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4ef7f5285c3f710ed4165135f7bec9e5d3c14987 | # Dataset Card for "random_letter_find_passage_train30_eval10_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train30_eval10_rare | [
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| 2023-11-14T10:42:17+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6451, "num_examples": 70}, {"name": "validation", "num_bytes": 1147, "num_examples": 10}], "download_size": 6923, "dataset_size": 7598}} | 2023-11-14T10:42:25+00:00 | []
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3f5aedc8e6e9f6fa6167a976193fd2671f3da2db | # Dataset Card for "random_letter_find_passage_train30_eval10_num"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train30_eval10_num | [
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| 2023-11-14T10:42:39+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6140, "num_examples": 70}, {"name": "validation", "num_bytes": 1120, "num_examples": 10}], "download_size": 6514, "dataset_size": 7260}} | 2023-11-14T10:42:47+00:00 | []
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29e34cb320e47f6a076caf8e2d2c8e82b6a0e6ec | # Dataset Card for "random_letter_find_passage_train30_eval20_title"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train30_eval20_title | [
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| 2023-11-14T10:43:02+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8260, "num_examples": 80}, {"name": "validation", "num_bytes": 2522, "num_examples": 20}], "download_size": 8644, "dataset_size": 10782}} | 2023-11-14T10:43:08+00:00 | []
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aee796788d577ea598d457dc59ea2e34b56921cc | # Dataset Card for "random_letter_find_passage_train30_eval20_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train30_eval20_rare | [
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| 2023-11-14T10:43:21+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7214, "num_examples": 80}, {"name": "validation", "num_bytes": 2302, "num_examples": 20}], "download_size": 8080, "dataset_size": 9516}} | 2023-11-14T10:43:29+00:00 | []
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615b0dd42c74d09ebe432c32183627e5ea138f47 | # Dataset Card for "random_letter_find_passage_train30_eval20_num"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train30_eval20_num | [
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| 2023-11-14T10:43:43+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6860, "num_examples": 80}, {"name": "validation", "num_bytes": 2240, "num_examples": 20}], "download_size": 7612, "dataset_size": 9100}} | 2023-11-14T10:43:48+00:00 | []
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83d75cf4f220266340df94b205ab67a1809e5960 | # Dataset Card for "random_letter_find_passage_train30_eval40_title"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train30_eval40_title | [
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| 2023-11-14T10:44:04+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10027, "num_examples": 100}, {"name": "validation", "num_bytes": 5089, "num_examples": 40}], "download_size": 11357, "dataset_size": 15116}} | 2023-11-14T10:44:11+00:00 | []
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e5665432e69e641a9dbeaa3db62fec3164bb7394 | # Dataset Card for "random_letter_find_passage_train30_eval40_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train30_eval40_rare | [
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| 2023-11-14T10:44:25+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8722, "num_examples": 100}, {"name": "validation", "num_bytes": 4604, "num_examples": 40}], "download_size": 10644, "dataset_size": 13326}} | 2023-11-14T10:44:33+00:00 | []
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7c9835cd88aa3caa6b2241753b3d540822e5894b | # Dataset Card for "random_letter_find_passage_train30_eval40_num"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train30_eval40_num | [
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| 2023-11-14T10:44:47+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8300, "num_examples": 100}, {"name": "validation", "num_bytes": 4480, "num_examples": 40}], "download_size": 9763, "dataset_size": 12780}} | 2023-11-14T10:44:53+00:00 | []
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3c58ec9203ea348f119e7fb169769b8c59643494 | # Dataset Card for "random_letter_find_passage_train50_eval10_title"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train50_eval10_title | [
"region:us"
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| 2023-11-14T10:45:08+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11698, "num_examples": 110}, {"name": "validation", "num_bytes": 1268, "num_examples": 10}], "download_size": 8976, "dataset_size": 12966}} | 2023-11-14T10:45:16+00:00 | []
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2e8eca9b121c6ec82d4471433521fbff14c7a906 | # Dataset Card for "random_letter_find_passage_train50_eval10_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train50_eval10_rare | [
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| 2023-11-14T10:45:29+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10286, "num_examples": 110}, {"name": "validation", "num_bytes": 1150, "num_examples": 10}], "download_size": 8266, "dataset_size": 11436}} | 2023-11-14T10:45:35+00:00 | []
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More Information needed | [
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dfc453df0b0c5a8f134a06f0512b82016b820081 | # Dataset Card for "random_letter_find_passage_train50_eval10_num"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train50_eval10_num | [
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| 2023-11-14T10:45:50+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9780, "num_examples": 110}, {"name": "validation", "num_bytes": 1120, "num_examples": 10}], "download_size": 7680, "dataset_size": 10900}} | 2023-11-14T10:45:57+00:00 | []
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More Information needed | [
