341, 210, and 345 samples in Spanish, Japanese, and Russian." ]
[ 74, 44, 18, 5, 99, 28 ]
[ "passage: TAGS\n#task_categories-text-generation #task_categories-translation #size_categories-n<1K #language-Spanish #language-Japanese #language-Russian #license-cc-by-sa-4.0 #code generation #arxiv-2203.08388 #region-us \n### Dataset Summary\n\n\nMCoNaLa is a Multilingual Code/Natural Language Challenge dataset with 896 NL-Code pairs in three languages: Spanish, Japanese, and Russian.### Languages\n\n\nSpanish, Japanese, Russian; Python\n\n\nDataset Structure\n-----------------### How to Use### Data Fields\n\n\nField: question\\_id, Type: int, Description: StackOverflow post id of the sample\nField: intent, Type: string, Description: Title of the Stackoverflow post as the initial NL intent\nField: rewritten\\_intent, Type: string, Description: nl intent rewritten by human annotators\nField: snippet, Type: string, Description: Python code solution to the NL intent### Data Splits\n\n\nThe dataset contains 341, 210, and 345 samples in Spanish, Japanese, and Russian." ]
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7a19a42b0594f4359985347355253fc8c675443b
# Dataset Card for "Europarl-ST-processed-mt-pl" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tj-solergibert/Europarl-ST-processed-mt-pl
[ "region:us" ]
2023-02-10T18:17:56+00:00
{"dataset_info": {"features": [{"name": "source_text", "dtype": "string"}, {"name": "dest_text", "dtype": "string"}, {"name": "dest_lang", "dtype": {"class_label": {"names": {"0": "de", "1": "en", "2": "es", "3": "fr", "4": "it", "5": "nl", "6": "pl", "7": "pt", "8": "ro"}}}}], "splits": [{"name": "train", "num_bytes": 137540859, "num_examples": 384704}, {"name": "valid", "num_bytes": 17934207, "num_examples": 48280}, {"name": "test", "num_bytes": 19550172, "num_examples": 53360}], "download_size": 65800571, "dataset_size": 175025238}}
2023-02-10T18:18:09+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Europarl-ST-processed-mt-pl" More Information needed
[ "# Dataset Card for \"Europarl-ST-processed-mt-pl\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Europarl-ST-processed-mt-pl\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Europarl-ST-processed-mt-pl\"\n\nMore Information needed" ]
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5f2872187573d4b52ddec4e1ac04ad1714200f96
Use the 25% suffix array to deduplicate the full Oscar, i.e. remove any document which has an at least 100-char span overlapping with the 25% chunk we selected in the previous bullet. This is more permissive and leaves us with 136 million documents or 31% of the original dataset. Also for reasons the explanation of which would probably involve terms like power laws, we still remove most of the most pervasive duplicates - so I'm pretty optimistic about this being useful.
datablations/oscar-dedup-expanded
[ "region:us" ]
2023-02-10T18:42:08+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "meta", "struct": [{"name": "warc_headers", "struct": [{"name": "warc-record-id", "dtype": "string"}, {"name": "warc-date", "dtype": "string"}, {"name": "content-type", "dtype": "string"}, {"name": "content-length", "dtype": "int32"}, {"name": "warc-type", "dtype": "string"}, {"name": "warc-identified-content-language", "dtype": "string"}, {"name": "warc-refers-to", "dtype": "string"}, {"name": "warc-target-uri", "dtype": "string"}, {"name": "warc-block-digest", "dtype": "string"}]}, {"name": "identification", "struct": [{"name": "label", "dtype": "string"}, {"name": "prob", "dtype": "float32"}]}, {"name": "annotations", "sequence": "string"}, {"name": "line_identifications", "list": [{"name": "label", "dtype": "string"}, {"name": "prob", "dtype": "float32"}]}]}, {"name": "perplexity_score", "dtype": "float64"}, {"name": "text_length", "dtype": "int64"}, {"name": "url", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "dup_ratio", "dtype": "float64"}, {"name": "pairs", "sequence": {"sequence": "int64"}}, {"name": "repetitions", "sequence": "binary"}, {"name": "included_in_dedup", "dtype": "bool"}, {"name": "cluster", "sequence": "int64"}, {"name": "has_dup_25", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 3188540880787, "num_examples": 431992659}], "download_size": 1732364041898, "dataset_size": 3188540880787}}
2023-05-10T05:57:52+00:00
[]
[]
TAGS #region-us
Use the 25% suffix array to deduplicate the full Oscar, i.e. remove any document which has an at least 100-char span overlapping with the 25% chunk we selected in the previous bullet. This is more permissive and leaves us with 136 million documents or 31% of the original dataset. Also for reasons the explanation of which would probably involve terms like power laws, we still remove most of the most pervasive duplicates - so I'm pretty optimistic about this being useful.
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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a550782239f9dc2a915c11d187d65887029f0e2a
# Dataset Card for "squad-pt-v1.1" Dataset squad-v1.1 traduzido pelo grupo [(www.deeplearningbrasil.com.br)](www.deeplearningbrasil.com.br). Todos os créditos ao grupo pela tradução e aos [autores originais](https://rajpurkar.github.io/SQuAD-explorer/).
tgsc/squad-pt-v1.1
[ "language:pt", "region:us" ]
2023-02-10T19:33:56+00:00
{"language": "pt", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 84838259, "num_examples": 87510}, {"name": "validation", "num_bytes": 11150628, "num_examples": 10570}], "download_size": 22898021, "dataset_size": 95988887}}
2023-07-13T11:02:03+00:00
[]
[ "pt" ]
TAGS #language-Portuguese #region-us
# Dataset Card for "squad-pt-v1.1" Dataset squad-v1.1 traduzido pelo grupo (URL). Todos os créditos ao grupo pela tradução e aos autores originais.
[ "# Dataset Card for \"squad-pt-v1.1\"\n\nDataset squad-v1.1 traduzido pelo grupo (URL). Todos os créditos ao grupo pela tradução e aos autores originais." ]
[ "TAGS\n#language-Portuguese #region-us \n", "# Dataset Card for \"squad-pt-v1.1\"\n\nDataset squad-v1.1 traduzido pelo grupo (URL). Todos os créditos ao grupo pela tradução e aos autores originais." ]
[ 12, 43 ]
[ "passage: TAGS\n#language-Portuguese #region-us \n# Dataset Card for \"squad-pt-v1.1\"\n\nDataset squad-v1.1 traduzido pelo grupo (URL). Todos os créditos ao grupo pela tradução e aos autores originais." ]
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0f67ba79e08f00187ecad10f049829dfda19ffbd
# Dataset Card for "test-model-outputs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lewtun/test-model-outputs
[ "region:us" ]
2023-02-10T19:42:02+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "source", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "outputs", "list": [{"name": "model", "dtype": "string"}, {"name": "outputs", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 982, "num_examples": 1}], "download_size": 5435, "dataset_size": 982}}
2023-02-10T19:43:22+00:00
[]
[]
TAGS #region-us
# Dataset Card for "test-model-outputs" More Information needed
[ "# Dataset Card for \"test-model-outputs\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"test-model-outputs\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"test-model-outputs\"\n\nMore Information needed" ]
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419b33d702956b3a5ee8520ce96947673c049652
# Dataset Card for "patched_test_p_10_m1_predictions_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
roa7n/patched_test_p_10_m1_predictions_v2
[ "region:us" ]
2023-02-10T19:53:53+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 1566287530, "num_examples": 2843834}], "download_size": 138365947, "dataset_size": 1566287530}}
2023-02-10T19:54:22+00:00
[]
[]
TAGS #region-us
# Dataset Card for "patched_test_p_10_m1_predictions_v2" More Information needed
[ "# Dataset Card for \"patched_test_p_10_m1_predictions_v2\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"patched_test_p_10_m1_predictions_v2\"\n\nMore Information needed" ]
[ 6, 28 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_10_m1_predictions_v2\"\n\nMore Information needed" ]
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3e67cc276131c4d34aa5c8d1892f8133a0739ffb
# Habr dataset ## Table of Contents - [Table of Contents](#table-of-contents) - [Description](#description) - [Usage](#usage) - [Data Instances](#data-instances) - [Source Data](#source-data) - [Personal and Sensitive Information](#personal-and-sensitive-information) ## Description **Summary:** Dataset of posts and comments from [habr.com](https://habr.com/ru/all/), a Russian collaborative blog about IT, computer science and anything related to the Internet. **Script:** [create_habr.py](https://github.com/IlyaGusev/rulm/blob/master/data_processing/create_habr.py) **Point of Contact:** [Ilya Gusev]([email protected]) **Languages:** Russian, English, some programming code. ## Usage Prerequisites: ```bash pip install datasets zstandard jsonlines pysimdjson ``` Dataset iteration: ```python from datasets import load_dataset dataset = load_dataset('IlyaGusev/habr', split="train", streaming=True) for example in dataset: print(example["text_markdown"]) ``` ## Data Instances ``` { "id": 12730, "language": "ru", "url": "https://habr.com/ru/post/12730/", "text_markdown": "...", "text_html": "...", "lead_markdown": "...", "lead_html": "...", "type": "article", "labels": [], "original_author": null, "original_url": null, "time_published": 1185962380, "author": "...", "title": "Хочешь в университет — сделай презентацию", "statistics": { "commentsCount": 23, "favoritesCount": 1, "readingCount": 1542, "score": 7, "votesCount": 15, "votesCountPlus": 11, "votesCountMinus": 4 }, "hubs": [ "itcompanies" ], "flows": [ "popsci" ], "tags": [ "PowerPoint", "презентация", "абитуриенты", ], "reading_time": 1, "format": null, "complexity": null, "comments": { "id": [11653537, 11653541], "parent_id": [null, 11653537], "level": [0, 1], "time_published": [1185963192, 1185967886], "score": [-1, 0], "votes": [1, 0], "message_html": ["...", "..."], "author": ["...", "..."], "children": [[11653541], []] } } ``` You can use this little helper to unflatten sequences: ```python def revert_flattening(records): fixed_records = [] for key, values in records.items(): if not fixed_records: fixed_records = [{} for _ in range(len(values))] for i, value in enumerate(values): fixed_records[i][key] = value return fixed_records ``` The original JSONL is already unflattened. ## Source Data * The data source is the [Habr](https://habr.com/) website. * API call example: [post 709430](https://habr.com/kek/v2/articles/709430). * Processing script is [here](https://github.com/IlyaGusev/rulm/blob/master/data_processing/create_habr.py). ## 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/habr
[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:ru", "language:en", "region:us" ]
2023-02-10T20:36:09+00:00
{"language": ["ru", "en"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "id", "dtype": "uint32"}, {"name": "language", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text_markdown", "dtype": "string"}, {"name": "text_html", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "original_author", "dtype": "string"}, {"name": "original_url", "dtype": "string"}, {"name": "lead_html", "dtype": "string"}, {"name": "lead_markdown", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "time_published", "dtype": "uint64"}, {"name": "statistics", "struct": [{"name": "commentsCount", "dtype": "uint32"}, {"name": "favoritesCount", "dtype": "uint32"}, {"name": "readingCount", "dtype": "uint32"}, {"name": "score", "dtype": "int32"}, {"name": "votesCount", "dtype": "int32"}, {"name": "votesCountPlus", "dtype": "int32"}, {"name": "votesCountMinus", "dtype": "int32"}]}, {"name": "labels", "sequence": "string"}, {"name": "hubs", "sequence": "string"}, {"name": "flows", "sequence": "string"}, {"name": "tags", "sequence": "string"}, {"name": "reading_time", "dtype": "uint32"}, {"name": "format", "dtype": "string"}, {"name": "complexity", "dtype": "string"}, {"name": "comments", "sequence": [{"name": "id", "dtype": "uint64"}, {"name": "parent_id", "dtype": "uint64"}, {"name": "level", "dtype": "uint32"}, {"name": "time_published", "dtype": "uint64"}, {"name": "score", "dtype": "int32"}, {"name": "votes", "dtype": "uint32"}, {"name": "message_html", "dtype": "string"}, {"name": "message_markdown", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "children", "sequence": "uint64"}]}], "splits": [{"name": "train", "num_bytes": 19968161329, "num_examples": 302049}], "download_size": 3485570346, "dataset_size": 19968161329}}
2023-03-09T23:16:35+00:00
[]
[ "ru", "en" ]
TAGS #task_categories-text-generation #size_categories-100K<n<1M #language-Russian #language-English #region-us
# Habr dataset ## Table of Contents - Table of Contents - Description - Usage - Data Instances - Source Data - Personal and Sensitive Information ## Description Summary: Dataset of posts and comments from URL, a Russian collaborative blog about IT, computer science and anything related to the Internet. Script: create_habr.py Point of Contact: Ilya Gusev Languages: Russian, English, some programming code. ## Usage Prerequisites: Dataset iteration: ## Data Instances You can use this little helper to unflatten sequences: The original JSONL is already unflattened. ## Source Data * The data source is the Habr website. * API call example: post 709430. * Processing script is here. ## 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.
[ "# Habr dataset", "## Table of Contents\n- Table of Contents\n- Description\n- Usage\n- Data Instances\n- Source Data\n- Personal and Sensitive Information", "## Description\n\nSummary: Dataset of posts and comments from URL, a Russian collaborative blog about IT, computer science and anything related to the Internet.\n\nScript: create_habr.py\n\nPoint of Contact: Ilya Gusev\n\nLanguages: Russian, English, some programming code.", "## Usage\n\nPrerequisites:\n\n\nDataset iteration:", "## Data Instances\n\n\n\nYou can use this little helper to unflatten sequences:\n\n\n\nThe original JSONL is already unflattened.", "## Source Data\n\n* The data source is the Habr website.\n* API call example: post 709430.\n* Processing script is here.", "## 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-100K<n<1M #language-Russian #language-English #region-us \n", "# Habr dataset", "## Table of Contents\n- Table of Contents\n- Description\n- Usage\n- Data Instances\n- Source Data\n- Personal and Sensitive Information", "## Description\n\nSummary: Dataset of posts and comments from URL, a Russian collaborative blog about IT, computer science and anything related to the Internet.\n\nScript: create_habr.py\n\nPoint of Contact: Ilya Gusev\n\nLanguages: Russian, English, some programming code.", "## Usage\n\nPrerequisites:\n\n\nDataset iteration:", "## Data Instances\n\n\n\nYou can use this little helper to unflatten sequences:\n\n\n\nThe original JSONL is already unflattened.", "## Source Data\n\n* The data source is the Habr website.\n* API call example: post 709430.\n* Processing script is here.", "## 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." ]
[ 38, 5, 30, 59, 14, 32, 30, 42 ]
[ "passage: TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-Russian #language-English #region-us \n# Habr dataset## Table of Contents\n- Table of Contents\n- Description\n- Usage\n- Data Instances\n- Source Data\n- Personal and Sensitive Information## Description\n\nSummary: Dataset of posts and comments from URL, a Russian collaborative blog about IT, computer science and anything related to the Internet.\n\nScript: create_habr.py\n\nPoint of Contact: Ilya Gusev\n\nLanguages: Russian, English, some programming code.## Usage\n\nPrerequisites:\n\n\nDataset iteration:## Data Instances\n\n\n\nYou can use this little helper to unflatten sequences:\n\n\n\nThe original JSONL is already unflattened.## Source Data\n\n* The data source is the Habr website.\n* API call example: post 709430.\n* Processing script is here.## 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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1f8e8fd492a1bca73d6999f7ec3fd8366363ba78
from https://domains-index.com/
breadlicker45/100k-websites
[ "region:us" ]
2023-02-10T22:04:42+00:00
{}
2023-02-10T22:05:35+00:00
[]
[]
TAGS #region-us
from URL
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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fd4ca9902476c8a62edbda901d09ba63eedb830e
# Dataset Card for "mediasum-summary-matching" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
karukas/mediasum-summary-matching
[ "region:us" ]
2023-02-11T00:04:28+00:00
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4149687650, "num_examples": 443596}, {"name": "validation", "num_bytes": 92028438, "num_examples": 10000}, {"name": "test", "num_bytes": 94033599, "num_examples": 10000}], "download_size": 2438334598, "dataset_size": 4335749687}}
2023-02-11T00:05:53+00:00
[]
[]
TAGS #region-us
# Dataset Card for "mediasum-summary-matching" More Information needed
[ "# Dataset Card for \"mediasum-summary-matching\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"mediasum-summary-matching\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"mediasum-summary-matching\"\n\nMore Information needed" ]
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a73c22348d3238d79eb28e8a9769d8bd25ca2f0e
# Dataset Card for "VALUE_wikitext2_lexical" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/VALUE_wikitext2_lexical
[ "region:us" ]
2023-02-11T00:08:14+00:00
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "idx", "dtype": "int64"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 1220298, "num_examples": 1796}, {"name": "train", "num_bytes": 10702271, "num_examples": 15501}, {"name": "validation", "num_bytes": 1097319, "num_examples": 1604}], "download_size": 7724483, "dataset_size": 13019888}}
2023-02-11T00:08:19+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VALUE_wikitext2_lexical" More Information needed
[ "# Dataset Card for \"VALUE_wikitext2_lexical\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VALUE_wikitext2_lexical\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext2_lexical\"\n\nMore Information needed" ]
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6046ed39f5e7d14102388ff5dc4cc94c8826544b
# Dataset Card for grade-school-math-instructions OpenAI's [grade-school-math](https://github.com/openai/grade-school-math) dataset converted into instructions. ## Citation Information ```bibtex @article{cobbe2021gsm8k, title={Training Verifiers to Solve Math Word Problems}, author={Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and Hesse, Christopher and Schulman, John}, journal={arXiv preprint arXiv:2110.14168}, year={2021} } ```
qwedsacf/grade-school-math-instructions
[ "region:us" ]
2023-02-11T01:32:53+00:00
{"dataset_info": {"features": [{"name": "INSTRUCTION", "dtype": "string"}, {"name": "RESPONSE", "dtype": "string"}, {"name": "SOURCE", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4804916, "num_examples": 8792}], "download_size": 2554896, "dataset_size": 4804916}}
2023-02-11T01:59:26+00:00
[]
[]