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1e460283e808783529152376a90270e20cf329e2 | # Dataset Card for "random_letter_find_passage_train50_eval20_title"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train50_eval20_title | [
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| 2023-11-14T10:46:11+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12552, "num_examples": 120}, {"name": "validation", "num_bytes": 2522, "num_examples": 20}], "download_size": 10247, "dataset_size": 15074}} | 2023-11-14T10:46:17+00:00 | []
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More Information needed | [
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84d7ced64de456629e98387969a6316c33c496b0 | # Dataset Card for "random_letter_find_passage_train50_eval20_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train50_eval20_rare | [
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| 2023-11-14T10:46:31+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10984, "num_examples": 120}, {"name": "validation", "num_bytes": 2298, "num_examples": 20}], "download_size": 9499, "dataset_size": 13282}} | 2023-11-14T10:46:36+00:00 | []
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More Information needed | [
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ab02793979690ea542b61b05ee302d67873a1f24 | # Dataset Card for "random_letter_find_passage_train50_eval20_num"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train50_eval20_num | [
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| 2023-11-14T10:46:51+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10500, "num_examples": 120}, {"name": "validation", "num_bytes": 2240, "num_examples": 20}], "download_size": 8784, "dataset_size": 12740}} | 2023-11-14T10:47:00+00:00 | []
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34c046aa01cfaeb003a65e0a469d9045b25c77d3 | # Dataset Card for "random_letter_find_passage_train50_eval40_title"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train50_eval40_title | [
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| 2023-11-14T10:47:15+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14319, "num_examples": 140}, {"name": "validation", "num_bytes": 5089, "num_examples": 40}], "download_size": 12791, "dataset_size": 19408}} | 2023-11-14T10:47:22+00:00 | []
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"passage: TAGS\n#region-us \n# Dataset Card for \"random_letter_find_passage_train50_eval40_title\"\n\nMore Information needed"
]
|
7025ce9e2965ee39a81351e14086ca9974a2db8a | # Dataset Card for "random_letter_find_passage_train50_eval40_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train50_eval40_rare | [
"region:us"
]
| 2023-11-14T10:47:37+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12528, "num_examples": 140}, {"name": "validation", "num_bytes": 4600, "num_examples": 40}], "download_size": 11793, "dataset_size": 17128}} | 2023-11-14T10:47:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "random_letter_find_passage_train50_eval40_rare"
More Information needed | [
"# Dataset Card for \"random_letter_find_passage_train50_eval40_rare\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
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8aa72fee2639d3879efaf25a1343ab25d341c265 | # Dataset Card for "random_letter_find_passage_train50_eval40_num"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train50_eval40_num | [
"region:us"
]
| 2023-11-14T10:47:59+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11940, "num_examples": 140}, {"name": "validation", "num_bytes": 4480, "num_examples": 40}], "download_size": 10906, "dataset_size": 16420}} | 2023-11-14T10:48:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "random_letter_find_passage_train50_eval40_num"
More Information needed | [
"# Dataset Card for \"random_letter_find_passage_train50_eval40_num\"\n\nMore Information needed"
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d37bed6c8d8edd5adce231aab451d9f95415112c | # Dataset Card for "random_letter_find_passage_train100_eval10_title"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train100_eval10_title | [
"region:us"
]
| 2023-11-14T10:48:21+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22546, "num_examples": 210}, {"name": "validation", "num_bytes": 1268, "num_examples": 10}], "download_size": 0, "dataset_size": 23814}} | 2023-11-16T04:47:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "random_letter_find_passage_train100_eval10_title"
More Information needed | [
"# Dataset Card for \"random_letter_find_passage_train100_eval10_title\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"random_letter_find_passage_train100_eval10_title\"\n\nMore Information needed"
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|
b3435b4a57a8672c117918bf3c2117b5efa1e462 | # Dataset Card for "random_letter_find_passage_train100_eval10_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train100_eval10_rare | [
"region:us"
]
| 2023-11-14T10:48:41+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19797, "num_examples": 210}, {"name": "validation", "num_bytes": 1151, "num_examples": 10}], "download_size": 11558, "dataset_size": 20948}} | 2023-11-16T04:47:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "random_letter_find_passage_train100_eval10_rare"
More Information needed | [
"# Dataset Card for \"random_letter_find_passage_train100_eval10_rare\"\n\nMore Information needed"
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d047e7e943d3218867db256ef651ceba50375b18 | # Dataset Card for "random_letter_find_passage_train100_eval10_num"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train100_eval10_num | [
"region:us"
]
| 2023-11-14T10:49:03+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18880, "num_examples": 210}, {"name": "validation", "num_bytes": 1120, "num_examples": 10}], "download_size": 0, "dataset_size": 20000}} | 2023-11-16T04:47:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "random_letter_find_passage_train100_eval10_num"
More Information needed | [