TAGS #region-us
# Dataset Card for grade-school-math-instructions OpenAI's grade-school-math dataset converted into instructions.
[ "# Dataset Card for grade-school-math-instructions\n\nOpenAI's grade-school-math dataset converted into instructions." ]
[ "TAGS\n#region-us \n", "# Dataset Card for grade-school-math-instructions\n\nOpenAI's grade-school-math dataset converted into instructions." ]
[ 6, 29 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for grade-school-math-instructions\n\nOpenAI's grade-school-math dataset converted into instructions." ]
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fd6171cef49e0bae2af2b137e996766bcc0a271e
# Dataset Card for "VALUE_wikitext2_negative_inversion" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/VALUE_wikitext2_negative_inversion
[ "region:us" ]
2023-02-11T02:06:52+00:00
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 647, "num_examples": 1}, {"name": "train", "num_bytes": 1026, "num_examples": 1}, {"name": "validation", "num_bytes": 631, "num_examples": 1}], "download_size": 18138, "dataset_size": 2304}}
2023-02-11T02:06:57+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VALUE_wikitext2_negative_inversion" More Information needed
[ "# Dataset Card for \"VALUE_wikitext2_negative_inversion\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VALUE_wikitext2_negative_inversion\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext2_negative_inversion\"\n\nMore Information needed" ]
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c7bda74048748f55749cd663c3d8d1025a841fd9
# Dataset Card for H4 Stack Exchange Preferences Dataset ## Dataset Description - **Homepage:** https://archive.org/details/stackexchange - **Repository:** (private for now) https://github.com/huggingface/h4 - **Point of Contact:** Nathan Lambert, [email protected] - **Size of downloaded dataset:** 22.13 GB - **Number of instructions:** 10,741,532 ### Dataset Summary This dataset contains questions and answers from the [Stack Overflow Data Dump](https://archive.org/details/stackexchange) for the purpose of **preference model training**. Importantly, the questions have been filtered to fit the following criteria for preference models (following closely from [Askell et al. 2021](https://arxiv.org/abs/2112.00861)): *have >=2 answers*. This data could also be used for instruction fine-tuning and language model training. The questions are grouped with answers that are assigned a score corresponding to the Anthropic paper: ``` score = log2 (1 + upvotes) rounded to the nearest integer, plus 1 if the answer was accepted by the questioner (we assign a score of −1 if the number of upvotes is negative). ``` Some important notes when using this dataset for preference model pretraining (PMP), which can be ignored for other uses: * the data will likely need to be filtered more due to matching scores. * see section 4.1 of Askel et al 2021 for instructions on using each pair of samples twice via the following `binarization` (for better pre-training initialization): ``` Subsequently, we created a binary dataset by applying a ‘binarization’ procedure to the ranked dataset. That is, for every ranked pair A > B, we transform it into two independent binary comparisons: GOOD:A > BAD:A BAD:B > GOOD:B ``` To see all the stackexchanges used in this data, please see [this file](https://huggingface.co/datasets/HuggingFaceH4/pmp-stack-exchange/blob/main/stack_exchanges.json). Unfortunately, sharing the binarized data directly without metadata violates the license, so we have shared a script for binarization. ### Using the data Here is a script from our internal tooling used to create a binarized dataset: ``` # Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import random from argparse import ArgumentParser from pathlib import Path import numpy as np from datasets import Dataset, concatenate_datasets, load_dataset from h4.data.utils import save_dataset_shards H4_DIR = Path(__file__).resolve().parents[3] DATA_DIR = H4_DIR / "data" if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--debug", action="store_true", help="Added print statements / limit data size for debugging") parser.add_argument( "--output_dir", default=f"{DATA_DIR}/pmp-binarized", type=str, help="Where to save the processed dataset", ) parser.add_argument( "--exchange_name", type=str, default=None, help="Optional argument to specify a specific subsection of the dataset", ) parser.add_argument( "--binary_score", type=int, default=8, help="Score assigned to binarized pairs for preference data." ) parser.add_argument( "--stream_data", action="store_true", help="Optionally stream data, which can be useful with weaker computers" ) parser.set_defaults(debug=False, stream_data=False) # default will process full dataset args = parser.parse_args() specific_exchange = args.exchange_name stream_dataset = args.stream_data binary_score = args.binary_score if specific_exchange: data_dir = "data/" + args.exchange_name else: data_dir = None if args.debug: data_len_limit = 10000 else: data_len_limit = np.inf dataset = load_dataset( "HuggingFaceH4/pmp-stack-exchange", data_dir=data_dir, split="train", streaming=stream_dataset, ) pmp_data = [] for i, d in enumerate(iter(dataset)): # check debug limit, quit if in debug mode (don't save) if i > data_len_limit: print("Early exit for debug mode!") print(pmp_data) break question = d["question"] answers = d["answers"] num_answers = len(answers) answer_scores = [a["pm_score"] for a in answers] if len(np.unique(answer_scores)) < 2: print(f"PM Scores are {answer_scores}, skipping this question {i}") else: # Sample 2 unique scores for binarization dif_scores = False while not dif_scores: # print("infinite loop...?") two_answers = random.sample(answers, 2) if two_answers[0]["pm_score"] != two_answers[1]["pm_score"]: dif_scores = True answer_0 = two_answers[0] answer_1 = two_answers[1] text_0 = "Question: " + question + "\n" + "Answer: " + answer_0["text"] text_1 = "Question: " + question + "\n" + "Answer: " + answer_1["text"] score_0 = binary_score score_1 = binary_score pmp_data.append({"context": text_0, "score": score_0}) pmp_data.append({"context": text_1, "score": score_1}) # Save binarized data sublist_len = 100000 print(f"Dataset length is {len(pmp_data)}") # bypass known issue in arrow https://issues.apache.org/jira/browse/ARROW-17137 print(f"Processed dataset length > {sublist_len}, processing to HF dataset in chunks") chunks = [pmp_data[x : x + sublist_len] for x in range(0, len(pmp_data), sublist_len)] ds_chunks = [Dataset.from_list(ch) for ch in chunks] ds = concatenate_datasets(ds_chunks) save_dataset_shards(ds, args.output_dir, subset="stackexchange", shard_size="100MB") ``` ### Languages This is intended to be English only, thought other languages may be present. Some Stack Exchanges that are omitted include: ``` spanish: es.meta.stackoverflow.com, es.stackoverflow.com japanese: ja.meta.stackoverflow.com, ja.stackoverflow.com portugese: pt.stackoverflow.com, pt.meta.stackoverflow.com russian: ru.stackoverflow, ru.meta.stackoverflow ``` ### Licensing Information License: https://creativecommons.org/licenses/by-sa/4.0/ The cc-by-sa 4.0 licensing, while intentionally permissive, does require attribution: Attribution — You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). Specifically the attribution requirements are as follows: 1. Visually display or otherwise indicate the source of the content as coming from the Stack Exchange Network. This requirement is satisfied with a discreet text blurb, or some other unobtrusive but clear visual indication. 2. Ensure that any Internet use of the content includes a hyperlink directly to the original question on the source site on the Network (e.g., http://stackoverflow.com/questions/12345) 3. Visually display or otherwise clearly indicate the author names for every question and answer used 4. Ensure that any Internet use of the content includes a hyperlink for each author name directly back to his or her user profile page on the source site on the Network (e.g., http://stackoverflow.com/users/12345/username), directly to the Stack Exchange domain, in standard HTML (i.e. not through a Tinyurl or other such indirect hyperlink, form of obfuscation or redirection), without any “nofollow” command or any other such means of avoiding detection by search engines, and visible even with JavaScript disabled. For more information, see the Stack Exchange Terms of Service. ### Citation Information ``` @online{h4stackexchange, author = {Lambert, Nathan and Tunstall, Lewis and Rajani, Nazneen and Thrush, Tristan}, title = {HuggingFace H4 Stack Exchange Preference Dataset}, year = 2023, url = {https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences}, } ```
HuggingFaceH4/stack-exchange-preferences
[ "task_categories:question-answering", "size_categories:10M<n<100M", "language:en", "license:cc-by-sa-4.0", "RLHF", "preferences", "human-feedback", "Stack Exchange", "arxiv:2112.00861", "region:us" ]
2023-02-11T03:24:28+00:00
{"language": ["en"], "license": "cc-by-sa-4.0", "size_categories": ["10M<n<100M"], "task_categories": ["question-answering"], "pretty_name": "H4 Stack Exchange Preferences Dataset", "tags": ["RLHF", "preferences", "human-feedback", "Stack Exchange"], "download_size": 22132072448}
2023-03-08T03:37:53+00:00
[ "2112.00861" ]
[ "en" ]
TAGS #task_categories-question-answering #size_categories-10M<n<100M #language-English #license-cc-by-sa-4.0 #RLHF #preferences #human-feedback #Stack Exchange #arxiv-2112.00861 #region-us
# Dataset Card for H4 Stack Exchange Preferences Dataset ## Dataset Description - Homepage: URL - Repository: (private for now) URL - Point of Contact: Nathan Lambert, nathan@URL - Size of downloaded dataset: 22.13 GB - Number of instructions: 10,741,532 ### Dataset Summary This dataset contains questions and answers from the Stack Overflow Data Dump for the purpose of preference model training. Importantly, the questions have been filtered to fit the following criteria for preference models (following closely from Askell et al. 2021): *have >=2 answers*. This data could also be used for instruction fine-tuning and language model training. The questions are grouped with answers that are assigned a score corresponding to the Anthropic paper: Some important notes when using this dataset for preference model pretraining (PMP), which can be ignored for other uses: * the data will likely need to be filtered more due to matching scores. * see section 4.1 of Askel et al 2021 for instructions on using each pair of samples twice via the following 'binarization' (for better pre-training initialization): To see all the stackexchanges used in this data, please see this file. Unfortunately, sharing the binarized data directly without metadata violates the license, so we have shared a script for binarization. ### Using the data Here is a script from our internal tooling used to create a binarized dataset: ### Languages This is intended to be English only, thought other languages may be present. Some Stack Exchanges that are omitted include: ### Licensing Information License: URL The cc-by-sa 4.0 licensing, while intentionally permissive, does require attribution: Attribution — You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). Specifically the attribution requirements are as follows: 1. Visually display or otherwise indicate the source of the content as coming from the Stack Exchange Network. This requirement is satisfied with a discreet text blurb, or some other unobtrusive but clear visual indication. 2. Ensure that any Internet use of the content includes a hyperlink directly to the original question on the source site on the Network (e.g., URL 3. Visually display or otherwise clearly indicate the author names for every question and answer used 4. Ensure that any Internet use of the content includes a hyperlink for each author name directly back to his or her user profile page on the source site on the Network (e.g., URL directly to the Stack Exchange domain, in standard HTML (i.e. not through a Tinyurl or other such indirect hyperlink, form of obfuscation or redirection), without any “nofollow” command or any other such means of avoiding detection by search engines, and visible even with JavaScript disabled. For more information, see the Stack Exchange Terms of Service.
[ "# Dataset Card for H4 Stack Exchange Preferences Dataset", "## Dataset Description\n\n- Homepage: URL\n- Repository: (private for now) URL\n- Point of Contact: Nathan Lambert, nathan@URL\n- Size of downloaded dataset: 22.13 GB\n- Number of instructions: 10,741,532", "### Dataset Summary\n\nThis dataset contains questions and answers from the Stack Overflow Data Dump for the purpose of preference model training. \nImportantly, the questions have been filtered to fit the following criteria for preference models (following closely from Askell et al. 2021): *have >=2 answers*. \nThis data could also be used for instruction fine-tuning and language model training.\n\nThe questions are grouped with answers that are assigned a score corresponding to the Anthropic paper:\n\n\nSome important notes when using this dataset for preference model pretraining (PMP), which can be ignored for other uses: \n* the data will likely need to be filtered more due to matching scores.\n* see section 4.1 of Askel et al 2021 for instructions on using each pair of samples twice via the following 'binarization' (for better pre-training initialization):\n\n\nTo see all the stackexchanges used in this data, please see this file.\n\nUnfortunately, sharing the binarized data directly without metadata violates the license, so we have shared a script for binarization.", "### Using the data\nHere is a script from our internal tooling used to create a binarized dataset:", "### Languages\n\nThis is intended to be English only, thought other languages may be present. Some Stack Exchanges that are omitted include:", "### Licensing Information\n\nLicense: URL\n\nThe cc-by-sa 4.0 licensing, while intentionally permissive, does require attribution:\n\nAttribution — You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work).\nSpecifically the attribution requirements are as follows:\n1. Visually display or otherwise indicate the source of the content as coming from the Stack Exchange Network. This requirement is satisfied with a discreet text blurb, or some other unobtrusive but clear visual indication.\n2. Ensure that any Internet use of the content includes a hyperlink directly to the original question on the source site on the Network (e.g., URL\n3. Visually display or otherwise clearly indicate the author names for every question and answer used\n4. Ensure that any Internet use of the content includes a hyperlink for each author name directly back to his or her user profile page on the source site on the Network (e.g., URL directly to the Stack Exchange domain, in standard HTML (i.e. not through a Tinyurl or other such indirect hyperlink, form of obfuscation or redirection), without any “nofollow” command or any other such means of avoiding detection by search engines, and visible even with JavaScript disabled.\nFor more information, see the Stack Exchange Terms of Service." ]
[ "TAGS\n#task_categories-question-answering #size_categories-10M<n<100M #language-English #license-cc-by-sa-4.0 #RLHF #preferences #human-feedback #Stack Exchange #arxiv-2112.00861 #region-us \n", "# Dataset Card for H4 Stack Exchange Preferences Dataset", "## Dataset Description\n\n- Homepage: URL\n- Repository: (private for now) URL\n- Point of Contact: Nathan Lambert, nathan@URL\n- Size of downloaded dataset: 22.13 GB\n- Number of instructions: 10,741,532", "### Dataset Summary\n\nThis dataset contains questions and answers from the Stack Overflow Data Dump for the purpose of preference model training. \nImportantly, the questions have been filtered to fit the following criteria for preference models (following closely from Askell et al. 2021): *have >=2 answers*. \nThis data could also be used for instruction fine-tuning and language model training.\n\nThe questions are grouped with answers that are assigned a score corresponding to the Anthropic paper:\n\n\nSome important notes when using this dataset for preference model pretraining (PMP), which can be ignored for other uses: \n* the data will likely need to be filtered more due to matching scores.\n* see section 4.1 of Askel et al 2021 for instructions on using each pair of samples twice via the following 'binarization' (for better pre-training initialization):\n\n\nTo see all the stackexchanges used in this data, please see this file.\n\nUnfortunately, sharing the binarized data directly without metadata violates the license, so we have shared a script for binarization.", "### Using the data\nHere is a script from our internal tooling used to create a binarized dataset:", "### Languages\n\nThis is intended to be English only, thought other languages may be present. Some Stack Exchanges that are omitted include:", "### Licensing Information\n\nLicense: URL\n\nThe cc-by-sa 4.0 licensing, while intentionally permissive, does require attribution:\n\nAttribution — You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work).\nSpecifically the attribution requirements are as follows:\n1. Visually display or otherwise indicate the source of the content as coming from the Stack Exchange Network. This requirement is satisfied with a discreet text blurb, or some other unobtrusive but clear visual indication.\n2. Ensure that any Internet use of the content includes a hyperlink directly to the original question on the source site on the Network (e.g., URL\n3. Visually display or otherwise clearly indicate the author names for every question and answer used\n4. Ensure that any Internet use of the content includes a hyperlink for each author name directly back to his or her user profile page on the source site on the Network (e.g., URL directly to the Stack Exchange domain, in standard HTML (i.e. not through a Tinyurl or other such indirect hyperlink, form of obfuscation or redirection), without any “nofollow” command or any other such means of avoiding detection by search engines, and visible even with JavaScript disabled.\nFor more information, see the Stack Exchange Terms of Service." ]
[ 70, 14, 53, 240, 25, 32, 304 ]
[ "passage: TAGS\n#task_categories-question-answering #size_categories-10M<n<100M #language-English #license-cc-by-sa-4.0 #RLHF #preferences #human-feedback #Stack Exchange #arxiv-2112.00861 #region-us \n# Dataset Card for H4 Stack Exchange Preferences Dataset## Dataset Description\n\n- Homepage: URL\n- Repository: (private for now) URL\n- Point of Contact: Nathan Lambert, nathan@URL\n- Size of downloaded dataset: 22.13 GB\n- Number of instructions: 10,741,532### Dataset Summary\n\nThis dataset contains questions and answers from the Stack Overflow Data Dump for the purpose of preference model training. \nImportantly, the questions have been filtered to fit the following criteria for preference models (following closely from Askell et al. 2021): *have >=2 answers*. \nThis data could also be used for instruction fine-tuning and language model training.\n\nThe questions are grouped with answers that are assigned a score corresponding to the Anthropic paper:\n\n\nSome important notes when using this dataset for preference model pretraining (PMP), which can be ignored for other uses: \n* the data will likely need to be filtered more due to matching scores.\n* see section 4.1 of Askel et al 2021 for instructions on using each pair of samples twice via the following 'binarization' (for better pre-training initialization):\n\n\nTo see all the stackexchanges used in this data, please see this file.\n\nUnfortunately, sharing the binarized data directly without metadata violates the license, so we have shared a script for binarization.### Using the data\nHere is a script from our internal tooling used to create a binarized dataset:### Languages\n\nThis is intended to be English only, thought other languages may be present. Some Stack Exchanges that are omitted include:" ]
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bb0218238f3bbf87889870b52227e2f7a0f69c69
# Dataset Card for "restaurant_order_local_test_colab" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
figfig/restaurant_order_local_test_colab
[ "region:us" ]
2023-02-11T05:12:53+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 407597.0, "num_examples": 3}, {"name": "test", "num_bytes": 407597.0, "num_examples": 3}], "download_size": 818170, "dataset_size": 815194.0}}
2023-02-11T05:13:36+00:00
[]
[]
TAGS #region-us
# Dataset Card for "restaurant_order_local_test_colab" More Information needed
[ "# Dataset Card for \"restaurant_order_local_test_colab\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"restaurant_order_local_test_colab\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"restaurant_order_local_test_colab\"\n\nMore Information needed" ]
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6b996a4f9455cf7960b1c89cf3c05b80791c727b
# Dataset Card for "RuDevSberDS" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MegaKosT/RuDevSberDS
[ "region:us" ]
2023-02-11T06:27:49+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5069939576.368, "num_examples": 43576}, {"name": "test", "num_bytes": 1096369193.45, "num_examples": 9225}, {"name": "validation", "num_bytes": 2231862564.3, "num_examples": 18450}], "download_size": 8198117805, "dataset_size": 8398171334.118}}
2023-02-11T06:50:40+00:00
[]
[]
TAGS #region-us
# Dataset Card for "RuDevSberDS" More Information needed
[ "# Dataset Card for \"RuDevSberDS\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"RuDevSberDS\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"RuDevSberDS\"\n\nMore Information needed" ]
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5ad70f98bd30e4c70283c11f03b46b4f47729d16
# AutoTrain Dataset for project: flan-xl-conversation ## Dataset Description This dataset has been automatically processed by AutoTrain for project flan-xl-conversation. ### 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 [ { "text": "What is the largest insect in the world?", "target": "The largest insect in the world is the Goliath Beetle." }, { "text": "What is the largest amphibian in the world?", "target": "The largest amphibian in the world is the Chinese giant salamander." } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "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 | 158 | | valid | 40 |
Jonnylaw/autotrain-data-flan-xl-conversation
[ "task_categories:summarization", "region:us" ]
2023-02-11T07:07:13+00:00
{"task_categories": ["summarization"]}
2023-02-11T07:07:39+00:00
[]
[]
TAGS #task_categories-summarization #region-us
AutoTrain Dataset for project: flan-xl-conversation =================================================== Dataset Description ------------------- This dataset has been automatically processed by AutoTrain for project flan-xl-conversation. ### 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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04be7aaff8d903a559f7248b0363e4b33a76b90c
# AutoTrain Dataset for project: flan-large-conv ## Dataset Description This dataset has been automatically processed by AutoTrain for project flan-large-conv. ### 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 [ { "text": "What is the largest insect in the world?", "target": "The largest insect in the world is the Goliath Beetle." }, { "text": "What is the largest amphibian in the world?", "target": "The largest amphibian in the world is the Chinese giant salamander." } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "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 | 158 | | valid | 40 |
Jonnylaw/chat-conversation-trainer
[ "task_categories:summarization", "region:us" ]
2023-02-11T07:19:28+00:00
{"task_categories": ["summarization"]}
2023-02-11T07:19:50+00:00
[]
[]
TAGS #task_categories-summarization #region-us
AutoTrain Dataset for project: flan-large-conv ============================================== Dataset Description ------------------- This dataset has been automatically processed by AutoTrain for project flan-large-conv. ### 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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19cd5192aef3a4568775ebedf7e1f2f2f2b5c939
# Dataset Card for "Fin_Corpus_EarningCall" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
FINDA-FIT/Fin_Corpus_EarningCall
[ "region:us" ]
2023-02-11T07:31:37+00:00
{"dataset_info": {"features": [{"name": "ID", "dtype": "string"}, {"name": "CONTEXT", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8724423352, "num_examples": 234119}], "download_size": 4615593313, "dataset_size": 8724423352}}
2023-02-14T04:58:13+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Fin_Corpus_EarningCall" More Information needed
[ "# Dataset Card for \"Fin_Corpus_EarningCall\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Fin_Corpus_EarningCall\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Fin_Corpus_EarningCall\"\n\nMore Information needed" ]
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d18c42bb45965f6ce0a6402ec26551a8fa5a60e3
# EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients. This repository contains the data of the second version of the paper [EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients](https://arxiv.org/pdf/2201.09699.pdf).