"# Dataset Card for \"random_letter_find_passage_train100_eval10_num\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
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"passage: TAGS\n#region-us \n# Dataset Card for \"random_letter_find_passage_train100_eval10_num\"\n\nMore Information needed"
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|
22716e8a2e079b75d7999bdfd5016dc69c02d42a | # Dataset Card for "random_letter_find_passage_train100_eval20_title"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train100_eval20_title | [
"region:us"
]
| 2023-11-14T10:49:27+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 23400, "num_examples": 220}, {"name": "validation", "num_bytes": 2522, "num_examples": 20}], "download_size": 0, "dataset_size": 25922}} | 2023-11-16T04:48:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "random_letter_find_passage_train100_eval20_title"
More Information needed | [
"# Dataset Card for \"random_letter_find_passage_train100_eval20_title\"\n\nMore Information needed"
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| [
"TAGS\n#region-us \n",
"# Dataset Card for \"random_letter_find_passage_train100_eval20_title\"\n\nMore Information needed"
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|
5890fa10490b70ca319f5f4acda8887173229eaa | # Dataset Card for "random_letter_find_passage_train100_eval20_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train100_eval20_rare | [
"region:us"
]
| 2023-11-14T10:49:49+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20551, "num_examples": 220}, {"name": "validation", "num_bytes": 2307, "num_examples": 20}], "download_size": 12831, "dataset_size": 22858}} | 2023-11-16T04:48:26+00:00 | []
| []
| TAGS
#region-us
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More Information needed | [
"# Dataset Card for \"random_letter_find_passage_train100_eval20_rare\"\n\nMore Information needed"
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|
1e478140d54f4a907c054988a62aa26c5c502292 | # Dataset Card for "random_letter_find_passage_train100_eval20_num"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train100_eval20_num | [
"region:us"
]
| 2023-11-14T10:50:11+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19600, "num_examples": 220}, {"name": "validation", "num_bytes": 2240, "num_examples": 20}], "download_size": 0, "dataset_size": 21840}} | 2023-11-16T04:48:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "random_letter_find_passage_train100_eval20_num"
More Information needed | [
"# Dataset Card for \"random_letter_find_passage_train100_eval20_num\"\n\nMore Information needed"
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"# Dataset Card for \"random_letter_find_passage_train100_eval20_num\"\n\nMore Information needed"
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|
400e6a098d8068ebd95bb13a47df32c15164de06 | # Dataset Card for "random_letter_find_passage_train100_eval40_title"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train100_eval40_title | [
"region:us"
]
| 2023-11-14T10:50:32+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25167, "num_examples": 240}, {"name": "validation", "num_bytes": 5089, "num_examples": 40}], "download_size": 0, "dataset_size": 30256}} | 2023-11-16T04:49:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "random_letter_find_passage_train100_eval40_title"
More Information needed | [
"# Dataset Card for \"random_letter_find_passage_train100_eval40_title\"\n\nMore Information needed"
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"# Dataset Card for \"random_letter_find_passage_train100_eval40_title\"\n\nMore Information needed"
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|
ff495248b12ea467b257a622d36b6b50a4d4c65e | # Dataset Card for "random_letter_find_passage_train100_eval40_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train100_eval40_rare | [
"region:us"
]
| 2023-11-14T10:50:52+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22059, "num_examples": 240}, {"name": "validation", "num_bytes": 4591, "num_examples": 40}], "download_size": 15185, "dataset_size": 26650}} | 2023-11-16T04:49:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "random_letter_find_passage_train100_eval40_rare"
More Information needed | [
"# Dataset Card for \"random_letter_find_passage_train100_eval40_rare\"\n\nMore Information needed"
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9c81339afac38ec2471077a6df65624747f89269 | # Dataset Card for "random_letter_find_passage_train100_eval40_num"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random_letter_find_passage_train100_eval40_num | [
"region:us"
]
| 2023-11-14T10:51:15+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21040, "num_examples": 240}, {"name": "validation", "num_bytes": 4480, "num_examples": 40}], "download_size": 0, "dataset_size": 25520}} | 2023-11-16T04:49:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "random_letter_find_passage_train100_eval40_num"
More Information needed | [
"# Dataset Card for \"random_letter_find_passage_train100_eval40_num\"\n\nMore Information needed"
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|
cd67364ca7fc048490dbbcdbdab3fc61fa35d4ed | # Dataset Card for "ultrafeedback_binarized_full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | argilla/ultrafeedback_binarized_full | [
"region:us"
]
| 2023-11-14T10:51:49+00:00 | {"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "best_response", "struct": [{"name": "annotations", "struct": [{"name": "helpfulness", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "Rationale For Rating", "dtype": "string"}, {"name": "Type", "sequence": "string"}]}, {"name": "honesty", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}]}, {"name": "instruction_following", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}]}, {"name": "truthfulness", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "Rationale For Rating", "dtype": "string"}, {"name": "Type", "sequence": "string"}]}]}, {"name": "critique", "dtype": "string"}, {"name": "custom_system_prompt", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "overall_score", "dtype": "float64"}, {"name": "principle", "dtype": "string"}, {"name": "response", "dtype": "string"}]}, {"name": "best_model", "dtype": "string"}, {"name": "best_score", "dtype": "float64"}, {"name": "random_response", "struct": [{"name": "annotations", "struct": [{"name": "helpfulness", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "Rationale For Rating", "dtype": "string"}, {"name": "Type", "sequence": "string"}]}, {"name": "honesty", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}]}, {"name": "instruction_following", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}]}, {"name": "truthfulness", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "Rationale For Rating", "dtype": "string"}, {"name": "Type", "sequence": "string"}]}]}, {"name": "critique", "dtype": "string"}, {"name": "custom_system_prompt", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "overall_score", "dtype": "float64"}, {"name": "principle", "dtype": "string"}, {"name": "response", "dtype": "string"}]}, {"name": "random_model", "dtype": "string"}, {"name": "random_score", "dtype": "float64"}, {"name": "correct_answers", "sequence": "string"}, {"name": "incorrect_answers", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 447221757, "num_examples": 63967}], "download_size": 199896433, "dataset_size": 447221757}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-14T10:52:04+00:00 | []
| []
| TAGS
#region-us
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More Information needed | [
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|
bc570e84b9223e8c120de27dcf3e4184e625f819 |
# Dataset Card for ALMA-prompt-completion
[ALMA Dataset](https://github.com/fe1ixxu/ALMA/tree/master/human_written_data) if format of [prompt-completion](https://github.com/higgsfield-ai/higgsfield/tree/main/tutorials)
- **Created by:** fe1ixxu
- **Shared by:** me
- **Language(s) (NLP):** English, Czech, German, Russian, Islandic, Chinese
- **License:** MIT
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [https://github.com/fe1ixxu/ALMA]
- **Paper [optional]:** [https://arxiv.org/abs/2309.11674]
## Uses
LLM translators | kristaller486/ALMA-prompt-completion | [
"task_categories:translation",
"size_categories:100K<n<1M",
"language:en",
"language:ru",
"language:cs",
"language:de",
"language:is",
"language:zh",
"license:mit",
"arxiv:2309.11674",
"region:us"
]
| 2023-11-14T10:58:43+00:00 | {"language": ["en", "ru", "cs", "de", "is", "zh"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["translation"], "pretty_name": "ALMA Dataset"} | 2023-11-15T08:43:52+00:00 | [
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]
| [
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| TAGS
#task_categories-translation #size_categories-100K<n<1M #language-English #language-Russian #language-Czech #language-German #language-Icelandic #language-Chinese #license-mit #arxiv-2309.11674 #region-us
|
# Dataset Card for ALMA-prompt-completion
ALMA Dataset if format of prompt-completion
- Created by: fe1ixxu
- Shared by: me
- Language(s) (NLP): English, Czech, German, Russian, Islandic, Chinese
- License: MIT
### Dataset Sources [optional]
- Repository: [URL
- Paper [optional]: [URL
## Uses
LLM translators | [
"# Dataset Card for ALMA-prompt-completion\n ALMA Dataset if format of prompt-completion\n\n- Created by: fe1ixxu\n- Shared by: me\n- Language(s) (NLP): English, Czech, German, Russian, Islandic, Chinese\n- License: MIT",
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"# Dataset Card for ALMA-prompt-completion\n ALMA Dataset if format of prompt-completion\n\n- Created by: fe1ixxu\n- Shared by: me\n- Language(s) (NLP): English, Czech, German, Russian, Islandic, Chinese\n- License: MIT",
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|
eadd07bf5c090d8b74f959fa839e35a8811b6ac9 | # Dataset Card for "zephyr-7b-beta-judgelm-new-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | alvarobartt/zephyr-7b-beta-judgelm-new-test | [
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| 2023-11-14T10:59:41+00:00 | {"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "models", "sequence": "string"}, {"name": "completions", "list": [{"name": "annotations", "struct": [{"name": "instruction_following", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}]}, {"name": "honesty", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}]}, {"name": "truthfulness", "struct": [{"name": "Type", "sequence": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "Rating", "dtype": "string"}, {"name": "Rationale For Rating", "dtype": "string"}]}, {"name": "helpfulness", "struct": [{"name": "Type", "sequence": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "Rating", "dtype": "string"}, {"name": "Rationale For Rating", "dtype": "string"}]}]}, {"name": "custom_system_prompt", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "principle", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "critique", "dtype": "string"}, {"name": "overall_score", "dtype": "float64"}]}, {"name": "correct_answers", "sequence": "string"}, {"name": "incorrect_answers", "sequence": "string"}, {"name": "generation_model", "dtype": "string"}, {"name": "generation_prompt", "dtype": "string"}, {"name": "raw_generation_responses", "sequence": "string"}, {"name": "generations", "sequence": "string"}, {"name": "labelling_model", "dtype": "string"}, {"name": "labelling_prompt", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "raw_labelling_response", "dtype": "string"}, {"name": "ratings", "sequence": "int64"}, {"name": "rationale", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 61495, "num_examples": 2}], "download_size": 107464, "dataset_size": 61495}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-17T14:13:43+00:00 | []
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38e3bc3411aaba1073d3ad97ff8701dc0297659b | # Dataset Card for "alt_tentacles"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mickume/alt_tentacles | [
"region:us"