ybendou/easy-v2
[ "license:apache-2.0", "arxiv:2201.09699", "region:us" ]
2023-02-11T07:43:49+00:00
{"license": "apache-2.0"}
2023-02-11T19:57:55+00:00
[ "2201.09699" ]
[]
TAGS #license-apache-2.0 #arxiv-2201.09699 #region-us
# EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients. This repository contains the data of the second version of the paper EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients.
[ "# EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients.\nThis repository contains the data of the second version of the paper EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients." ]
[ "TAGS\n#license-apache-2.0 #arxiv-2201.09699 #region-us \n", "# EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients.\nThis repository contains the data of the second version of the paper EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients." ]
[ 23, 88 ]
[ "passage: TAGS\n#license-apache-2.0 #arxiv-2201.09699 #region-us \n# EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients.\nThis repository contains the data of the second version of the paper EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients." ]
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78341365858aa84623265c276997f93339021c71
# Dataset Card for "deprem_satellite_semantic_whu_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
deprem-ml/deprem_satellite_semantic_whu_dataset
[ "region:us" ]
2023-02-11T10:34:24+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3110422171.552, "num_examples": 4736}, {"name": "validation", "num_bytes": 659257774.42, "num_examples": 1036}, {"name": "test", "num_bytes": 307212582.0, "num_examples": 500}], "download_size": 4016000111, "dataset_size": 4076892527.972}}
2023-02-11T10:49:51+00:00
[]
[]
TAGS #region-us
# Dataset Card for "deprem_satellite_semantic_whu_dataset" More Information needed
[ "# Dataset Card for \"deprem_satellite_semantic_whu_dataset\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"deprem_satellite_semantic_whu_dataset\"\n\nMore Information needed" ]
[ 6, 25 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"deprem_satellite_semantic_whu_dataset\"\n\nMore Information needed" ]
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77d2b9f9f5d596ab0316996e4c1eccbc0dd053cc
# Dataset Card for "tiktok-people" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yaeldekel/tiktok-people
[ "region:us" ]
2023-02-11T10:55:36+00:00
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1093773.0, "num_examples": 3}], "download_size": 1095552, "dataset_size": 1093773.0}}
2023-02-11T10:55:45+00:00
[]
[]
TAGS #region-us
# Dataset Card for "tiktok-people" More Information needed
[ "# Dataset Card for \"tiktok-people\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"tiktok-people\"\n\nMore Information needed" ]
[ 6, 14 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"tiktok-people\"\n\nMore Information needed" ]
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e571779bd04d3a09113d8cc6f4206912dec2cbcc
# Dataset Card for "wikipedia_stage2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MartinKu/wikipedia_stage2
[ "region:us" ]
2023-02-11T14:38:47+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "S_V", "sequence": "string"}, {"name": "S_V_position", "sequence": "int64"}, {"name": "O_C", "sequence": "string"}, {"name": "O_C_position", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 45092871426, "num_examples": 6458670}], "download_size": 25091808148, "dataset_size": 45092871426}}
2023-02-14T13:43:57+00:00
[]
[]
TAGS #region-us
# Dataset Card for "wikipedia_stage2" More Information needed
[ "# Dataset Card for \"wikipedia_stage2\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"wikipedia_stage2\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"wikipedia_stage2\"\n\nMore Information needed" ]
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a622fdab5491246125756a2c39cab72fb50caf03
# Dataset Card for Dataset kotlin_code ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This Dataset contains Kotlin functions with there documentation. This dataset can be useful in fine-tuning or creating new models for developing models which can generate the code documentaiton ### 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]
codkiller0911/kotlin_code
[ "size_categories:1K<n<10K", "language:en", "kotlin", "android", "region:us" ]
2023-02-11T14:39:47+00:00
{"language": ["en"], "size_categories": ["1K<n<10K"], "tags": ["kotlin", "android"]}
2023-02-11T16:42:21+00:00
[]
[ "en" ]
TAGS #size_categories-1K<n<10K #language-English #kotlin #android #region-us
# Dataset Card for Dataset kotlin_code ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This Dataset contains Kotlin functions with there documentation. This dataset can be useful in fine-tuning or creating new models for developing models which can generate the code documentaiton ### 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 kotlin_code", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis Dataset contains Kotlin functions with there documentation. This dataset can be useful in fine-tuning or creating new models for developing models which can generate the code documentaiton", "### 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#size_categories-1K<n<10K #language-English #kotlin #android #region-us \n", "# Dataset Card for Dataset kotlin_code", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis Dataset contains Kotlin functions with there documentation. This dataset can be useful in fine-tuning or creating new models for developing models which can generate the code documentaiton", "### 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" ]
[ 28, 11, 24, 45, 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#size_categories-1K<n<10K #language-English #kotlin #android #region-us \n# Dataset Card for Dataset kotlin_code## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis Dataset contains Kotlin functions with there documentation. This dataset can be useful in fine-tuning or creating new models for developing models which can generate the code documentaiton### 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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6755eda595f7d32fc815c8107dee27c733c866a7
# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Dataset contains pairs of sentences with next_sentence_label for NSP. Sentences was given from public jira projects dataset. Next sentence is always next sentence in one comment or sentence from reply to the comment. ### Supported Tasks and Leaderboards NSP, MLM ### Languages English ## Dataset Structure sentence_a, sentence_b, next_sentence_label ### Source Data https://zenodo.org/record/5901804#.Y_Xv4HZBxD9
pheepa/jira-comments-nsp
[ "task_categories:text-generation", "size_categories:1M<n<10M", "language:en", "jira", "region:us" ]
2023-02-11T14:43:28+00:00
{"language": ["en"], "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "pretty_name": "jira-comments", "tags": ["jira"]}
2023-02-22T10:37:20+00:00
[]
[ "en" ]
TAGS #task_categories-text-generation #size_categories-1M<n<10M #language-English #jira #region-us
# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary Dataset contains pairs of sentences with next_sentence_label for NSP. Sentences was given from public jira projects dataset. Next sentence is always next sentence in one comment or sentence from reply to the comment. ### Supported Tasks and Leaderboards NSP, MLM ### Languages English ## Dataset Structure sentence_a, sentence_b, next_sentence_label ### Source Data URL
[ "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nDataset contains pairs of sentences with next_sentence_label for NSP. Sentences was given from public jira projects dataset. Next sentence is always next sentence in one comment or sentence from reply to the comment.", "### Supported Tasks and Leaderboards\n\nNSP, MLM", "### Languages\n\nEnglish", "## Dataset Structure\n\nsentence_a, sentence_b, next_sentence_label", "### Source Data\n\nURL" ]
[ "TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-English #jira #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\n\nDataset contains pairs of sentences with next_sentence_label for NSP. Sentences was given from public jira projects dataset. Next sentence is always next sentence in one comment or sentence from reply to the comment.", "### Supported Tasks and Leaderboards\n\nNSP, MLM", "### Languages\n\nEnglish", "## Dataset Structure\n\nsentence_a, sentence_b, next_sentence_label", "### Source Data\n\nURL" ]
[ 36, 8, 24, 54, 15, 5, 20, 5 ]
[ "passage: TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-English #jira #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\n\nDataset contains pairs of sentences with next_sentence_label for NSP. Sentences was given from public jira projects dataset. Next sentence is always next sentence in one comment or sentence from reply to the comment.### Supported Tasks and Leaderboards\n\nNSP, MLM### Languages\n\nEnglish## Dataset Structure\n\nsentence_a, sentence_b, next_sentence_label### Source Data\n\nURL" ]
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b1296aef91bb33dc580c8582da4cf3cad4004b07
# Lora - hanboka-000003 ## Dataset Description - **원본** [한복 lora](https://arca.live/b/aiart/69417775) 한복 로라 파일 프롬프트에 hanbok, korean clothes 가중치 0.8 권장 [다운로드](https://huggingface.co/datasets/AIARTCHAN/lora-hanboka-000003/resolve/main/hanboka-000003.safetensors)
AIARTCHAN/lora-hanboka-000003
[ "license:creativeml-openrail-m", "lora", "aiartchan", "stable-diffusion", "region:us" ]
2023-02-11T15:55:24+00:00
{"license": "creativeml-openrail-m", "tags": ["lora", "aiartchan", "stable-diffusion"]}
2023-02-11T16:02:16+00:00
[]
[]
TAGS #license-creativeml-openrail-m #lora #aiartchan #stable-diffusion #region-us
# Lora - hanboka-000003 ## Dataset Description - 원본 한복 lora 한복 로라 파일 프롬프트에 hanbok, korean clothes 가중치 0.8 권장 다운로드
[ "# Lora - hanboka-000003", "## Dataset Description\n\n- 원본 한복 lora\n\n한복 로라 파일\n\n프롬프트에 hanbok, korean clothes\n\n가중치 0.8 권장\n\n다운로드" ]
[ "TAGS\n#license-creativeml-openrail-m #lora #aiartchan #stable-diffusion #region-us \n", "# Lora - hanboka-000003", "## Dataset Description\n\n- 원본 한복 lora\n\n한복 로라 파일\n\n프롬프트에 hanbok, korean clothes\n\n가중치 0.8 권장\n\n다운로드" ]
[ 32, 9, 35 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #lora #aiartchan #stable-diffusion #region-us \n# Lora - hanboka-000003## Dataset Description\n\n- 원본 한복 lora\n\n한복 로라 파일\n\n프롬프트에 hanbok, korean clothes\n\n가중치 0.8 권장\n\n다운로드" ]
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33765d9f7f731aa69fa795c803718db10be4e0fc
# Lora - Hanbok_LoRA_V2 ## Dataset Description - **원본** [한복 로라 다시 만들어왔음](https://arca.live/b/aiart/69505242) 한복 로라 파일 예시 프롬프트 ``` (masterpiece, best quality:1.2), 1girl, solo, <lora:Hanbok_LoRA_V2:0.8>, hanbok, korean_clothes, smile, best ratio four finger and one thumb, looking_at_viewer, cowboy_shot, (white background), (simple background) Negative prompt: EasyNegative, extra fingers,fewer fingers, watermark, text, animal_ears, japanese_clothes, kimono, chinese_clothes ``` ``` Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 640x960, Model: Counterfeit-V2.5 Denoising strength: 0.6, Clip skip: 2, Hires upscale: 1.5, Hires upscaler: Latent ``` 가중치 0.8 권장 [다운로드](https://huggingface.co/datasets/AIARTCHAN/lora-Hanbok_LoRA_V2/resolve/main/Hanbok_LoRA_V2.safetensors)
AIARTCHAN/lora-Hanbok_LoRA_V2
[ "license:creativeml-openrail-m", "lora", "aiartchan", "stable-diffusion", "region:us" ]
2023-02-11T16:13:21+00:00
{"license": "creativeml-openrail-m", "tags": ["lora", "aiartchan", "stable-diffusion"]}
2023-02-11T16:15:47+00:00
[]
[]
TAGS #license-creativeml-openrail-m #lora #aiartchan #stable-diffusion #region-us
# Lora - Hanbok_LoRA_V2 ## Dataset Description - 원본 한복 로라 다시 만들어왔음 한복 로라 파일 예시 프롬프트 가중치 0.8 권장 다운로드
[ "# Lora - Hanbok_LoRA_V2", "## Dataset Description\n\n- 원본 한복 로라 다시 만들어왔음\n\n한복 로라 파일\n\n예시 프롬프트\n\n\n\n\n\n가중치 0.8 권장\n\n다운로드" ]
[ "TAGS\n#license-creativeml-openrail-m #lora #aiartchan #stable-diffusion #region-us \n", "# Lora - Hanbok_LoRA_V2", "## Dataset Description\n\n- 원본 한복 로라 다시 만들어왔음\n\n한복 로라 파일\n\n예시 프롬프트\n\n\n\n\n\n가중치 0.8 권장\n\n다운로드" ]
[ 32, 12, 34 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #lora #aiartchan #stable-diffusion #region-us \n# Lora - Hanbok_LoRA_V2## Dataset Description\n\n- 원본 한복 로라 다시 만들어왔음\n\n한복 로라 파일\n\n예시 프롬프트\n\n\n\n\n\n가중치 0.8 권장\n\n다운로드" ]
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f744a6ae04b17e94d5788ed380177a03f44043c8
# Dataset Card for "restaurant_order_HSR_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
figfig/restaurant_order_HSR_test
[ "region:us" ]
2023-02-11T17:29:22+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 267265.0, "num_examples": 6}, {"name": "test", "num_bytes": 267265.0, "num_examples": 6}], "download_size": 537202, "dataset_size": 534530.0}}
2023-02-11T19:45:43+00:00
[]
[]
TAGS #region-us
# Dataset Card for "restaurant_order_HSR_test" More Information needed
[ "# Dataset Card for \"restaurant_order_HSR_test\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"restaurant_order_HSR_test\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"restaurant_order_HSR_test\"\n\nMore Information needed" ]
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b76521e6f581c3a3dfa482cb4476cc708e9df34f
# Dataset Card for [Dataset Name] ## 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) ## Dataset Description - **Homepage:** https://huggingface.co/datasets/RayhanADev/replit-comments-categorized - **Repository:** https://huggingface.co/datasets/RayhanADev/replit-comments-categorized - **Point of Contact:** [email protected] ### Dataset Summary Comments from [Replit](https://replit.com/)'s Community, sourced via moderator GraphQL queries and personally labeled :). For use in Replit + Weights and Biases Hackathon. ### Supported Tasks and Leaderboards Text Classification ### Languages English ## Dataset Structure ### Data Instances ```json {"label":3,"text":"@KENDALPETERSON\nShut up you dont have a permit to brag."} ``` Labels - 0: General - 1: Spam - 2: NSFW - 3: Harassment ### Data Fields Label, Text ### Data Splits Train, Validation, Test ## Dataset Creation ### Curation Rationale Fine-tuning data for the Replit + Weights and Biases Hackathon. ### Source Data #### Initial Data Collection and Normalization This data was collected via Replit's GraphQL API using a query only available to site moderators and admins, allowing for querying comments by pattern. By not setting a pattern one could get up to 50 comments within a given time span. Data was sourced from February 1st, 2023 to February 10th, 2023. #### Who are the source language producers? Replit Community users ### Annotations #### Annotation process I tagged the data using a small website that I made to assign labels to individual comments. ![](https://media.furret.dev/JKnDLTKMqF) #### Who are the annotators? Me ([@rayhanadev](https://www.furret.dev)) ### Personal and Sensitive Information This dataset is purely unfiltered and may contain personal and sensitive information. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases The data is categorized according to three categories of content that are not allowed on Replit (and would fall into a fourth "general" category if it is acceptable content). ### Other Known Limitations The dataset is small (n<1k) but I am working on increasing the amount of available data. There is also significantly more unacceptable content labelled than there is acceptable ("general") content. ## Additional Information ### Dataset Curators Me ([@rayhanadev](https://www.furret.dev)) ### Licensing Information This data is licensed under MIT but Replit or any concerned parties is free to issue a takedown of this dataset. Any person who's data is available in this dataset is free to request that their data is deleted, however it is not a breach of Replit's data use policy for this data to be available.