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| 2023-11-14T11:06:06+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 89128469, "num_examples": 432634}], "download_size": 54523298, "dataset_size": 89128469}} | 2023-11-14T11:09:52+00:00 | []
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More Information needed | [
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730180366d10a45e75c5676c31b58ec0df0b89ce | # Dataset Card for "SDv2-GPT4Spatial-2000-filtered2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Doub7e/SDv2-GPT4Spatial-2000-filtered2 | [
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| 2023-11-14T11:20:31+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "T5_last_hidden_states", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "train", "num_bytes": 592374789.0, "num_examples": 461}], "download_size": 589571360, "dataset_size": 592374789.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-14T11:21:05+00:00 | []
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d53aff53e3f0cb9b3deb988d736a78bccbbeb01b |
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
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).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | sky2708/albis_chat_dataset | [
"region:us"
]
| 2023-11-14T11:39:50+00:00 | {} | 2023-11-14T13:00:02+00:00 | []
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7231021419b14e2d1d4bcf24886ffa7d27ed7db3 | # Dataset Card for "SingerVerification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | DynamicSuperb/SingerVerification_M4Singer | [
"region:us"
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| 2023-11-14T12:26:08+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "file", "dtype": "string"}, {"name": "audio", "dtype": "audio"}, {"name": "file2", "dtype": "string"}, {"name": "audio2", "dtype": "audio"}, {"name": "instruction", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 729851376.0, "num_examples": 2000}], "download_size": 700165696, "dataset_size": 729851376.0}} | 2023-11-15T00:35:33+00:00 | []
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| TAGS
#region-us
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More Information needed | [
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d9a05773594405781a98c0e1a4c924d25e0a6d25 | # Dataset Card for "rewrite_instructions_bu"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Eitanli/rewrite_instructions_bu | [
"region:us"
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| 2023-11-14T12:30:51+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "recipe", "dtype": "string"}, {"name": "instructions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 160548334, "num_examples": 74401}], "download_size": 81393986, "dataset_size": 160548334}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-12-02T19:08:07+00:00 | []
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#region-us
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More Information needed | [
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7eacb6719d04fe42aed71325f47cace1e60d7be6 | # Dataset Card for "ru-paraphrases"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | wyluilipe/ru-paraphrases | [
"region:us"
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| 2023-11-14T12:49:38+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "original", "dtype": "string"}, {"name": "paraphrase", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 687174801.2, "num_examples": 1881000}, {"name": "test", "num_bytes": 36167094.8, "num_examples": 99000}], "download_size": 421032139, "dataset_size": 723341896.0}} | 2023-11-14T12:52:24+00:00 | []
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More Information needed | [
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0acbdfc594624fbad56bec9b3920a22e141d97d3 | # Dataset Card for "esg3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bh8648/esg3 | [
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| 2023-11-14T12:54:25+00:00 | {"dataset_info": {"features": [{"name": "Major Category", "dtype": "string"}, {"name": "Middle Categoty", "dtype": "string"}, {"name": "Small Category", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 222466, "num_examples": 56}], "download_size": 107170, "dataset_size": 222466}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-14T12:54:27+00:00 | []
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f2364081aaf3c3df8fa79ba55f89d9e387ccc1b6 | # Dataset Card for "no_robots"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rishiraj/no_robots | [
"region:us"
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| 2023-11-14T13:34:00+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28805395, "num_examples": 9500}, {"name": "test", "num_bytes": 1545168, "num_examples": 500}], "download_size": 18891461, "dataset_size": 30350563}} | 2023-11-14T13:34:01+00:00 | []
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104a50660a81c71d6be67ee73a78352f52416d73 | # Dataset Card for "ultrachat_200k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rishiraj/ultrachat_200k | [
"region:us"
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| 2023-11-14T13:39:46+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2609053656, "num_examples": 207865}, {"name": "test", "num_bytes": 288657697, "num_examples": 23110}], "download_size": 1486981342, "dataset_size": 2897711353}} | 2023-11-14T13:40:27+00:00 | []
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71537460af351d9716a83300e90fd9a7ed92f5f4 | # Dataset Card for "hh-rlhf_with_features_flan_t5_large_max_5_xl_zeroshot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dongyoung4091/hh-rlhf_with_features_flan_t5_large_max_5_xl_zeroshot | [
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#region-us
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More Information needed | [
"# Dataset Card for \"hh-rlhf_with_features_flan_t5_large_max_5_xl_zeroshot\"\n\nMore Information needed"
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e9c5fc88dbb17fd375d5bffd6d4fb9e3a048f248 | # Dataset Card for "esg1to3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bh8648/esg1to3 | [