RayhanADev/replit-comments-categorized
[ "task_categories:text-classification", "task_ids:intent-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:mit", "replit", "comments", "forum", "chat", "intent", "classification", "doi:10.57967/hf/0351", "region:us" ]
2023-02-11T18:25:37+00:00
{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["intent-classification"], "pretty_name": "Replit Comments Categorized", "tags": ["replit", "comments", "forum", "chat", "intent", "classification"]}
2023-02-11T22:17:51+00:00
[]
[ "en" ]
TAGS #task_categories-text-classification #task_ids-intent-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-mit #replit #comments #forum #chat #intent #classification #doi-10.57967/hf/0351 #region-us
# Dataset Card for [Dataset Name] ## 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 ## Dataset Description - Homepage: URL - Repository: URL - Point of Contact: ray@URL ### Dataset Summary Comments from Replit's Community, sourced via moderator GraphQL queries and personally labeled :). For use in Replit + Weights and Biases Hackathon. ### Supported Tasks and Leaderboards Text Classification ### Languages English ## Dataset Structure ### Data Instances Labels - 0: General - 1: Spam - 2: NSFW - 3: Harassment ### Data Fields Label, Text ### Data Splits Train, Validation, Test ## Dataset Creation ### Curation Rationale Fine-tuning data for the Replit + Weights and Biases Hackathon. ### Source Data #### Initial Data Collection and Normalization This data was collected via Replit's GraphQL API using a query only available to site moderators and admins, allowing for querying comments by pattern. By not setting a pattern one could get up to 50 comments within a given time span. Data was sourced from February 1st, 2023 to February 10th, 2023. #### Who are the source language producers? Replit Community users ### Annotations #### Annotation process I tagged the data using a small website that I made to assign labels to individual comments. ![](URL #### Who are the annotators? Me (@rayhanadev) ### Personal and Sensitive Information This dataset is purely unfiltered and may contain personal and sensitive information. ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases The data is categorized according to three categories of content that are not allowed on Replit (and would fall into a fourth "general" category if it is acceptable content). ### Other Known Limitations The dataset is small (n<1k) but I am working on increasing the amount of available data. There is also significantly more unacceptable content labelled than there is acceptable ("general") content. ## Additional Information ### Dataset Curators Me (@rayhanadev) ### Licensing Information This data is licensed under MIT but Replit or any concerned parties is free to issue a takedown of this dataset. Any person who's data is available in this dataset is free to request that their data is deleted, however it is not a breach of Replit's data use policy for this data to be available.
[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- 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", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Point of Contact: ray@URL", "### Dataset Summary\n\nComments from Replit's Community, sourced via moderator GraphQL queries and personally labeled :). For use in Replit + Weights and Biases Hackathon.", "### Supported Tasks and Leaderboards\n\nText Classification", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\n\n\nLabels\n- 0: General\n- 1: Spam\n- 2: NSFW\n- 3: Harassment", "### Data Fields\n\nLabel, Text", "### Data Splits\n\nTrain, Validation, Test", "## Dataset Creation", "### Curation Rationale\n\nFine-tuning data for the Replit + Weights and Biases Hackathon.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThis data was collected via Replit's GraphQL API using a query only available to site moderators and admins, allowing for querying comments by pattern. By not setting a\npattern one could get up to 50 comments within a given time span. Data was sourced from February 1st, 2023 to February 10th, 2023.", "#### Who are the source language producers?\n\nReplit Community users", "### Annotations", "#### Annotation process\n\nI tagged the data using a small website that I made to assign labels to individual comments.\n\n![](URL", "#### Who are the annotators?\n\nMe (@rayhanadev)", "### Personal and Sensitive Information\n\nThis dataset is purely unfiltered and may contain personal and sensitive information.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\nThe data is categorized according to three categories of content that are not allowed on Replit (and would fall into a fourth \"general\" category if it is acceptable\ncontent).", "### Other Known Limitations\n\nThe dataset is small (n<1k) but I am working on increasing the amount of available data. There is also significantly more unacceptable content labelled than there is\nacceptable (\"general\") content.", "## Additional Information", "### Dataset Curators\n\nMe (@rayhanadev)", "### Licensing Information\n\nThis data is licensed under MIT but Replit or any concerned parties is free to issue a takedown of this dataset. Any person who's data is available in this dataset is free\nto request that their data is deleted, however it is not a breach of Replit's data use policy for this data to be available." ]
[ "TAGS\n#task_categories-text-classification #task_ids-intent-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-mit #replit #comments #forum #chat #intent #classification #doi-10.57967/hf/0351 #region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- 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", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Point of Contact: ray@URL", "### Dataset Summary\n\nComments from Replit's Community, sourced via moderator GraphQL queries and personally labeled :). For use in Replit + Weights and Biases Hackathon.", "### Supported Tasks and Leaderboards\n\nText Classification", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances\n\n\n\nLabels\n- 0: General\n- 1: Spam\n- 2: NSFW\n- 3: Harassment", "### Data Fields\n\nLabel, Text", "### Data Splits\n\nTrain, Validation, Test", "## Dataset Creation", "### Curation Rationale\n\nFine-tuning data for the Replit + Weights and Biases Hackathon.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThis data was collected via Replit's GraphQL API using a query only available to site moderators and admins, allowing for querying comments by pattern. By not setting a\npattern one could get up to 50 comments within a given time span. Data was sourced from February 1st, 2023 to February 10th, 2023.", "#### Who are the source language producers?\n\nReplit Community users", "### Annotations", "#### Annotation process\n\nI tagged the data using a small website that I made to assign labels to individual comments.\n\n![](URL", "#### Who are the annotators?\n\nMe (@rayhanadev)", "### Personal and Sensitive Information\n\nThis dataset is purely unfiltered and may contain personal and sensitive information.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\nThe data is categorized according to three categories of content that are not allowed on Replit (and would fall into a fourth \"general\" category if it is acceptable\ncontent).", "### Other Known Limitations\n\nThe dataset is small (n<1k) but I am working on increasing the amount of available data. There is also significantly more unacceptable content labelled than there is\nacceptable (\"general\") content.", "## Additional Information", "### Dataset Curators\n\nMe (@rayhanadev)", "### Licensing Information\n\nThis data is licensed under MIT but Replit or any concerned parties is free to issue a takedown of this dataset. Any person who's data is available in this dataset is free\nto request that their data is deleted, however it is not a breach of Replit's data use policy for this data to be available." ]
[ 112, 10, 117, 22, 46, 13, 5, 6, 25, 8, 12, 5, 27, 4, 84, 14, 5, 29, 15, 25, 8, 7, 45, 50, 5, 12, 77 ]
[ "passage: TAGS\n#task_categories-text-classification #task_ids-intent-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-mit #replit #comments #forum #chat #intent #classification #doi-10.57967/hf/0351 #region-us \n# Dataset Card for [Dataset Name]## Table of Contents\n- 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## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Point of Contact: ray@URL### Dataset Summary\n\nComments from Replit's Community, sourced via moderator GraphQL queries and personally labeled :). For use in Replit + Weights and Biases Hackathon.### Supported Tasks and Leaderboards\n\nText Classification### Languages\n\nEnglish## Dataset Structure### Data Instances\n\n\n\nLabels\n- 0: General\n- 1: Spam\n- 2: NSFW\n- 3: Harassment### Data Fields\n\nLabel, Text### Data Splits\n\nTrain, Validation, Test## Dataset Creation### Curation Rationale\n\nFine-tuning data for the Replit + Weights and Biases Hackathon.### Source Data#### Initial Data Collection and Normalization\n\nThis data was collected via Replit's GraphQL API using a query only available to site moderators and admins, allowing for querying comments by pattern. By not setting a\npattern one could get up to 50 comments within a given time span. Data was sourced from February 1st, 2023 to February 10th, 2023." ]
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30094805ea29d75b542d6c3f0288e4560a9fa13a
# Dataset Card for "mc4_es_cl" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jorgeortizfuentes/mc4_es_cl
[ "region:us" ]
2023-02-11T18:30:50+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "timestamp", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 33474281363.716232, "num_examples": 8698099}, {"name": "validation", "num_bytes": 33072762.075400714, "num_examples": 8582}], "download_size": 15903346209, "dataset_size": 33507354125.791634}}
2023-02-12T01:10:09+00:00
[]
[]
TAGS #region-us
# Dataset Card for "mc4_es_cl" More Information needed
[ "# Dataset Card for \"mc4_es_cl\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"mc4_es_cl\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"mc4_es_cl\"\n\nMore Information needed" ]
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44069e5a58a02cd0aae1e8af2a625c6d2ebb4f03
# Dataset Card for "MedQA-USMLE-4-options-hf-MiniLM-IR-cs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
GBaker/MedQA-USMLE-4-options-hf-MiniLM-IR-cs
[ "region:us" ]
2023-02-11T18:45:57+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sent1", "dtype": "string"}, {"name": "sent2", "dtype": "string"}, {"name": "ending0", "dtype": "string"}, {"name": "ending1", "dtype": "string"}, {"name": "ending2", "dtype": "string"}, {"name": "ending3", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 1933180, "num_examples": 1273}, {"name": "validation", "num_bytes": 1905261, "num_examples": 1272}, {"name": "train", "num_bytes": 15360790, "num_examples": 10178}], "download_size": 11125239, "dataset_size": 19199231}}
2023-02-11T23:26:10+00:00
[]
[]
TAGS #region-us
# Dataset Card for "MedQA-USMLE-4-options-hf-MiniLM-IR-cs" More Information needed
[ "# Dataset Card for \"MedQA-USMLE-4-options-hf-MiniLM-IR-cs\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"MedQA-USMLE-4-options-hf-MiniLM-IR-cs\"\n\nMore Information needed" ]
[ 6, 30 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"MedQA-USMLE-4-options-hf-MiniLM-IR-cs\"\n\nMore Information needed" ]
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216cacef201c58138a9b8b223ae62f424b1c20e1
# AutoTrain Dataset for project: exacts ## Dataset Description This dataset has been automatically processed by AutoTrain for project exacts. ### 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 [ { "text": "How do plants adapt to different environments?", "target": "Plants adapt to different environments through a variety of mechanisms. Some examples include developing deep roots to access water in dry regions, growing smaller leaves to reduce water loss in hot regions, or producing chemicals to deter herbivores in regions with high herbivore populations." }, { "text": "How do volcanoes erupt?", "target": "Volcanoes erupt when pressure from molten rock, ash, and gas inside the volcano builds up and eventually forces its way to the surface. This can happen through a volcano's central vent or through fissures on its slopes." } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "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 | 156 | | valid | 39 |
Jonnylaw/questioning-dataset
[ "task_categories:summarization", "region:us" ]
2023-02-11T19:39:14+00:00
{"task_categories": ["summarization"]}
2023-02-11T19:39:35+00:00
[]
[]
TAGS #task_categories-summarization #region-us
AutoTrain Dataset for project: exacts ===================================== Dataset Description ------------------- This dataset has been automatically processed by AutoTrain for project exacts. ### 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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eb952841bd8fec669fa91a71612655c7170681f1
### Contributions Thanks to [@raldblox](https://github.com/raldblox) for adding this dataset.
raldblox/cotlin
[ "license:openrail", "region:us" ]
2023-02-11T20:18:54+00:00
{"license": "openrail"}
2023-02-11T21:17:02+00:00
[]
[]
TAGS #license-openrail #region-us
### Contributions Thanks to @raldblox for adding this dataset.
[ "### Contributions\n\nThanks to @raldblox for adding this dataset." ]
[ "TAGS\n#license-openrail #region-us \n", "### Contributions\n\nThanks to @raldblox for adding this dataset." ]
[ 12, 18 ]
[ "passage: TAGS\n#license-openrail #region-us \n### Contributions\n\nThanks to @raldblox for adding this dataset." ]
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89d026fff4cc5c020228821c51ae6012e77b2485
# HC3-textgen-qa - the `Hello-SimpleAI/HC3` reformatted for textgen - special tokens for question/answer, see dataset preview
pszemraj/HC3-textgen-qa
[ "task_categories:text-generation", "source_datasets:Hello-SimpleAI/HC3", "language:en", "license:apache-2.0", "chatgpt", "conversation", "region:us" ]
2023-02-11T22:42:21+00:00
{"language": ["en"], "license": "apache-2.0", "source_datasets": "Hello-SimpleAI/HC3", "task_categories": ["text-generation"], "pretty_name": "HC3 for QA textgen", "tags": ["chatgpt", "conversation"]}
2023-02-11T22:56:14+00:00
[]
[ "en" ]
TAGS #task_categories-text-generation #source_datasets-Hello-SimpleAI/HC3 #language-English #license-apache-2.0 #chatgpt #conversation #region-us
# HC3-textgen-qa - the 'Hello-SimpleAI/HC3' reformatted for textgen - special tokens for question/answer, see dataset preview
[ "# HC3-textgen-qa\n\n\n- the 'Hello-SimpleAI/HC3' reformatted for textgen\n- special tokens for question/answer, see dataset preview" ]
[ "TAGS\n#task_categories-text-generation #source_datasets-Hello-SimpleAI/HC3 #language-English #license-apache-2.0 #chatgpt #conversation #region-us \n", "# HC3-textgen-qa\n\n\n- the 'Hello-SimpleAI/HC3' reformatted for textgen\n- special tokens for question/answer, see dataset preview" ]
[ 53, 41 ]
[ "passage: TAGS\n#task_categories-text-generation #source_datasets-Hello-SimpleAI/HC3 #language-English #license-apache-2.0 #chatgpt #conversation #region-us \n# HC3-textgen-qa\n\n\n- the 'Hello-SimpleAI/HC3' reformatted for textgen\n- special tokens for question/answer, see dataset preview" ]
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b1d3a9287f5ea06ee21498551c08159b56a66a4f
# Dataset Card for "quran-tafsir" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tarteel-ai/quran-tafsir
[ "region:us" ]
2023-02-11T23:18:20+00:00
{"dataset_info": {"features": [{"name": "en-ahmedali", "dtype": "string"}, {"name": "en-ahmedraza", "dtype": "string"}, {"name": "en-arberry", "dtype": "string"}, {"name": "en-asad", "dtype": "string"}, {"name": "en-daryabadi", "dtype": "string"}, {"name": "en-hilali", "dtype": "string"}, {"name": "en-itani", "dtype": "string"}, {"name": "en-maududi", "dtype": "string"}, {"name": "en-mubarakpuri", "dtype": "string"}, {"name": "en-pickthall", "dtype": "string"}, {"name": "en-qarai", "dtype": "string"}, {"name": "en-qaribullah", "dtype": "string"}, {"name": "en-sahih", "dtype": "string"}, {"name": "en-sarwar", "dtype": "string"}, {"name": "en-shakir", "dtype": "string"}, {"name": "en-transliterati", "dtype": "string"}, {"name": "en-wahiduddi", "dtype": "string"}, {"name": "en-yusufali", "dtype": "string"}, {"name": "surah", "dtype": "int64"}, {"name": "ayah", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 16266291, "num_examples": 6236}], "download_size": 9038013, "dataset_size": 16266291}}
2023-02-11T23:18:23+00:00
[]
[]
TAGS #region-us
# Dataset Card for "quran-tafsir" More Information needed
[ "# Dataset Card for \"quran-tafsir\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"quran-tafsir\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"quran-tafsir\"\n\nMore Information needed" ]
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69b32ad2941d3c12fdfc902f9fb356401ae08711
# Dataset Card for "curiosamente" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
juancopi81/curiosamente
[ "task_categories:automatic-speech-recognition", "whisper", "whispering", "large", "region:us" ]
2023-02-12T03:04:13+00:00
{"task_categories": ["automatic-speech-recognition"], "dataset_info": {"features": [{"name": "CHANNEL_NAME", "dtype": "string"}, {"name": "URL", "dtype": "string"}, {"name": "TITLE", "dtype": "string"}, {"name": "DESCRIPTION", "dtype": "string"}, {"name": "TRANSCRIPTION", "dtype": "string"}, {"name": "SEGMENTS", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2864256, "num_examples": 122}], "download_size": 1516584, "dataset_size": 2864256}, "tags": ["whisper", "whispering", "large"]}
2023-02-16T22:05:33+00:00
[]
[]
TAGS #task_categories-automatic-speech-recognition #whisper #whispering #large #region-us
# Dataset Card for "curiosamente" More Information needed
[ "# Dataset Card for \"curiosamente\"\n\nMore Information needed" ]
[ "TAGS\n#task_categories-automatic-speech-recognition #whisper #whispering #large #region-us \n", "# Dataset Card for \"curiosamente\"\n\nMore Information needed" ]
[ 34, 12 ]
[ "passage: TAGS\n#task_categories-automatic-speech-recognition #whisper #whispering #large #region-us \n# Dataset Card for \"curiosamente\"\n\nMore Information needed" ]
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f1b3f0bb2af209ba38d14a877b029043c573ebc0
# Chilean Spanish Corpus ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Chilean Spanish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@jorgeortizfuentes](https://github.com/jorgeortizfuentes) for adding this dataset.
jorgeortizfuentes/chilean-spanish-corpus
[ "task_categories:text-generation", "task_categories:fill-mask", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:es", "license:cc-by-sa-4.0", "region:us" ]
2023-02-12T03:05:27+00:00
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["es"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "pretty_name": "Chilean Spanish Corpus", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 31427795307.483433, "num_examples": 37126025}], "download_size": 18718981152, "dataset_size": 31427795307.483433}}
2023-02-13T04:13:50+00:00
[]
[ "es" ]
TAGS #task_categories-text-generation #task_categories-fill-mask #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-Spanish #license-cc-by-sa-4.0 #region-us
# Chilean Spanish Corpus ## 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: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages Chilean Spanish ## 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 Thanks to @jorgeortizfuentes for adding this dataset.