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| 2023-11-14T14:01:10+00:00 | {"dataset_info": {"features": [{"name": "Major Category", "dtype": "string"}, {"name": "Middle Category", "dtype": "string"}, {"name": "Small Category", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 690585, "num_examples": 170}], "download_size": 339311, "dataset_size": 690585}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-14T14:01:13+00:00 | []
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|
9ced3e6d44366f78a05edd0ba693a1240b570f0a | # Dataset Card for "hh-generated_flan_t5_large_flan_t5_zeroshot_DA_Bard"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dongyoung4091/hh-generated_flan_t5_large_flan_t5_zeroshot_DA_Bard | [
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| 2023-11-14T14:02:28+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "zeroshot_helpfulness", "dtype": "float64"}, {"name": "zeroshot_specificity", "dtype": "float64"}, {"name": "zeroshot_intent", "dtype": "float64"}, {"name": "zeroshot_factuality", "dtype": "float64"}, {"name": "zeroshot_easy-to-understand", "dtype": "float64"}, {"name": "zeroshot_relevance", "dtype": "float64"}, {"name": "zeroshot_readability", "dtype": "float64"}, {"name": "zeroshot_enough-detail", "dtype": "float64"}, {"name": "zeroshot_biased:", "dtype": "float64"}, {"name": "zeroshot_fail-to-consider-individual-preferences", "dtype": "float64"}, {"name": "zeroshot_repetetive", "dtype": "float64"}, {"name": "zeroshot_fail-to-consider-context", "dtype": "float64"}, {"name": "zeroshot_too-long", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 6336357, "num_examples": 25600}], "download_size": 801358, "dataset_size": 6336357}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-14T14:30:07+00:00 | []
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#region-us
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More Information needed | [
"# Dataset Card for \"hh-generated_flan_t5_large_flan_t5_zeroshot_DA_Bard\"\n\nMore Information needed"
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37b6a3207d48d533e1ada0d9188eea9ebb59c2ea | # Dataset Card for "hh-generated_flan_t5_large_flan_t5_zeroshot_max_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dongyoung4091/hh-generated_flan_t5_large_flan_t5_zeroshot_max_5 | [
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| 2023-11-14T14:03:38+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "zeroshot_helpfulness", "dtype": "float64"}, {"name": "zeroshot_specificity", "dtype": "float64"}, {"name": "zeroshot_intent", "dtype": "float64"}, {"name": "zeroshot_factuality", "dtype": "float64"}, {"name": "zeroshot_easy-to-understand", "dtype": "float64"}, {"name": "zeroshot_relevance", "dtype": "float64"}, {"name": "zeroshot_readability", "dtype": "float64"}, {"name": "zeroshot_enough-detail", "dtype": "float64"}, {"name": "zeroshot_biased:", "dtype": "float64"}, {"name": "zeroshot_fail-to-consider-individual-preferences", "dtype": "float64"}, {"name": "zeroshot_repetetive", "dtype": "float64"}, {"name": "zeroshot_fail-to-consider-context", "dtype": "float64"}, {"name": "zeroshot_too-long", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 6336357, "num_examples": 25600}], "download_size": 0, "dataset_size": 6336357}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-14T14:30:15+00:00 | []
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|
2e6480c737f937091e9e5c48788b7fa4d1cac580 | # Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
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).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | vetertann/promease_chat | [
"language:ru",
"license:mit",
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| 2023-11-14T14:06:22+00:00 | {"language": ["ru"], "license": "mit", "pretty_name": "vp_train"} | 2023-11-17T19:18:31+00:00 | []
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- Funded by [optional]:
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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adc272eeab9b74a4a8d89490b2f44584683c8d9f | # Dataset Card for "hh-rlhf_with_features_flan_t5_large_DA_Bard_xl_zeroshot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dongyoung4091/hh-rlhf_with_features_flan_t5_large_DA_Bard_xl_zeroshot | [
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| 2023-11-14T14:07:27+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}, {"name": "helpfulness_chosen", "dtype": "int64"}, {"name": "helpfulness_rejected", "dtype": "int64"}, {"name": "specificity_chosen", "dtype": "int64"}, {"name": "specificity_rejected", "dtype": "int64"}, {"name": "intent_chosen", "dtype": "int64"}, {"name": "intent_rejected", "dtype": "int64"}, {"name": "factuality_chosen", "dtype": "int64"}, {"name": "factuality_rejected", "dtype": "int64"}, {"name": "easy-to-understand_chosen", "dtype": "int64"}, {"name": "easy-to-understand_rejected", "dtype": "int64"}, {"name": "relevance_chosen", "dtype": "int64"}, {"name": "relevance_rejected", "dtype": "int64"}, {"name": "readability_chosen", "dtype": "int64"}, {"name": "readability_rejected", "dtype": "int64"}, {"name": "enough-detail_chosen", "dtype": "int64"}, {"name": "enough-detail_rejected", "dtype": "int64"}, {"name": "biased:_chosen", "dtype": "int64"}, {"name": "biased:_rejected", "dtype": "int64"}, {"name": "fail-to-consider-individual-preferences_chosen", "dtype": "int64"}, {"name": "fail-to-consider-individual-preferences_rejected", "dtype": "int64"}, {"name": "repetetive_chosen", "dtype": "int64"}, {"name": "repetetive_rejected", "dtype": "int64"}, {"name": "fail-to-consider-context_chosen", "dtype": "int64"}, {"name": "fail-to-consider-context_rejected", "dtype": "int64"}, {"name": "too-long_chosen", "dtype": "int64"}, {"name": "too-long_rejected", "dtype": "int64"}, {"name": "human", "dtype": "string"}, {"name": "assistant_chosen", "dtype": "string"}, {"name": "assistant_rejected", "dtype": "string"}, {"name": "log_score_chosen", "dtype": "float64"}, {"name": "log_score_rejected", "dtype": "float64"}, {"name": "labels", "dtype": "string"}, {"name": "zeroshot_helpfulness_chosen", "dtype": "int64"}, {"name": "zeroshot_helpfulness_rejected", "dtype": "int64"}, {"name": "zeroshot_specificity_chosen", "dtype": "int64"}, {"name": "zeroshot_specificity_rejected", "dtype": "int64"}, {"name": "zeroshot_intent_chosen", "dtype": "int64"}, {"name": "zeroshot_intent_rejected", "dtype": "int64"}, {"name": "zeroshot_factuality_chosen", "dtype": "int64"}, {"name": "zeroshot_factuality_rejected", "dtype": "int64"}, {"name": "zeroshot_easy-to-understand_chosen", "dtype": "int64"}, {"name": "zeroshot_easy-to-understand_rejected", "dtype": "int64"}, {"name": "zeroshot_relevance_chosen", "dtype": "int64"}, {"name": "zeroshot_relevance_rejected", "dtype": "int64"}, {"name": "zeroshot_readability_chosen", "dtype": "int64"}, {"name": "zeroshot_readability_rejected", "dtype": "int64"}, {"name": "zeroshot_enough-detail_chosen", "dtype": "int64"}, {"name": "zeroshot_enough-detail_rejected", "dtype": "int64"}, {"name": "zeroshot_biased:_chosen", "dtype": "int64"}, {"name": "zeroshot_biased:_rejected", "dtype": "int64"}, {"name": "zeroshot_fail-to-consider-individual-preferences_chosen", "dtype": "int64"}, {"name": "zeroshot_fail-to-consider-individual-preferences_rejected", "dtype": "int64"}, {"name": "zeroshot_repetetive_chosen", "dtype": "int64"}, {"name": "zeroshot_repetetive_rejected", "dtype": "int64"}, {"name": "zeroshot_fail-to-consider-context_chosen", "dtype": "int64"}, {"name": "zeroshot_fail-to-consider-context_rejected", "dtype": "int64"}, {"name": "zeroshot_too-long_chosen", "dtype": "int64"}, {"name": "zeroshot_too-long_rejected", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 16425816, "num_examples": 9574}, {"name": "test", "num_bytes": 16369741, "num_examples": 9574}], "download_size": 16118427, "dataset_size": 32795557}} | 2023-11-14T14:32:01+00:00 | []
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|
38b9b59117ca780b1df0e60b9b314c07a013eda8 |
# MC^2: A Multilingual Corpus of Minority Languages in China
We present MC^2, a **M**ultilingual **C**orpus of **M**inority Languages in **C**hina, which is the largest open-source corpus so far. This corpus encompasses four languages, namely Tibetan, Uyghur, Kazakh written in the Kazakh Arabic script, and Mongolian written in the traditional Mongolian script.
Our preprint is now on [Arxiv](https://arxiv.org/abs/2311.08348).
The processing scripts are to be released on our [Github Repo](https://github.com/luciusssss/mc2_corpus).
## Languages and Sizes
There are four minority languages in the dataset, and we report the dataset sizes below:
| | MC^2 (crawl) | MC^2 (full) |
| --------------------------- | ------------ | ----------- |
| **Tibetan** | 1.7G | 2.2G |
| **Uyghur** | 520M | 736M |
| **Kazakh (Arabic)** | 397M | 937M |
| **Mongolian (Traditional)** | 874M | 874M |
MC^2 (crawl) denotes the subset of our newly-collected web crawls. MC^2 (full) is the complete set of MC^2, which additionally contains texts collected from existing resources.
## Dataset Structure
The dataset is in JSON format, with each line containing one entry with three keys: `title`, `text`, and `url`.
This is an example:
```
{
"title":"پارتيانىڭ مەملەكەتتىك 19 - قۇرىلتايىنىڭ ورىنباسار باس حاتشىلارى",
"text":"ليۋ چيباۋ، مىڭ جيانجۋ، جاۋ لىجي، لي جانشۋ\n\n\n(شينحۋا اگەنتتىگىنىڭ 17 - قازاندا بەيجيڭنەن بەرگەن حابارى)",
"url":"kazakh.altxw.com\/system\/2017\/10\/24\/030007713.shtml"
}
```
## How to Obtain the Data
Our data mainly contains three parts.
You can download our web-crawled data from [Hugging Face](https://huggingface.co/datasets/pkupie/mc2_corpus).
For data from [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) and [Wikipedia](https://huggingface.co/datasets/graelo/wikipedia), you can download and then process them using scripts in [our Github Repo](https://github.com/luciusssss/mc2_corpus/tree/main#how-to-obtain-the-data).
## License Information
We released the data under the [Creative Commons CC0 license](http://creativecommons.org/publicdomain/zero/1.0/).
```
These data are released under this licensing scheme
We do not own any of the text from which these data have been extracted.
We license the data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
To the extent possible under law, Peking University has waived all copyright and related or neighboring rights to MC^2.
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number, or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
We will comply with legitimate requests by removing the affected sources from the next release of the corpus.
```
## Citation Information
```
@article{zhang2023mc2,
title={MC^2: A Multilingual Corpus of Minority Languages in China},
author={Chen Zhang and Mingxu Tao and Quzhe Huang and Jiuheng Lin and Zhibin Chen and Yansong Feng},
journal={ArXiv},
year={2023},
volume={abs/2311.08348}
}
```
## Contributors
We thank [Chen Zhang](https://luciusssss.github.io/)\*, [Mingxu Tao](https://kobayashikanna01.github.io/)\*, [Quzhe Huang](https://andrewzhe.github.io/)\*, [Jiuheng Lin](https://github.com/Infinite-set)\*, [Zhibin Chen](https://zacharychenpk.github.io/), [Yansong Feng](https://yansongfeng.github.io/) for their contribution.
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| 2023-11-14T14:16:45+00:00 | {"language": ["multilingual", "bo", "ug", "kk", "mn"], "license": "cc0-1.0", "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "tags": ["multilingual"]} | 2023-11-24T01:44:34+00:00 | [
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| TAGS
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| MC^2: A Multilingual Corpus of Minority Languages in China
==========================================================
We present MC^2, a Multilingual Corpus of Minority Languages in China, which is the largest open-source corpus so far. This corpus encompasses four languages, namely Tibetan, Uyghur, Kazakh written in the Kazakh Arabic script, and Mongolian written in the traditional Mongolian script.