[ "# Chilean Spanish Corpus", "## Table of Contents\n- 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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages\n\nChilean Spanish", "## 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\n\nThanks to @jorgeortizfuentes for adding this dataset." ]
[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-Spanish #license-cc-by-sa-4.0 #region-us \n", "# Chilean Spanish Corpus", "## Table of Contents\n- 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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages\n\nChilean Spanish", "## 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\n\nThanks to @jorgeortizfuentes for adding this dataset." ]
[ 93, 5, 125, 24, 6, 10, 7, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 20 ]
[ "passage: TAGS\n#task_categories-text-generation #task_categories-fill-mask #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-Spanish #license-cc-by-sa-4.0 #region-us \n# Chilean Spanish Corpus## Table of Contents\n- 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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:### Dataset Summary### Supported Tasks and Leaderboards### Languages\n\nChilean Spanish## 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\n\nThanks to @jorgeortizfuentes for adding this dataset." ]
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0d8aaaf6a4affbd04595facfed61e3c6188aa425
# Dataset Card for "poetaexmachina-recitations-lsb" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lsb/poetaexmachina-recitations-lsb
[ "region:us" ]
2023-02-12T04:08:01+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 817933.0, "num_examples": 4}], "download_size": 733536, "dataset_size": 817933.0}}
2023-02-12T04:26:01+00:00
[]
[]
TAGS #region-us
# Dataset Card for "poetaexmachina-recitations-lsb" More Information needed
[ "# Dataset Card for \"poetaexmachina-recitations-lsb\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"poetaexmachina-recitations-lsb\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"poetaexmachina-recitations-lsb\"\n\nMore Information needed" ]
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b387a33d847b7355b9e00d8106b88a90a37d8665
# Dataset Card for "poetaexmachina-recitations-milli-d6vergil" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lsb/poetaexmachina-recitations-milli-d6vergil
[ "region:us" ]
2023-02-12T04:27:03+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2850254.0, "num_examples": 17}], "download_size": 2612867, "dataset_size": 2850254.0}}
2023-02-12T04:27:16+00:00
[]
[]
TAGS #region-us
# Dataset Card for "poetaexmachina-recitations-milli-d6vergil" More Information needed
[ "# Dataset Card for \"poetaexmachina-recitations-milli-d6vergil\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"poetaexmachina-recitations-milli-d6vergil\"\n\nMore Information needed" ]
[ 6, 26 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"poetaexmachina-recitations-milli-d6vergil\"\n\nMore Information needed" ]
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6a334b9611d19be0e2559a140a4338036cb3c4a0
# Dataset Card for "poetaexmachina-recitations-milli-onegrams" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lsb/poetaexmachina-recitations-milli-onegrams
[ "region:us" ]
2023-02-12T04:27:17+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7916446.0, "num_examples": 177}], "download_size": 6517487, "dataset_size": 7916446.0}}
2023-02-12T04:27:32+00:00
[]
[]
TAGS #region-us
# Dataset Card for "poetaexmachina-recitations-milli-onegrams" More Information needed
[ "# Dataset Card for \"poetaexmachina-recitations-milli-onegrams\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"poetaexmachina-recitations-milli-onegrams\"\n\nMore Information needed" ]
[ 6, 25 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"poetaexmachina-recitations-milli-onegrams\"\n\nMore Information needed" ]
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432ac0e2243a1eba785227110bf580bd102d1688
# Dataset Card for "poetaexmachina-recitations-d6vergil" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lsb/poetaexmachina-recitations-d6vergil
[ "region:us" ]
2023-02-12T04:27:34+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2443347256.8, "num_examples": 15287}], "download_size": 2195373036, "dataset_size": 2443347256.8}}
2023-02-12T04:30:03+00:00
[]
[]
TAGS #region-us
# Dataset Card for "poetaexmachina-recitations-d6vergil" More Information needed
[ "# Dataset Card for \"poetaexmachina-recitations-d6vergil\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"poetaexmachina-recitations-d6vergil\"\n\nMore Information needed" ]
[ 6, 24 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"poetaexmachina-recitations-d6vergil\"\n\nMore Information needed" ]
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7f78309bc5b3ec78290bcaa842b6b2d8310b3a48
# Dataset Card for "poetaexmachina-recitations-onegrams" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lsb/poetaexmachina-recitations-onegrams
[ "region:us" ]
2023-02-12T04:30:15+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6909913371.324, "num_examples": 176826}], "download_size": 6026737051, "dataset_size": 6909913371.324}}
2023-02-12T04:38:19+00:00
[]
[]
TAGS #region-us
# Dataset Card for "poetaexmachina-recitations-onegrams" More Information needed
[ "# Dataset Card for \"poetaexmachina-recitations-onegrams\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"poetaexmachina-recitations-onegrams\"\n\nMore Information needed" ]
[ 6, 23 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"poetaexmachina-recitations-onegrams\"\n\nMore Information needed" ]
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a831833e6323636e673c0e87a8950fda2e59824b
This is a conversational dataset, collected from WarOnine Israeli military forum Language = Russian (with hebrew addins) Target Audience = Military Dataset has been used to train a Military Chat Bot
kertser/WarOnline
[ "size_categories:100K<n<1M", "language:ru", "license:apache-2.0", "NLP", "WarOnline", "ChatBot", "Conversational", "region:us" ]
2023-02-12T07:09:49+00:00
{"language": ["ru"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "pretty_name": "War Online", "tags": ["NLP", "WarOnline", "ChatBot", "Conversational"]}
2023-02-12T08:18:01+00:00
[]
[ "ru" ]
TAGS #size_categories-100K<n<1M #language-Russian #license-apache-2.0 #NLP #WarOnline #ChatBot #Conversational #region-us
This is a conversational dataset, collected from WarOnine Israeli military forum Language = Russian (with hebrew addins) Target Audience = Military Dataset has been used to train a Military Chat Bot
[]
[ "TAGS\n#size_categories-100K<n<1M #language-Russian #license-apache-2.0 #NLP #WarOnline #ChatBot #Conversational #region-us \n" ]
[ 44 ]
[ "passage: TAGS\n#size_categories-100K<n<1M #language-Russian #license-apache-2.0 #NLP #WarOnline #ChatBot #Conversational #region-us \n" ]
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ac96eaa2363cc7454a810c7e3882d3f8f58213ff
# Dataset Card for "render_text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
achang/render_text
[ "region:us" ]
2023-02-12T08:15:23+00:00
{"dataset_info": {"features": [{"name": "review_body", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "section", "dtype": "image"}, {"name": "section_text", "dtype": "string"}, {"name": "pos_section_txt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6082763237.75, "num_examples": 63057}, {"name": "validation", "num_bytes": 150630977.75, "num_examples": 1561}, {"name": "test", "num_bytes": 152523383.75, "num_examples": 1581}], "download_size": 6373937421, "dataset_size": 6385917599.25}}
2023-02-19T07:22:49+00:00
[]
[]
TAGS #region-us
# Dataset Card for "render_text" More Information needed
[ "# Dataset Card for \"render_text\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"render_text\"\n\nMore Information needed" ]
[ 6, 14 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"render_text\"\n\nMore Information needed" ]
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0d95002952e1fe53a2f2e9e761bed1fa737229f8
Creation (Copied & adapted from https://github.com/stanford-crfm/helm/blob/0eaaa62a2263ddb94e9850ee629423b010f57e4a/src/helm/benchmark/scenarios/babi_qa_scenario.py): ```python !wget http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2.tar.gz !tar -xf tasks_1-20_v1-2.tar.gz import json from typing import List tasks = list(range(1, 20)) splits = ["train", "valid", "test"] def process_path(path: str) -> str: """Turn a path string (task 19) from the original format 's,w' to a verbal model-friendly format 'south west'""" steps: List[str] = path.split(",") directions = {"s": "south", "n": "north", "e": "east", "w": "west"} path = " ".join([directions[step] for step in steps]) return path for split in splits: with open(f"babi_{split}.jsonl", "w") as f_base: for task in tasks: split_path: str = f"./tasks_1-20_v1-2/en-valid/qa{task}_{split}.txt" with open(split_path, "r") as f: facts = list(f) story: List[str] = [] for fact in facts: fid = int(fact.split(" ")[0]) if fid == 1: story = [] fact = " ".join(fact.split(" ")[1:]) is_question = "?" in fact if is_question: question, answer = fact.split("\t")[:2] question, answer = question.strip(), answer.strip() # All tasks except task 19 have a verbal single-word answer (e.g. kitchen, apple, yes). # Task 19 (path finding) has a non verbal answer format ( if task == 19: answer = process_path(answer) f_base.write(json.dumps({ "passage": "".join(story), "question": question, "answer": answer, "task": task, }) + "\n") if "?" in story: print("STORY", "".join(story)) else: story.append(fact) ```
Muennighoff/babi
[ "region:us" ]
2023-02-12T09:19:00+00:00
{}
2023-02-12T13:34:24+00:00
[]
[]
TAGS #region-us
Creation (Copied & adapted from URL
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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aaff98ff0e2e55dd18256a02ca3f8eca990881cc
# RAFT submissions for my-raft-submission ## Submitting to the leaderboard To make a submission to the [leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard), there are three main steps: 1. Generate predictions on the unlabeled test set of each task 2. Validate the predictions are compatible with the evaluation framework 3. Push the predictions to the Hub! See the instructions below for more details. ### Rules 1. To prevent overfitting to the public leaderboard, we only evaluate **one submission per week**. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week. 2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed. 3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted. 4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches. ### Submission file format For each task in RAFT, you should create a CSV file called `predictions.csv` with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns: * ID (int) * Label (string) See the dummy predictions in the `data` folder for examples with the expected format. Here is a simple example that creates a majority-class baseline: ```python from pathlib import Path import pandas as pd from collections import Counter from datasets import load_dataset, get_dataset_config_names tasks = get_dataset_config_names("ought/raft") for task in tasks: # Load dataset raft_subset = load_dataset("ought/raft", task) # Compute majority class over training set counter = Counter(raft_subset["train"]["Label"]) majority_class = counter.most_common(1)[0][0] # Load predictions file preds = pd.read_csv(f"data/{task}/predictions.csv") # Convert label IDs to label names preds["Label"] = raft_subset["train"].features["Label"].int2str(majority_class) # Save predictions preds.to_csv(f"data/{task}/predictions.csv", index=False) ``` As you can see in the example, each `predictions.csv` file should be stored in the task's subfolder in `data` and at the end you should have something like the following: ``` data ├── ade_corpus_v2 │ ├── predictions.csv │ └── task.json ├── banking_77 │ ├── predictions.csv │ └── task.json ├── neurips_impact_statement_risks │ ├── predictions.csv │ └── task.json ├── one_stop_english │ ├── predictions.csv │ └── task.json ├── overruling │ ├── predictions.csv │ └── task.json ├── semiconductor_org_types │ ├── predictions.csv │ └── task.json ├── systematic_review_inclusion │ ├── predictions.csv │ └── task.json ├── tai_safety_research │ ├── predictions.csv │ └── task.json ├── terms_of_service │ ├── predictions.csv │ └── task.json ├── tweet_eval_hate │ ├── predictions.csv │ └── task.json └── twitter_complaints ├── predictions.csv └── task.json ``` ### Validate your submission To ensure that your submission files are correctly formatted, run the following command from the root of the repository: ``` python cli.py validate ``` If everything is correct, you should see the following message: ``` All submission files validated! ✨ 🚀 ✨ Now you can make a submission 🤗 ``` ### Push your submission to the Hugging Face Hub! The final step is to commit your files and push them to the Hub: ``` python cli.py submit ``` If there are no errors, you should see the following message: ``` Submission successful! 🎉 🥳 🎉 Your submission will be evaulated on Sunday 05 September 2021 ⏳ ``` where the evaluation is run every Sunday and your results will be visible on the leaderboard.
Linuxdex/my-raft-submission
[ "benchmark:raft", "region:us" ]
2023-02-12T09:19:33+00:00
{"benchmark": "raft", "type": "prediction", "submission_name": "AG-tt"}
2023-03-20T09:35:25+00:00
[]
[]
TAGS #benchmark-raft #region-us
# RAFT submissions for my-raft-submission ## Submitting to the leaderboard To make a submission to the leaderboard, there are three main steps: 1. Generate predictions on the unlabeled test set of each task 2. Validate the predictions are compatible with the evaluation framework 3. Push the predictions to the Hub! See the instructions below for more details. ### Rules 1. To prevent overfitting to the public leaderboard, we only evaluate one submission per week. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week. 2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed. 3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted. 4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches. ### Submission file format For each task in RAFT, you should create a CSV file called 'URL' with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns: * ID (int) * Label (string) See the dummy predictions in the 'data' folder for examples with the expected format. Here is a simple example that creates a majority-class baseline: As you can see in the example, each 'URL' file should be stored in the task's subfolder in 'data' and at the end you should have something like the following: ### Validate your submission To ensure that your submission files are correctly formatted, run the following command from the root of the repository: If everything is correct, you should see the following message: ### Push your submission to the Hugging Face Hub! The final step is to commit your files and push them to the Hub: If there are no errors, you should see the following message: where the evaluation is run every Sunday and your results will be visible on the leaderboard.
[ "# RAFT submissions for my-raft-submission", "## Submitting to the leaderboard\n\nTo make a submission to the leaderboard, there are three main steps:\n\n1. Generate predictions on the unlabeled test set of each task\n2. Validate the predictions are compatible with the evaluation framework\n3. Push the predictions to the Hub!\n\nSee the instructions below for more details.", "### Rules\n\n1. To prevent overfitting to the public leaderboard, we only evaluate one submission per week. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week. \n2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.\n3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.\n4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.", "### Submission file format\n\nFor each task in RAFT, you should create a CSV file called 'URL' with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:\n\n* ID (int)\n* Label (string)\n\nSee the dummy predictions in the 'data' folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:\n\n\n\nAs you can see in the example, each 'URL' file should be stored in the task's subfolder in 'data' and at the end you should have something like the following:", "### Validate your submission\n\nTo ensure that your submission files are correctly formatted, run the following command from the root of the repository:\n\n\n\nIf everything is correct, you should see the following message:", "### Push your submission to the Hugging Face Hub!\n\nThe final step is to commit your files and push them to the Hub:\n\n\n\nIf there are no errors, you should see the following message:\n\n\n\nwhere the evaluation is run every Sunday and your results will be visible on the leaderboard." ]
[ "TAGS\n#benchmark-raft #region-us \n", "# RAFT submissions for my-raft-submission", "## Submitting to the leaderboard\n\nTo make a submission to the leaderboard, there are three main steps:\n\n1. Generate predictions on the unlabeled test set of each task\n2. Validate the predictions are compatible with the evaluation framework\n3. Push the predictions to the Hub!\n\nSee the instructions below for more details.", "### Rules\n\n1. To prevent overfitting to the public leaderboard, we only evaluate one submission per week. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week. \n2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.\n3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.\n4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.", "### Submission file format\n\nFor each task in RAFT, you should create a CSV file called 'URL' with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:\n\n* ID (int)\n* Label (string)\n\nSee the dummy predictions in the 'data' folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:\n\n\n\nAs you can see in the example, each 'URL' file should be stored in the task's subfolder in 'data' and at the end you should have something like the following:", "### Validate your submission\n\nTo ensure that your submission files are correctly formatted, run the following command from the root of the repository:\n\n\n\nIf everything is correct, you should see the following message:", "### Push your submission to the Hugging Face Hub!\n\nThe final step is to commit your files and push them to the Hub:\n\n\n\nIf there are no errors, you should see the following message:\n\n\n\nwhere the evaluation is run every Sunday and your results will be visible on the leaderboard." ]
[ 13, 13, 68, 150, 135, 45, 61 ]
[ "passage: TAGS\n#benchmark-raft #region-us \n# RAFT submissions for my-raft-submission## Submitting to the leaderboard\n\nTo make a submission to the leaderboard, there are three main steps:\n\n1. Generate predictions on the unlabeled test set of each task\n2. Validate the predictions are compatible with the evaluation framework\n3. Push the predictions to the Hub!\n\nSee the instructions below for more details.### Rules\n\n1. To prevent overfitting to the public leaderboard, we only evaluate one submission per week. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week. \n2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.\n3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.\n4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.### Submission file format\n\nFor each task in RAFT, you should create a CSV file called 'URL' with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:\n\n* ID (int)\n* Label (string)\n\nSee the dummy predictions in the 'data' folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:\n\n\n\nAs you can see in the example, each 'URL' file should be stored in the task's subfolder in 'data' and at the end you should have something like the following:### Validate your submission\n\nTo ensure that your submission files are correctly formatted, run the following command from the root of the repository:\n\n\n\nIf everything is correct, you should see the following message:### Push your submission to the Hugging Face Hub!\n\nThe final step is to commit your files and push them to the Hub:\n\n\n\nIf there are no errors, you should see the following message:\n\n\n\nwhere the evaluation is run every Sunday and your results will be visible on the leaderboard." ]
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70b0346b9db77199203df5f077deeefec29af1da
# Dataset Card for "PneumoniaHippo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HippoLite/PneumoniaHippo
[ "region:us" ]
2023-02-12T09:59:44+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3500970321.536, "num_examples": 11712}], "download_size": 2465721553, "dataset_size": 3500970321.536}}
2023-02-12T10:00:58+00:00
[]
[]
TAGS #region-us
# Dataset Card for "PneumoniaHippo" More Information needed
[ "# Dataset Card for \"PneumoniaHippo\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"PneumoniaHippo\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"PneumoniaHippo\"\n\nMore Information needed" ]
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63eeeb7847fe648cd71277a3f8872f2be82cf199
# Dataset Card for "bookcorpus_compact_1024_shard6_of_10_meta" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
saibo/bookcorpus_compact_1024_shard6_of_10_meta
[ "region:us" ]
2023-02-12T10:10:53+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 7837212848, "num_examples": 61605}], "download_size": 1730877027, "dataset_size": 7837212848}}
2023-02-12T10:40:35+00:00
[]
[]
TAGS #region-us
# Dataset Card for "bookcorpus_compact_1024_shard6_of_10_meta" More Information needed
[ "# Dataset Card for \"bookcorpus_compact_1024_shard6_of_10_meta\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_compact_1024_shard6_of_10_meta\"\n\nMore Information needed" ]
[ 6, 29 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_compact_1024_shard6_of_10_meta\"\n\nMore Information needed" ]
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bedcc4438826674ab0175c8d47caaca534175af2
# Peanut Comic Strip Dataset (Snoopy & Co.) ![Peanuts 1999/01/30](preview.png) This is a dataset Peanuts comic strips from `1950/10/02` to `2000/02/13`. There are `77,457` panels extracted from `17,816` comic strips. The dataset size is approximately `4.4G`. Each row in the dataset contains the following fields: - `image`: `PIL.Image` containing the extracted panel. - `panel_name`: unique identifier for the row. - `characters`: `tuple[str, ...]` of characters included in the comic strip the panel is part of. - `themes`: `tuple[str, ...]` of theme in the comic strip the panel is part of. - `color`: `str` indicating whether the panel is grayscale or in color. - `caption`: [BLIP-2_OPT_6.7B](https://huggingface.co/docs/transformers/main/model_doc/blip-2) generated caption from the panel. - `year`: `int` storing the year the specific panel was released. > **OPT-6.7B has a non-commercial use license and so this dataset cannot be used for commercial projects. If you need a dataset for commercial use please see [this similar dataset](https://huggingface.co/datasets/afmck/peanuts-flan-t5-xl) that uses Flan-T5-XL, which allows for commercial use.** Character and theme information was extracted from [Peanuts Wiki (Fandom)](https://peanuts.fandom.com/wiki/Peanuts_Wiki) using [Beautiful Soup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/). Images were extracted from [Peanuts Search](https://peanuts-search.com/). Only strips with the following characters were extracted: ``` - "Charlie Brown" - "Sally Brown" - "Joe Cool" # Snoopy alter-ego - "Franklin" - "Violet Gray" - "Eudora" - "Frieda" - "Marcie" - "Peppermint Patty" - "Patty" - "Pig-Pen" - "Linus van Pelt" - "Lucy van Pelt" - "Rerun van Pelt" - "Schroeder" - "Snoopy" - "Shermy" - "Spike" - "Woodstock" - "the World War I Flying Ace" # Snoopy alter-ego ``` ### Extraction Details Panel detection and extraction was done using the following codeblock: ```python def check_contour(cnt): area = cv2.contourArea(cnt) if area < 600: return False _, _, w, h = cv2.boundingRect(cnt) if w / h < 1 / 2: return False if w / h > 2 / 1: return False return True def get_panels_from_image(path): panels = [] original_img = cv2.imread(path) gray = cv2.cvtColor(original_img, cv2.COLOR_BGR2GRAY) blur = cv2.GaussianBlur(gray, (5,5), 0) thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1] kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3)) opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1) invert = 255 - opening cnts, _ = cv2.findContours(invert, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) idx = 0 for cnt in cnts: if not check_contour(cnt): continue idx += 1 x,y,w,h = cv2.boundingRect(cnt) roi = original_img[y:y+h,x:x+w] panels.append(roi) return panels ``` `check_contour` will reject panels with `area < 600` or with aspect ratios larger than `2` or smaller than `0.5`. Grayscale detection was done using the following codeblock: ```python def is_grayscale(panel): LAB_THRESHOLD = 10. img = cv2.cvtColor(panel, cv2.COLOR_RGB2LAB) _, ea, eb = cv2.split(img) de = abs(ea - eb) mean_e = np.mean(de) return mean_e < LAB_THRESHOLD ``` Captioning was done using the standard BLIP-2 pipeline shown in the [Huggingface docs](https://huggingface.co/docs/transformers/main/model_doc/blip-2) using beam search over 10 beams and a repetition penalty of `2.0`. Raw captions are extracted and no postprocessing is applied. You may wish to normalise captions (such as replacing "cartoon" with "peanuts cartoon") or incorporate extra metadata into prompts.
afmck/peanuts-opt-6.7b
[ "task_categories:text-to-image", "size_categories:10K<n<100K", "language:en", "license:other", "region:us" ]
2023-02-12T10:20:14+00:00
{"language": ["en"], "license": "other", "size_categories": ["10K<n<100K"], "task_categories": ["text-to-image"], "pretty_name": "Peanuts Dataset (Snoopy and Co.)", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "panel_name", "dtype": "string"}, {"name": "characters", "sequence": "string"}, {"name": "themes", "sequence": "string"}, {"name": "color", "dtype": "string"}, {"name": "year", "dtype": "int64"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2948640650.848, "num_examples": 77456}], "download_size": 4601323640, "dataset_size": 2948640650.848}}
2023-04-03T15:09:56+00:00
[]
[ "en" ]
TAGS #task_categories-text-to-image #size_categories-10K<n<100K #language-English #license-other #region-us
# Peanut Comic Strip Dataset (Snoopy & Co.) !Peanuts 1999/01/30 This is a dataset Peanuts comic strips from '1950/10/02' to '2000/02/13'. There are '77,457' panels extracted from '17,816' comic strips. The dataset size is approximately '4.4G'. Each row in the dataset contains the following fields: - 'image': 'PIL.Image' containing the extracted panel. - 'panel_name': unique identifier for the row. - 'characters': 'tuple[str, ...]' of characters included in the comic strip the panel is part of. - 'themes': 'tuple[str, ...]' of theme in the comic strip the panel is part of. - 'color': 'str' indicating whether the panel is grayscale or in color. - 'caption': BLIP-2_OPT_6.7B generated caption from the panel. - 'year': 'int' storing the year the specific panel was released. > OPT-6.7B has a non-commercial use license and so this dataset cannot be used for commercial projects. If you need a dataset for commercial use please see this similar dataset that uses Flan-T5-XL, which allows for commercial use. Character and theme information was extracted from Peanuts Wiki (Fandom) using Beautiful Soup. Images were extracted from Peanuts Search. Only strips with the following characters were extracted: ### Extraction Details Panel detection and extraction was done using the following codeblock: 'check_contour' will reject panels with 'area < 600' or with aspect ratios larger than '2' or smaller than '0.5'. Grayscale detection was done using the following codeblock: Captioning was done using the standard BLIP-2 pipeline shown in the Huggingface docs using beam search over 10 beams and a repetition penalty of '2.0'. Raw captions are extracted and no postprocessing is applied. You may wish to normalise captions (such as replacing "cartoon" with "peanuts cartoon") or incorporate extra metadata into prompts.
[ "# Peanut Comic Strip Dataset (Snoopy & Co.)\n\n!Peanuts 1999/01/30\n\nThis is a dataset Peanuts comic strips from '1950/10/02' to '2000/02/13'.\nThere are '77,457' panels extracted from '17,816' comic strips. \nThe dataset size is approximately '4.4G'.\n\nEach row in the dataset contains the following fields:\n- 'image': 'PIL.Image' containing the extracted panel.\n- 'panel_name': unique identifier for the row.\n- 'characters': 'tuple[str, ...]' of characters included in the comic strip the panel is part of.\n- 'themes': 'tuple[str, ...]' of theme in the comic strip the panel is part of.\n- 'color': 'str' indicating whether the panel is grayscale or in color.\n- 'caption': BLIP-2_OPT_6.7B generated caption from the panel.\n- 'year': 'int' storing the year the specific panel was released.\n\n> OPT-6.7B has a non-commercial use license and so this dataset cannot be used for commercial projects. If you need a dataset for commercial use please see this similar dataset that uses Flan-T5-XL, which allows for commercial use.\n\nCharacter and theme information was extracted from Peanuts Wiki (Fandom) using Beautiful Soup.\nImages were extracted from Peanuts Search.\n\nOnly strips with the following characters were extracted:", "### Extraction Details\nPanel detection and extraction was done using the following codeblock:\n\n'check_contour' will reject panels with 'area < 600' or with aspect ratios larger than '2' or smaller than '0.5'.\n\nGrayscale detection was done using the following codeblock:\n\n\nCaptioning was done using the standard BLIP-2 pipeline shown in the Huggingface docs using beam search over 10 beams and a repetition penalty of '2.0'.\nRaw captions are extracted and no postprocessing is applied. You may wish to normalise captions (such as replacing \"cartoon\" with \"peanuts cartoon\") or incorporate extra metadata into prompts." ]
[ "TAGS\n#task_categories-text-to-image #size_categories-10K<n<100K #language-English #license-other #region-us \n", "# Peanut Comic Strip Dataset (Snoopy & Co.)\n\n!Peanuts 1999/01/30\n\nThis is a dataset Peanuts comic strips from '1950/10/02' to '2000/02/13'.\nThere are '77,457' panels extracted from '17,816' comic strips. \nThe dataset size is approximately '4.4G'.\n\nEach row in the dataset contains the following fields:\n- 'image': 'PIL.Image' containing the extracted panel.\n- 'panel_name': unique identifier for the row.\n- 'characters': 'tuple[str, ...]' of characters included in the comic strip the panel is part of.\n- 'themes': 'tuple[str, ...]' of theme in the comic strip the panel is part of.\n- 'color': 'str' indicating whether the panel is grayscale or in color.\n- 'caption': BLIP-2_OPT_6.7B generated caption from the panel.\n- 'year': 'int' storing the year the specific panel was released.\n\n> OPT-6.7B has a non-commercial use license and so this dataset cannot be used for commercial projects. If you need a dataset for commercial use please see this similar dataset that uses Flan-T5-XL, which allows for commercial use.\n\nCharacter and theme information was extracted from Peanuts Wiki (Fandom) using Beautiful Soup.\nImages were extracted from Peanuts Search.\n\nOnly strips with the following characters were extracted:", "### Extraction Details\nPanel detection and extraction was done using the following codeblock:\n\n'check_contour' will reject panels with 'area < 600' or with aspect ratios larger than '2' or smaller than '0.5'.\n\nGrayscale detection was done using the following codeblock:\n\n\nCaptioning was done using the standard BLIP-2 pipeline shown in the Huggingface docs using beam search over 10 beams and a repetition penalty of '2.0'.\nRaw captions are extracted and no postprocessing is applied. You may wish to normalise captions (such as replacing \"cartoon\" with \"peanuts cartoon\") or incorporate extra metadata into prompts." ]
[ 39, 345, 154 ]
[ "passage: TAGS\n#task_categories-text-to-image #size_categories-10K<n<100K #language-English #license-other #region-us \n# Peanut Comic Strip Dataset (Snoopy & Co.)\n\n!Peanuts 1999/01/30\n\nThis is a dataset Peanuts comic strips from '1950/10/02' to '2000/02/13'.\nThere are '77,457' panels extracted from '17,816' comic strips. \nThe dataset size is approximately '4.4G'.\n\nEach row in the dataset contains the following fields:\n- 'image': 'PIL.Image' containing the extracted panel.\n- 'panel_name': unique identifier for the row.\n- 'characters': 'tuple[str, ...]' of characters included in the comic strip the panel is part of.\n- 'themes': 'tuple[str, ...]' of theme in the comic strip the panel is part of.\n- 'color': 'str' indicating whether the panel is grayscale or in color.\n- 'caption': BLIP-2_OPT_6.7B generated caption from the panel.\n- 'year': 'int' storing the year the specific panel was released.\n\n> OPT-6.7B has a non-commercial use license and so this dataset cannot be used for commercial projects. If you need a dataset for commercial use please see this similar dataset that uses Flan-T5-XL, which allows for commercial use.\n\nCharacter and theme information was extracted from Peanuts Wiki (Fandom) using Beautiful Soup.\nImages were extracted from Peanuts Search.\n\nOnly strips with the following characters were extracted:" ]
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b8139a0a66bc2c05983b378a043c700a2b919835
# Dataset Card for "pile-small-tokenized-2b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NeelNanda/pile-small-tokenized-2b
[ "region:us" ]
2023-02-12T12:20:37+00:00
{"dataset_info": {"features": [{"name": "tokens", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 44263497500, "num_examples": 10795975}], "download_size": 19763664789, "dataset_size": 44263497500}}
2023-02-12T16:25:43+00:00
[]
[]
TAGS #region-us
# Dataset Card for "pile-small-tokenized-2b" More Information needed
[ "# Dataset Card for \"pile-small-tokenized-2b\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"pile-small-tokenized-2b\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"pile-small-tokenized-2b\"\n\nMore Information needed" ]
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5755097f4cb23f6bc76559e927b2310de365ceb8
# Dataset Card for "fnli" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Deojoandco/fnli
[ "region:us" ]
2023-02-12T13:04:48+00:00
{"dataset_info": {"features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 61159046, "num_examples": 550152}, {"name": "validation", "num_bytes": 1120856, "num_examples": 10000}, {"name": "test", "num_bytes": 1117922, "num_examples": 10000}], "download_size": 20299372, "dataset_size": 63397824}}
2023-02-24T08:42:01+00:00
[]
[]
TAGS #region-us
# Dataset Card for "fnli" More Information needed
[ "# Dataset Card for \"fnli\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"fnli\"\n\nMore Information needed" ]
[ 6, 12 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"fnli\"\n\nMore Information needed" ]
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f11d658775bd3e0bbbd6a1b0f240003f980ebc94
# Dataset Card for "java_0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
thmk/java_0
[ "region:us" ]
2023-02-12T15:01:14+00:00
{"dataset_info": {"features": [{"name": "code", "dtype": "string"}, {"name": "repo_name", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "size", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 597095942, "num_examples": 100000}], "download_size": 0, "dataset_size": 597095942}}
2023-02-12T15:23:46+00:00
[]
[]
TAGS #region-us
# Dataset Card for "java_0" More Information needed
[ "# Dataset Card for \"java_0\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"java_0\"\n\nMore Information needed" ]
[ 6, 13 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"java_0\"\n\nMore Information needed" ]
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581a521327149965a3c21bfe06d6dacb6217079e
# Dataset Card for "java_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
thmk/java_10
[ "region:us" ]
2023-02-12T15:55:44+00:00
{"dataset_info": {"features": [{"name": "code", "dtype": "string"}, {"name": "repo_name", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "size", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 605825109, "num_examples": 100000}], "download_size": 195428485, "dataset_size": 605825109}}
2023-02-12T15:56:01+00:00
[]
[]
TAGS #region-us
# Dataset Card for "java_10" More Information needed
[ "# Dataset Card for \"java_10\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"java_10\"\n\nMore Information needed" ]
[ 6, 13 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"java_10\"\n\nMore Information needed" ]
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47a6bb18216a6a0cd17223d23a0ce6fa3a3a6724
# Dataset Card for "class_dataset2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
LFBMS/class_dataset2
[ "region:us" ]
2023-02-12T16:12:48+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "bilanz_datev", "1": "bilanz_lexware", "2": "guv", "3": "other"}}}}], "splits": [{"name": "train", "num_bytes": 13700431777.0, "num_examples": 4000}, {"name": "validation", "num_bytes": 548626720.0, "num_examples": 500}, {"name": "test", "num_bytes": 559045772.0, "num_examples": 500}], "download_size": 5407648855, "dataset_size": 14808104269.0}}
2023-02-12T16:15:51+00:00
[]
[]
TAGS #region-us
# Dataset Card for "class_dataset2" More Information needed
[ "# Dataset Card for \"class_dataset2\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"class_dataset2\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"class_dataset2\"\n\nMore Information needed" ]
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95fa88d0a7fc98cbf5454d920dc857b1ff6b2e43
# Dataset Card for "class_dataset_donut2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
LFBMS/class_dataset_donut2
[ "region:us" ]
2023-02-12T16:22:15+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "bilanz_datev", "1": "bilanz_lexware", "2": "guv", "3": "other"}}}}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 559064953.0, "num_examples": 500}, {"name": "train", "num_bytes": 4343890380.0, "num_examples": 4000}, {"name": "validation", "num_bytes": 548645901.0, "num_examples": 500}], "download_size": 5424719748, "dataset_size": 5451601234.0}}
2023-02-12T16:25:06+00:00
[]
[]
TAGS #region-us
# Dataset Card for "class_dataset_donut2" More Information needed
[ "# Dataset Card for \"class_dataset_donut2\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"class_dataset_donut2\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"class_dataset_donut2\"\n\nMore Information needed" ]
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cb40061d1c6c6493facce3bb2b4437dccd457efc
# Dataset Card for "VQAv2_sample_validation_embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/VQAv2_sample_validation_embeddings
[ "region:us" ]
2023-02-12T16:59:52+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 158671010.0, "num_examples": 1000}], "download_size": 156748467, "dataset_size": 158671010.0}}
2023-02-12T17:00:15+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_sample_validation_embeddings" More Information needed
[ "# Dataset Card for \"VQAv2_sample_validation_embeddings\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_sample_validation_embeddings\"\n\nMore Information needed" ]
[ 6, 25 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_embeddings\"\n\nMore Information needed" ]
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be4dc50651721106d36a4a225597d22374acce25
# Dataset Card for "VQAv2_sample_test_embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/VQAv2_sample_test_embeddings
[ "region:us" ]
2023-02-12T17:02:34+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 160166334.0, "num_examples": 1000}], "download_size": 159038686, "dataset_size": 160166334.0}}
2023-02-12T17:02:45+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_sample_test_embeddings" More Information needed
[ "# Dataset Card for \"VQAv2_sample_test_embeddings\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_sample_test_embeddings\"\n\nMore Information needed" ]
[ 6, 23 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_test_embeddings\"\n\nMore Information needed" ]
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e5fbed726ca7289dd04cf9bc106024cc0d3bd648
# Dataset Card for "VQAv2_sample_testdev_embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/VQAv2_sample_testdev_embeddings
[ "region:us" ]
2023-02-12T17:04:14+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 161538407.0, "num_examples": 1000}], "download_size": 160035377, "dataset_size": 161538407.0}}
2023-02-12T17:04:41+00:00
[]
[]
TAGS #region-us
# Dataset Card for "VQAv2_sample_testdev_embeddings" More Information needed
[ "# Dataset Card for \"VQAv2_sample_testdev_embeddings\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"VQAv2_sample_testdev_embeddings\"\n\nMore Information needed" ]
[ 6, 24 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_testdev_embeddings\"\n\nMore Information needed" ]
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f5bcb32ca4fdfbb9972b39f90ae0212fd78d92db
# Dataset Card for "Sunshine-the-Chicken" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Hatman/Sunshine-the-banta-chicken
[ "region:us" ]
2023-02-12T17:33:15+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 15439966.0, "num_examples": 13}], "download_size": 15318668, "dataset_size": 15439966.0}}
2023-02-28T03:40:30+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Sunshine-the-Chicken" More Information needed
[ "# Dataset Card for \"Sunshine-the-Chicken\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Sunshine-the-Chicken\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Sunshine-the-Chicken\"\n\nMore Information needed" ]
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0df7d0f25e98595129777372867c29cef9b4ef27
# Neuro CNN Project - Fernando Feltrin # Brain Meningioma images (39 classes) for image classification ## Dataset Description - **More info: [email protected]** ### Dataset Summary A collection of T1, contrast-enhanced, and T2-weighted MRI images of meningiomas sorted according to location in the brain. Images without any type of marking or patient identification, interpreted by radiologists and provided for study purposes. Images are separated by clivus / petroclival, sphenoid / cavernous sinus, anterior cranial fossa, medial cranial fossa, posterior cranial fossa, frontal / frontoparietal, frontotemporal, infratentorial / cerebellar, interhemispheric / suprasellar, intracisternal, intraventricular / parafalkyne, parietal / parietooccipital, supratentorial, temporal/temporoparietal.
fernando2rad/neuro_cnn_meningioma_39c
[ "task_categories:image-classification", "size_categories:1K<n<10K", "license:odc-by", "brain mri images", "brain images", "brain tumor", "brain meningioma", "meningioma mri images", "region:us" ]
2023-02-12T17:52:50+00:00
{"license": "odc-by", "size_categories": ["1K<n<10K"], "task_categories": ["image-classification"], "pretty_name": "Neuro CNN Brain Meningioma 39 classes", "tags": ["brain mri images", "brain images", "brain tumor", "brain meningioma", "meningioma mri images"]}
2023-02-12T18:20:00+00:00
[]
[]
TAGS #task_categories-image-classification #size_categories-1K<n<10K #license-odc-by #brain mri images #brain images #brain tumor #brain meningioma #meningioma mri images #region-us
# Neuro CNN Project - Fernando Feltrin # Brain Meningioma images (39 classes) for image classification ## Dataset Description - More info: fernando2rad@URL ### Dataset Summary A collection of T1, contrast-enhanced, and T2-weighted MRI images of meningiomas sorted according to location in the brain. Images without any type of marking or patient identification, interpreted by radiologists and provided for study purposes. Images are separated by clivus / petroclival, sphenoid / cavernous sinus, anterior cranial fossa, medial cranial fossa, posterior cranial fossa, frontal / frontoparietal, frontotemporal, infratentorial / cerebellar, interhemispheric / suprasellar, intracisternal, intraventricular / parafalkyne, parietal / parietooccipital, supratentorial, temporal/temporoparietal.
[ "# Neuro CNN Project - Fernando Feltrin", "# Brain Meningioma images (39 classes) for image classification", "## Dataset Description\n\n- More info: fernando2rad@URL", "### Dataset Summary\n\nA collection of T1, contrast-enhanced, and T2-weighted MRI images of meningiomas sorted according to location in the brain. Images without any type of marking or patient identification, interpreted by radiologists and provided for study purposes.\n\nImages are separated by clivus / petroclival, sphenoid / cavernous sinus, anterior cranial fossa, medial cranial fossa, posterior cranial fossa, frontal / frontoparietal, frontotemporal, infratentorial / cerebellar, interhemispheric / suprasellar, intracisternal, intraventricular / parafalkyne, parietal / parietooccipital, supratentorial, temporal/temporoparietal." ]
[ "TAGS\n#task_categories-image-classification #size_categories-1K<n<10K #license-odc-by #brain mri images #brain images #brain tumor #brain meningioma #meningioma mri images #region-us \n", "# Neuro CNN Project - Fernando Feltrin", "# Brain Meningioma images (39 classes) for image classification", "## Dataset Description\n\n- More info: fernando2rad@URL", "### Dataset Summary\n\nA collection of T1, contrast-enhanced, and T2-weighted MRI images of meningiomas sorted according to location in the brain. Images without any type of marking or patient identification, interpreted by radiologists and provided for study purposes.\n\nImages are separated by clivus / petroclival, sphenoid / cavernous sinus, anterior cranial fossa, medial cranial fossa, posterior cranial fossa, frontal / frontoparietal, frontotemporal, infratentorial / cerebellar, interhemispheric / suprasellar, intracisternal, intraventricular / parafalkyne, parietal / parietooccipital, supratentorial, temporal/temporoparietal." ]
[ 64, 8, 15, 15, 174 ]
[ "passage: TAGS\n#task_categories-image-classification #size_categories-1K<n<10K #license-odc-by #brain mri images #brain images #brain tumor #brain meningioma #meningioma mri images #region-us \n# Neuro CNN Project - Fernando Feltrin# Brain Meningioma images (39 classes) for image classification## Dataset Description\n\n- More info: fernando2rad@URL### Dataset Summary\n\nA collection of T1, contrast-enhanced, and T2-weighted MRI images of meningiomas sorted according to location in the brain. Images without any type of marking or patient identification, interpreted by radiologists and provided for study purposes.\n\nImages are separated by clivus / petroclival, sphenoid / cavernous sinus, anterior cranial fossa, medial cranial fossa, posterior cranial fossa, frontal / frontoparietal, frontotemporal, infratentorial / cerebellar, interhemispheric / suprasellar, intracisternal, intraventricular / parafalkyne, parietal / parietooccipital, supratentorial, temporal/temporoparietal." ]
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2f84abcda7ff942628a476ab24e6296c35d25199
# Dataset card for personSeg ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset description](#dataset-description) - [Dataset categories](#dataset-categories) ## Dataset description - **Homepage:** https://segments.ai/shahardekel/personSeg This dataset was created using [Segments.ai](https://segments.ai). It can be found [here](https://segments.ai/shahardekel/personSeg). ## Dataset categories | Id | Name | Description | | --- | ---- | ----------- | | 1 | person | - |
shahardekel/personSeg
[ "task_categories:image-segmentation", "region:us" ]
2023-02-12T18:40:19+00:00
{"task_categories": ["image-segmentation"]}
2023-02-14T14:38:54+00:00
[]
[]
TAGS #task_categories-image-segmentation #region-us
Dataset card for personSeg ========================== Table of Contents ----------------- * Table of Contents * Dataset description * Dataset categories Dataset description ------------------- * Homepage: URL This dataset was created using URL. It can be found here. Dataset categories ------------------ Id: 1, Name: person, Description: -
[]
[ "TAGS\n#task_categories-image-segmentation #region-us \n" ]
[ 18 ]
[ "passage: TAGS\n#task_categories-image-segmentation #region-us \n" ]
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fb87cbf30db41c4214e647a6bfac2f1dcb862c3e
RecipePairs dataset, originally from the 2022 EMNLP paper: ["SHARE: a System for Hierarchical Assistive Recipe Editing"](https://aclanthology.org/2022.emnlp-main.761/) by Shuyang Li, Yufei Li, Jianmo Ni, and Julian McAuley. This version (1.5.0) has been updated with 6.9M pairs of `base -> target` recipes, alongside their name overlap, IOU (longest common subsequence / union), and target dietary categories. These cover the 459K recipes from the original GeniusKitcen/Food.com dataset. If you would like to use this data or found it useful in your work/research, please cite the following papers: ``` @inproceedings{li-etal-2022-share, title = "{SHARE}: a System for Hierarchical Assistive Recipe Editing", author = "Li, Shuyang and Li, Yufei and Ni, Jianmo and McAuley, Julian", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.761", pages = "11077--11090", abstract = "The large population of home cooks with dietary restrictions is under-served by existing cooking resources and recipe generation models. To help them, we propose the task of controllable recipe editing: adapt a base recipe to satisfy a user-specified dietary constraint. This task is challenging, and cannot be adequately solved with human-written ingredient substitution rules or existing end-to-end recipe generation models. We tackle this problem with SHARE: a System for Hierarchical Assistive Recipe Editing, which performs simultaneous ingredient substitution before generating natural-language steps using the edited ingredients. By decoupling ingredient and step editing, our step generator can explicitly integrate the available ingredients. Experiments on the novel RecipePairs dataset{---}83K pairs of similar recipes where each recipe satisfies one of seven dietary constraints{---}demonstrate that SHARE produces convincing, coherent recipes that are appropriate for a target dietary constraint. We further show through human evaluations and real-world cooking trials that recipes edited by SHARE can be easily followed by home cooks to create appealing dishes.", } @inproceedings{majumder-etal-2019-generating, title = "Generating Personalized Recipes from Historical User Preferences", author = "Majumder, Bodhisattwa Prasad and Li, Shuyang and Ni, Jianmo and McAuley, Julian", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D19-1613", doi = "10.18653/v1/D19-1613", pages = "5976--5982", abstract = "Existing approaches to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes. We propose a new task of personalized recipe generation to help these users: expanding a name and incomplete ingredient details into complete natural-text instructions aligned with the user{'}s historical preferences. We attend on technique- and recipe-level representations of a user{'}s previously consumed recipes, fusing these {`}user-aware{'} representations in an attention fusion layer to control recipe text generation. Experiments on a new dataset of 180K recipes and 700K interactions show our model{'}s ability to generate plausible and personalized recipes compared to non-personalized baselines.", } ```
lishuyang/recipepairs
[ "task_categories:text-generation", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:gpl-3.0", "region:us" ]
2023-02-12T19:29:57+00:00
{"annotations_creators": "no-annotation", "language_creators": "found", "language": "en", "license": "gpl-3.0", "multilinguality": "monolingual", "size_categories": ["1M<n<10M"], "source_datasets": "original", "task_categories": ["text-generation"], "pretty_name": "RecipePairs", "dataset_info": [{"config_name": "1.5.0", "splits": [{"name": "pairs", "num_examples": 6908697}]}]}
2023-03-21T15:12:41+00:00
[]
[ "en" ]
TAGS #task_categories-text-generation #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-gpl-3.0 #region-us
RecipePairs dataset, originally from the 2022 EMNLP paper: "SHARE: a System for Hierarchical Assistive Recipe Editing" by Shuyang Li, Yufei Li, Jianmo Ni, and Julian McAuley. This version (1.5.0) has been updated with 6.9M pairs of 'base -> target' recipes, alongside their name overlap, IOU (longest common subsequence / union), and target dietary categories. These cover the 459K recipes from the original GeniusKitcen/URL dataset. If you would like to use this data or found it useful in your work/research, please cite the following papers:
[]
[ "TAGS\n#task_categories-text-generation #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-gpl-3.0 #region-us \n" ]
[ 57 ]
[ "passage: TAGS\n#task_categories-text-generation #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-gpl-3.0 #region-us \n" ]
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0e33fc0e87fa493ddf7ddd45ce1df0c68aa6ec4e
# Dataset Card for "dilbert-short-comic" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
akadhim-ai/dilbert-short-comic
[ "region:us" ]
2023-02-12T22:01:03+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 377934.0, "num_examples": 12}], "download_size": 379115, "dataset_size": 377934.0}}
2023-02-12T22:03:15+00:00
[]
[]
TAGS #region-us
# Dataset Card for "dilbert-short-comic" More Information needed
[ "# Dataset Card for \"dilbert-short-comic\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"dilbert-short-comic\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"dilbert-short-comic\"\n\nMore Information needed" ]
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9716a0cf5cd45494ff4afbc0915cd9a92a2b2320
# Dataset Card for "bookcorpus_compact_1024_shard8_of_10_meta" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
saibo/bookcorpus_compact_1024_shard8_of_10_meta
[ "region:us" ]
2023-02-12T23:21:10+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 7774340762, "num_examples": 61605}], "download_size": 1711444340, "dataset_size": 7774340762}}
2023-02-12T23:38:28+00:00
[]
[]
TAGS #region-us
# Dataset Card for "bookcorpus_compact_1024_shard8_of_10_meta" More Information needed
[ "# Dataset Card for \"bookcorpus_compact_1024_shard8_of_10_meta\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_compact_1024_shard8_of_10_meta\"\n\nMore Information needed" ]
[ 6, 29 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_compact_1024_shard8_of_10_meta\"\n\nMore Information needed" ]
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0677791870a30024336ec2685b29ca1f714764a2
# Dataset Card for "ID-SQuAD" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Rifky/ID-SQuAD
[ "region:us" ]
2023-02-13T01:37:38+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}], "splits": [{"name": "test", "num_bytes": 12218827, "num_examples": 11858}, {"name": "train", "num_bytes": 121632833, "num_examples": 130318}, {"name": "validation", "num_bytes": 12218827, "num_examples": 11858}], "download_size": 19391596, "dataset_size": 146070487}}
2023-04-08T03:55:02+00:00
[]
[]
TAGS #region-us
# Dataset Card for "ID-SQuAD" More Information needed
[ "# Dataset Card for \"ID-SQuAD\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"ID-SQuAD\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"ID-SQuAD\"\n\nMore Information needed" ]
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74d7b965343554ded234dd792af16eb57351d480
### Top10 sampled news category dataset randomly sampled news data original dataset: https://www.kaggle.com/datasets/rmisra/news-category-dataset ### Value Counts per Category ``` ENTERTAINMENT 10000 POLITICS 10000 WELLNESS 10000 TRAVEL 9900 STYLE & BEAUTY 9814 PARENTING 8791 HEALTHY LIVING 6694 QUEER VOICES 6347 FOOD & DRINK 6340 BUSINESS 5992 ```
heegyu/news-category-balanced-top10
[ "license:cc-by-4.0", "region:us" ]
2023-02-13T02:45:28+00:00
{"license": "cc-by-4.0"}
2023-02-13T02:56:31+00:00
[]
[]
TAGS #license-cc-by-4.0 #region-us
### Top10 sampled news category dataset randomly sampled news data original dataset: URL ### Value Counts per Category
[ "### Top10 sampled news category dataset\nrandomly sampled news data\noriginal dataset: URL", "### Value Counts per Category" ]
[ "TAGS\n#license-cc-by-4.0 #region-us \n", "### Top10 sampled news category dataset\nrandomly sampled news data\noriginal dataset: URL", "### Value Counts per Category" ]
[ 15, 21, 8 ]
[ "passage: TAGS\n#license-cc-by-4.0 #region-us \n### Top10 sampled news category dataset\nrandomly sampled news data\noriginal dataset: URL### Value Counts per Category" ]
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9fb691283b5a1ecbf1776ef7299f855334da5304
# Dataset Card for "pc_380" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
taldarim/pc_380
[ "region:us" ]
2023-02-13T04:39:33+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "Results interpretation", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "Frameworks usage", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "Algorithms design", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "Algorithms implementation", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "Launching problem", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "Performance issue", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "Feasibility of application development", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "Application availability", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "Device usage", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "Input data usage", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}], "splits": [{"name": "train", "num_bytes": 594651, "num_examples": 383}], "download_size": 281261, "dataset_size": 594651}}
2023-02-13T04:39:44+00:00
[]
[]
TAGS #region-us
# Dataset Card for "pc_380" More Information needed
[ "# Dataset Card for \"pc_380\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"pc_380\"\n\nMore Information needed" ]
[ 6, 13 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"pc_380\"\n\nMore Information needed" ]
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0af072d582c2f625185b9b4533b650d0a38ad63f
# Dataset Card for "contract" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jhn9803/contract
[ "region:us" ]
2023-02-13T04:56:37+00:00
{"dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "clean_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2618996, "num_examples": 7982}, {"name": "val", "num_bytes": 323947, "num_examples": 994}], "download_size": 987111, "dataset_size": 2942943}}
2023-02-13T04:57:02+00:00
[]
[]
TAGS #region-us
# Dataset Card for "contract" More Information needed
[ "# Dataset Card for \"contract\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"contract\"\n\nMore Information needed" ]
[ 6, 11 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"contract\"\n\nMore Information needed" ]
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cdf105f1bd1d70dce7e5a5d22470d6d118c8926d
[ { "review_id": "1", "product_id": "1234", "reviewer_id": "John Doe", "stars": 4, "review_body": "This product is great!", "review_title": "Fantastic Product!", "language": "en", "product_category": "Electronics" }, { "review_id": "2", "product_id": "5678", "reviewer_id": "Jane Doe", "stars": 5, "review_body": "This product is amazing!", "review_title": "Wonderful Product!", "language": "en", "product_category": "Apparel" }, // ... { "review_id": "1000", "product_id": "2468", "reviewer_id": "John Smith", "stars": 3, "review_body": "This product is alright.", "review_title": "Average Product!", "language": "en", "product_category": "Home & Kitchen" } ]
elhaddajiotmane/dar
[ "region:us" ]
2023-02-13T07:20:36+00:00
{}
2023-02-13T07:21:53+00:00
[]
[]
TAGS #region-us
[ { "review_id": "1", "product_id": "1234", "reviewer_id": "John Doe", "stars": 4, "review_body": "This product is great!", "review_title": "Fantastic Product!", "language": "en", "product_category": "Electronics" }, { "review_id": "2", "product_id": "5678", "reviewer_id": "Jane Doe", "stars": 5, "review_body": "This product is amazing!", "review_title": "Wonderful Product!", "language": "en", "product_category": "Apparel" }, // ... { "review_id": "1000", "product_id": "2468", "reviewer_id": "John Smith", "stars": 3, "review_body": "This product is alright.", "review_title": "Average Product!", "language": "en", "product_category": "Home & Kitchen" } ]
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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70780f6ab57b2cf897727755ab4a9ebf226c3c90
# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
arguezva/games
[ "region:us" ]
2023-02-13T07:52:01+00:00
{}
2023-02-13T07:52:38+00:00
[]
[]
TAGS #region-us
# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using this raw template. ### 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\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### 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\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### 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, 32, 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\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### 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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362c78b668ef3c8d121492ffdf77c83a0f4ecfc2
# Dataset Card for "simplewiki2023" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lsb/simplewiki2023
[ "region:us" ]
2023-02-13T08:42:44+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 275839740, "num_examples": 225332}], "download_size": 148218428, "dataset_size": 275839740}}
2023-02-13T08:43:12+00:00
[]
[]
TAGS #region-us
# Dataset Card for "simplewiki2023" More Information needed
[ "# Dataset Card for \"simplewiki2023\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"simplewiki2023\"\n\nMore Information needed" ]
[ 6, 14 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"simplewiki2023\"\n\nMore Information needed" ]
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e77e98b1d66afc0ec525fd5d031e54c40f491a4c
# Dataset Card for "jva-missions-report-raw" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jeveuxaider/jva-missions-report-raw
[ "region:us" ]
2023-02-13T09:28:57+00:00
{"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "Valid\u00e9e", "1": "Signal\u00e9e"}}}}, {"name": "idx", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22880285, "num_examples": 16433}], "download_size": 9897212, "dataset_size": 22880285}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2023-11-20T09:24:45+00:00
[]
[]
TAGS #region-us
# Dataset Card for "jva-missions-report-raw" More Information needed
[ "# Dataset Card for \"jva-missions-report-raw\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"jva-missions-report-raw\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"jva-missions-report-raw\"\n\nMore Information needed" ]
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e4d796df61352fc4b50c6f75e9f71602f4e3f7d5
## Example dataset card on playing video inside dataset cards <video loop autoplay controls src="https://huggingface.co/araffin/ppo-LunarLander-v2/resolve/main/replay.mp4"></video> Since datset cards support html, you can just use html video tag: ```html <video loop autoplay controls src="https://huggingface.co/araffin/ppo-LunarLander-v2/resolve/main/replay.mp4"></video> ``` note: change the src to your video. You can uplaod the demo video as part of your dataset as well & use it like `https://huggingface.co/{DATASET_OWNER}/{DATASET_NAME}/resolve/main/{VIDEO_PATH}.mp4`
mishig/tets_rl
[ "region:us" ]
2023-02-13T09:49:56+00:00
{}
2023-02-13T13:13:45+00:00
[]
[]
TAGS #region-us
## Example dataset card on playing video inside dataset cards <video loop autoplay controls src="URL Since datset cards support html, you can just use html video tag: note: change the src to your video. You can uplaod the demo video as part of your dataset as well & use it like 'URL
[ "## Example dataset card on playing video inside dataset cards\n\n<video loop autoplay controls src=\"URL\n\nSince datset cards support html, you can just use html video tag:\n\n\n\nnote: change the src to your video. You can uplaod the demo video as part of your dataset as well & use it like 'URL" ]
[ "TAGS\n#region-us \n", "## Example dataset card on playing video inside dataset cards\n\n<video loop autoplay controls src=\"URL\n\nSince datset cards support html, you can just use html video tag:\n\n\n\nnote: change the src to your video. You can uplaod the demo video as part of your dataset as well & use it like 'URL" ]
[ 6, 73 ]
[ "passage: TAGS\n#region-us \n## Example dataset card on playing video inside dataset cards\n\n<video loop autoplay controls src=\"URL\n\nSince datset cards support html, you can just use html video tag:\n\n\n\nnote: change the src to your video. You can uplaod the demo video as part of your dataset as well & use it like 'URL" ]
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fdcc6cd6a50d0bbf3be08af9beaab5436b73b448
# Dataset Card for Output ## 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) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/andstor/lm-output-dataset - **Repository:** https://github.com/andstor/lm-output-dataset - **Paper:** - **Leaderboard:** - **Point of Contact:** [André Storhaug](mailto:[email protected]) ### Dataset Summary This is a dataset of various language model outputs from different datasets. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@andstor](https://github.com/andstor) for adding this dataset.
andstor/output
[ "task_categories:text-generation", "language:en", "license:mit", "region:us" ]
2023-02-13T10:03:32+00:00
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2023-07-09T13:22:59+00:00
[]
[ "en" ]
TAGS #task_categories-text-generation #language-English #license-mit #region-us
# Dataset Card for Output ## 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 - 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 - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: - Leaderboard: - Point of Contact: André Storhaug ### Dataset Summary This is a dataset of various language model outputs from different datasets. ### 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 Thanks to @andstor for adding this dataset.
[ "# Dataset Card for Output", "## Table of Contents\n- 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 - Initial Data Collection and Normalization\n - Who are the source language producers?\n - Annotations\n - Annotation process\n - Who are the annotators?\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: URL\n- Repository: URL\n- Paper: \n- Leaderboard:\n- Point of Contact: André Storhaug", "### Dataset Summary\n\nThis is a dataset of various language model outputs from different datasets.", "### 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\n\nThanks to @andstor for adding this dataset." ]
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[ "passage: TAGS\n#task_categories-text-generation #language-English #license-mit #region-us \n# Dataset Card for Output## Table of Contents\n- 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 - Initial Data Collection and Normalization\n - Who are the source language producers?\n - Annotations\n - Annotation process\n - Who are the annotators?\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: URL\n- Repository: URL\n- Paper: \n- Leaderboard:\n- Point of Contact: André Storhaug### Dataset Summary\n\nThis is a dataset of various language model outputs from different datasets.### 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\n\nThanks to @andstor for adding this dataset." ]
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3ec568ef46a1a8695aa951671e6dac6b8fc5a449
# Dataset Card for "doguri" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
leeminxji/doguri
[ "region:us" ]
2023-02-13T10:24:07+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 211325.0, "num_examples": 32}], "download_size": 212377, "dataset_size": 211325.0}}
2023-02-14T02:42:32+00:00
[]
[]
TAGS #region-us
# Dataset Card for "doguri" More Information needed
[ "# Dataset Card for \"doguri\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"doguri\"\n\nMore Information needed" ]
[ 6, 12 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"doguri\"\n\nMore Information needed" ]
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05728aa81ddf264926211726ca7f16a2db7f0731
# `voc_superpixels_edge_wt_only_coord_10` ### Dataset Summary | Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric | |---|---|---|---|---|---| | PascalVOC-SP| Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 | | Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter |---|---:|---:|---:|:---:|---:|---:|---:|---:| | PascalVOC-SP| 11,355 | 5,443,545 | 479.40 | 5.65 | 30,777,444 | 2,710.48 | 10.74±0.51 | 27.62±2.13 | ## Additional Information ### Dataset Curators * Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75)) ### Licensing Information [Custom License](http://host.robots.ox.ac.uk/pascal/VOC/voc2011/index.html) for Pascal VOC 2011 (respecting Flickr terms of use) ### Citation Information ``` @article{dwivedi2022LRGB, title={Long Range Graph Benchmark}, author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique}, journal={arXiv:2206.08164}, year={2022} } ```
LRGB/voc_superpixels_edge_wt_only_coord_10
[ "task_categories:graph-ml", "size_categories:1M<n<10M", "lrgb", "region:us" ]
2023-02-13T11:12:15+00:00
{"size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"]}
2023-02-13T13:09:21+00:00
[]
[]
TAGS #task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us
'voc\_superpixels\_edge\_wt\_only\_coord\_10' ============================================= ### Dataset Summary Additional Information ---------------------- ### Dataset Curators * Vijay Prakash Dwivedi (vijaydwivedi75) ### Licensing Information Custom License for Pascal VOC 2011 (respecting Flickr terms of use)
[ "### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)", "### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
[ "TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us \n", "### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)", "### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
[ 32, 13, 17, 20 ]
[ "passage: TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us \n### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
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e8424a3f5c1233b821ce5967f34a69a7566694a0
# `voc_superpixels_edge_wt_only_coord_30` ### Dataset Summary | Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric | |---|---|---|---|---|---| | PascalVOC-SP| Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 | | Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter |---|---:|---:|---:|:---:|---:|---:|---:|---:| | PascalVOC-SP| 11,355 | 5,443,545 | 479.40 | 5.65 | 30,777,444 | 2,710.48 | 10.74±0.51 | 27.62±2.13 | ## Additional Information ### Dataset Curators * Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75)) ### Licensing Information [Custom License](http://host.robots.ox.ac.uk/pascal/VOC/voc2011/index.html) for Pascal VOC 2011 (respecting Flickr terms of use) ### Citation Information ``` @article{dwivedi2022LRGB, title={Long Range Graph Benchmark}, author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique}, journal={arXiv:2206.08164}, year={2022} } ```
LRGB/voc_superpixels_edge_wt_only_coord_30
[ "task_categories:graph-ml", "size_categories:1M<n<10M", "lrgb", "region:us" ]
2023-02-13T11:27:27+00:00
{"size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"]}
2023-02-13T13:15:58+00:00
[]
[]
TAGS #task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us
'voc\_superpixels\_edge\_wt\_only\_coord\_30' ============================================= ### Dataset Summary Additional Information ---------------------- ### Dataset Curators * Vijay Prakash Dwivedi (vijaydwivedi75) ### Licensing Information Custom License for Pascal VOC 2011 (respecting Flickr terms of use)
[ "### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)", "### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
[ "TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us \n", "### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)", "### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
[ 32, 13, 17, 20 ]
[ "passage: TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us \n### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
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145c9f04fa3e3ef458558546a127a9a97907ed60
## Dataset Description A subset of [the-stack](https://huggingface.co/datasets/bigcode/the-stack) dataset, from 87 programming languages, and 295 extensions. Each language is in a separate folder under `data/` and contains folders of its extensions. We select samples from 20,000 random files of the original dataset, and keep a maximum of 1,000 files per extension. Check this [space](https://huggingface.co/spaces/bigcode/the-stack-inspection) for inspecting this dataset. ## Languages The dataset contains 87 programming languages: ```` 'ada', 'agda', 'alloy', 'antlr', 'applescript', 'assembly', 'augeas', 'awk', 'batchfile', 'bison', 'bluespec', 'c', 'c++', 'c-sharp', 'clojure', 'cmake', 'coffeescript', 'common-lisp', 'css', 'cuda', 'dart', 'dockerfile', 'elixir', 'elm', 'emacs-lisp','erlang', 'f-sharp', 'fortran', 'glsl', 'go', 'groovy', 'haskell','html', 'idris', 'isabelle', 'java', 'java-server-pages', 'javascript', 'julia', 'kotlin', 'lean', 'literate-agda', 'literate-coffeescript', 'literate-haskell', 'lua', 'makefile', 'maple', 'markdown', 'mathematica', 'matlab', 'ocaml', 'pascal', 'perl', 'php', 'powershell', 'prolog', 'protocol-buffer', 'python', 'r', 'racket', 'restructuredtext', 'rmarkdown', 'ruby', 'rust', 'sas', 'scala', 'scheme', 'shell', 'smalltalk', 'solidity', 'sparql', 'sql', 'stan', 'standard-ml', 'stata', 'systemverilog', 'tcl', 'tcsh', 'tex', 'thrift', 'typescript', 'verilog', 'vhdl', 'visual-basic', 'xslt', 'yacc', 'zig' ````` ## Dataset Structure You can specify which language and extension you want to load: ```python # to load py extension of python from datasets import load_dataset load_dataset("bigcode/the-stack-inspection-data", data_dir="data/python/py") DatasetDict({ train: Dataset({ features: ['content', 'lang', 'size', 'ext', 'max_stars_count', 'avg_line_length', 'max_line_length', 'alphanum_fraction'], num_rows: 1000 }) }) ```
bigcode/the-stack-inspection-data
[ "task_categories:text-generation", "task_ids:language-modeling", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:unknown", "language:code", "region:us" ]
2023-02-13T11:49:13+00:00
{"annotations_creators": [], "language_creators": ["crowdsourced"], "language": ["code"], "multilinguality": ["multilingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["language-modeling"]}
2023-02-13T19:26:43+00:00
[]
[ "code" ]
TAGS #task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #multilinguality-multilingual #size_categories-unknown #language-code #region-us
## Dataset Description A subset of the-stack dataset, from 87 programming languages, and 295 extensions. Each language is in a separate folder under 'data/' and contains folders of its extensions. We select samples from 20,000 random files of the original dataset, and keep a maximum of 1,000 files per extension. Check this space for inspecting this dataset. ## Languages The dataset contains 87 programming languages: '' ## Dataset Structure You can specify which language and extension you want to load:
[ "## Dataset Description\nA subset of the-stack dataset, from 87 programming languages, and 295 extensions. \nEach language is in a separate folder under 'data/' and contains folders of its extensions. We select samples from 20,000 random files of the original dataset, and keep a \nmaximum of 1,000 files per extension.\n\nCheck this space for inspecting this dataset.", "## Languages\n\nThe dataset contains 87 programming languages:\n''", "## Dataset Structure\nYou can specify which language and extension you want to load:" ]
[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #multilinguality-multilingual #size_categories-unknown #language-code #region-us \n", "## Dataset Description\nA subset of the-stack dataset, from 87 programming languages, and 295 extensions. \nEach language is in a separate folder under 'data/' and contains folders of its extensions. We select samples from 20,000 random files of the original dataset, and keep a \nmaximum of 1,000 files per extension.\n\nCheck this space for inspecting this dataset.", "## Languages\n\nThe dataset contains 87 programming languages:\n''", "## Dataset Structure\nYou can specify which language and extension you want to load:" ]
[ 59, 85, 15, 19 ]
[ "passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #multilinguality-multilingual #size_categories-unknown #language-code #region-us \n## Dataset Description\nA subset of the-stack dataset, from 87 programming languages, and 295 extensions. \nEach language is in a separate folder under 'data/' and contains folders of its extensions. We select samples from 20,000 random files of the original dataset, and keep a \nmaximum of 1,000 files per extension.\n\nCheck this space for inspecting this dataset.## Languages\n\nThe dataset contains 87 programming languages:\n''## Dataset Structure\nYou can specify which language and extension you want to load:" ]
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6a9922e2caa328214a5d5da6d186fcfc766f95bd
# `voc_superpixels_edge_wt_coord_feat_10` ### Dataset Summary | Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric | |---|---|---|---|---|---| | PascalVOC-SP| Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 | | Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter |---|---:|---:|---:|:---:|---:|---:|---:|---:| | PascalVOC-SP| 11,355 | 5,443,545 | 479.40 | 5.65 | 30,777,444 | 2,710.48 | 10.74±0.51 | 27.62±2.13 | ## Additional Information ### Dataset Curators * Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75)) ### Licensing Information [Custom License](http://host.robots.ox.ac.uk/pascal/VOC/voc2011/index.html) for Pascal VOC 2011 (respecting Flickr terms of use) ### Citation Information ``` @article{dwivedi2022LRGB, title={Long Range Graph Benchmark}, author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique}, journal={arXiv:2206.08164}, year={2022} } ```
LRGB/voc_superpixels_edge_wt_coord_feat_10
[ "task_categories:graph-ml", "size_categories:1M<n<10M", "lrgb", "region:us" ]
2023-02-13T11:55:48+00:00
{"size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"]}
2023-03-01T10:34:24+00:00
[]
[]
TAGS #task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us
'voc\_superpixels\_edge\_wt\_coord\_feat\_10' ============================================= ### Dataset Summary Additional Information ---------------------- ### Dataset Curators * Vijay Prakash Dwivedi (vijaydwivedi75) ### Licensing Information Custom License for Pascal VOC 2011 (respecting Flickr terms of use)
[ "### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)", "### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
[ "TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us \n", "### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)", "### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
[ 32, 13, 17, 20 ]
[ "passage: TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us \n### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
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4edf8008ba3f31593fe0b2970b0ab33867e9a16f
# Dataset Card for "sst2-project-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Zombely/sst2-project-dataset
[ "region:us" ]
2023-02-13T11:58:57+00:00
{"dataset_info": {"features": [{"name": "idx", "dtype": "int32"}, {"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "negative", "1": "positive"}}}}], "splits": [{"name": "train", "num_bytes": 4570382.866070766, "num_examples": 65749}, {"name": "test", "num_bytes": 106252, "num_examples": 872}, {"name": "validation", "num_bytes": 111220.13392923429, "num_examples": 1600}], "download_size": 3178339, "dataset_size": 4787855.0}}
2023-02-13T11:59:06+00:00
[]
[]
TAGS #region-us
# Dataset Card for "sst2-project-dataset" More Information needed
[ "# Dataset Card for \"sst2-project-dataset\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"sst2-project-dataset\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"sst2-project-dataset\"\n\nMore Information needed" ]
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a2e5d63bc8148e1746580080dbf7f3aa09679d80
# `voc_superpixels_edge_wt_only_coord_30` ### Dataset Summary | Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric | |---|---|---|---|---|---| | PascalVOC-SP| Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 | | Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter |---|---:|---:|---:|:---:|---:|---:|---:|---:| | PascalVOC-SP| 11,355 | 5,443,545 | 479.40 | 5.65 | 30,777,444 | 2,710.48 | 10.74±0.51 | 27.62±2.13 | ## Additional Information ### Dataset Curators * Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75)) ### Licensing Information [Custom License](http://host.robots.ox.ac.uk/pascal/VOC/voc2011/index.html) for Pascal VOC 2011 (respecting Flickr terms of use) ### Citation Information ``` @article{dwivedi2022LRGB, title={Long Range Graph Benchmark}, author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique}, journal={arXiv:2206.08164}, year={2022} } ```
LRGB/voc_superpixels_edge_wt_coord_feat_30
[ "task_categories:graph-ml", "size_categories:1M<n<10M", "lrgb", "region:us" ]
2023-02-13T12:08:46+00:00
{"size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"]}
2023-02-13T12:13:11+00:00
[]
[]
TAGS #task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us
'voc\_superpixels\_edge\_wt\_only\_coord\_30' ============================================= ### Dataset Summary Additional Information ---------------------- ### Dataset Curators * Vijay Prakash Dwivedi (vijaydwivedi75) ### Licensing Information Custom License for Pascal VOC 2011 (respecting Flickr terms of use)
[ "### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)", "### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
[ "TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us \n", "### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)", "### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
[ 32, 13, 17, 20 ]
[ "passage: TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us \n### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
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e39135bfa15045afb4dcfc4ac8f741e9e6d7248f
# `voc_superpixels_edge_wt_region_boundary_10` ### Dataset Summary | Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric | |---|---|---|---|---|---| | PascalVOC-SP| Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 | | Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter |---|---:|---:|---:|:---:|---:|---:|---:|---:| | PascalVOC-SP| 11,355 | 5,443,545 | 479.40 | 5.65 | 30,777,444 | 2,710.48 | 10.74±0.51 | 27.62±2.13 | ## Additional Information ### Dataset Curators * Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75)) ### Licensing Information [Custom License](http://host.robots.ox.ac.uk/pascal/VOC/voc2011/index.html) for Pascal VOC 2011 (respecting Flickr terms of use) ### Citation Information ``` @article{dwivedi2022LRGB, title={Long Range Graph Benchmark}, author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique}, journal={arXiv:2206.08164}, year={2022} } ```
LRGB/voc_superpixels_edge_wt_region_boundary_10
[ "task_categories:graph-ml", "size_categories:1M<n<10M", "lrgb", "region:us" ]
2023-02-13T12:15:28+00:00
{"size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"]}
2023-02-13T12:26:01+00:00
[]
[]
TAGS #task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us
'voc\_superpixels\_edge\_wt\_region\_boundary\_10' ================================================== ### Dataset Summary Additional Information ---------------------- ### Dataset Curators * Vijay Prakash Dwivedi (vijaydwivedi75) ### Licensing Information Custom License for Pascal VOC 2011 (respecting Flickr terms of use)
[ "### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)", "### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
[ "TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us \n", "### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)", "### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
[ 32, 13, 17, 20 ]
[ "passage: TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us \n### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
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c81e391e38e4c11de88b004c4a0445387fe00dc9
# `voc_superpixels_edge_wt_region_boundary_30` ### Dataset Summary | Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric | |---|---|---|---|---|---| | PascalVOC-SP| Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 | | Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter |---|---:|---:|---:|:---:|---:|---:|---:|---:| | PascalVOC-SP| 11,355 | 5,443,545 | 479.40 | 5.65 | 30,777,444 | 2,710.48 | 10.74±0.51 | 27.62±2.13 | ## Additional Information ### Dataset Curators * Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75)) ### Licensing Information [Custom License](http://host.robots.ox.ac.uk/pascal/VOC/voc2011/index.html) for Pascal VOC 2011 (respecting Flickr terms of use) ### Citation Information ``` @article{dwivedi2022LRGB, title={Long Range Graph Benchmark}, author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique}, journal={arXiv:2206.08164}, year={2022} } ```
LRGB/voc_superpixels_edge_wt_region_boundary_30
[ "task_categories:graph-ml", "size_categories:1M<n<10M", "lrgb", "region:us" ]
2023-02-13T12:28:28+00:00
{"size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"]}
2023-02-13T12:37:10+00:00
[]
[]
TAGS #task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us
'voc\_superpixels\_edge\_wt\_region\_boundary\_30' ================================================== ### Dataset Summary Additional Information ---------------------- ### Dataset Curators * Vijay Prakash Dwivedi (vijaydwivedi75) ### Licensing Information Custom License for Pascal VOC 2011 (respecting Flickr terms of use)
[ "### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)", "### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
[ "TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us \n", "### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)", "### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
[ 32, 13, 17, 20 ]
[ "passage: TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #lrgb #region-us \n### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)### Licensing Information\n\n\nCustom License for Pascal VOC 2011 (respecting Flickr terms of use)" ]
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064486a15c7f1aa55d2ed9593c11e3afab2d223c
# Dataset Card for "historical_texts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nadav/historical_texts
[ "region:us" ]
2023-02-13T12:42:44+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "file", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6181333245, "num_examples": 40579}, {"name": "test", "num_bytes": 313259202, "num_examples": 2135}], "download_size": 3761925437, "dataset_size": 6494592447}}
2023-02-13T12:46:56+00:00
[]
[]
TAGS #region-us
# Dataset Card for "historical_texts" More Information needed
[ "# Dataset Card for \"historical_texts\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"historical_texts\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"historical_texts\"\n\nMore Information needed" ]
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