Our preprint is now on Arxiv.
The processing scripts are to be released on our Github Repo.
Languages and Sizes
-------------------
There are four minority languages in the dataset, and we report the dataset sizes below:
MC^2 (crawl): Tibetan, MC^2 (full): 1.7G
MC^2 (crawl): Uyghur, MC^2 (full): 520M
MC^2 (crawl): Kazakh (Arabic), MC^2 (full): 397M
MC^2 (crawl): Mongolian (Traditional), MC^2 (full): 874M
MC^2 (crawl) denotes the subset of our newly-collected web crawls. MC^2 (full) is the complete set of MC^2, which additionally contains texts collected from existing resources.
Dataset Structure
-----------------
The dataset is in JSON format, with each line containing one entry with three keys: 'title', 'text', and 'url'.
This is an example:
How to Obtain the Data
----------------------
Our data mainly contains three parts.
You can download our web-crawled data from Hugging Face.
For data from CulturaX and Wikipedia, you can download and then process them using scripts in our Github Repo.
License Information
-------------------
We released the data under the Creative Commons CC0 license.
Contributors
------------
We thank Chen Zhang\*, Mingxu Tao\*, Quzhe Huang\*, Jiuheng Lin\*, Zhibin Chen, Yansong Feng for their contribution.
| []
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|
72057516d6c1daf987b72d2074ed11ffda4e03a0 |
# Dataset Card for ScandiRedditFiltered
## Dataset Description
- **Repository:** <https://github.com/alexandrainst/ScandiReddit>
- **Point of Contact:** [Anders Jess Pedersen](mailto:[email protected])
### Dataset Summary
ScandiRedditFiltered is manually filtered and post-processed corpus consisting of comments from [ScandiReddit](https://huggingface.co/datasets/alexandrainst/scandi-reddit).
The intended use of the filtered sentences is for Text-To-Speech (TTS) models.
### Supported Tasks and Leaderboards
Training language models is the intended task for this dataset. No leaderboard is active at this point.
### Languages
The dataset is available in Danish (`da`).
## Dataset Structure
### Data Instances
An example from the dataset looks as follows.
```
{
'sentence': 'Bergen er ødelagt. Det er ikke moro mer.',
'username': 'alexandra_0',
'keep': 'y',
'index': 2
}
```
### Data Fields
The data fields are the same among all splits.
- `sentence`: a `string` feature.
- `username`: a `string` feature.
- `keep`: a `string` feature.
- `index`: a `int` feature.
## Dataset Creation
### Curation Rationale
The Scandinavian languages do not have any open source social media TTS datasets.
### Source Data
The raw Reddit data was collected through [PushShift](https://files.pushshift.io/reddit/comments/).
## Additional Information
### Dataset Curators
[Anders Jess Pedersen](mailto:[email protected]) from the [The Alexandra
Institute](https://alexandra.dk/) curated this dataset.
### Licensing Information
The dataset is licensed under the [CC BY 4.0
license](https://creativecommons.org/licenses/by/4.0/). | alexandrainst/scandi-reddit-filtered | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"size_categories:1K<n<10K",
"language:da",
"license:cc-by-4.0",
"region:us"
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| 2023-11-14T14:33:57+00:00 | {"language": ["da"], "license": ["cc-by-4.0"], "size_categories": ["1K<n<10K"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling"], "pretty_name": "ScandiRedditFiltered"} | 2023-11-14T14:44:30+00:00 | []
| [
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|
# Dataset Card for ScandiRedditFiltered
## Dataset Description
- Repository: <URL
- Point of Contact: Anders Jess Pedersen
### Dataset Summary
ScandiRedditFiltered is manually filtered and post-processed corpus consisting of comments from ScandiReddit.
The intended use of the filtered sentences is for Text-To-Speech (TTS) models.
### Supported Tasks and Leaderboards
Training language models is the intended task for this dataset. No leaderboard is active at this point.
### Languages
The dataset is available in Danish ('da').
## Dataset Structure
### Data Instances
An example from the dataset looks as follows.
### Data Fields
The data fields are the same among all splits.
- 'sentence': a 'string' feature.
- 'username': a 'string' feature.
- 'keep': a 'string' feature.
- 'index': a 'int' feature.
## Dataset Creation
### Curation Rationale
The Scandinavian languages do not have any open source social media TTS datasets.
### Source Data
The raw Reddit data was collected through PushShift.
## Additional Information
### Dataset Curators
Anders Jess Pedersen from the The Alexandra
Institute curated this dataset.
### Licensing Information
The dataset is licensed under the CC BY 4.0
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]
|
dab3411ad90cd16b979bdf8c73b89f2e896f0668 | # Dataset Card for "test_ds_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Back-up/test_ds_v2 | [
"region:us"
]
| 2023-11-14T14:34:53+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "struct": [{"name": "response", "dtype": "string"}]}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "instruction", "dtype": "string"}, {"name": "prompt_name", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "max_ratio", "dtype": "float64"}, {"name": "paragraph_similar", "dtype": "string"}, {"name": "start_index", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 21511872, "num_examples": 7597}], "download_size": 8276932, "dataset_size": 21511872}} | 2023-11-14T14:34:55+00:00 | []
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e74faceb76a42c5de6623ec60275a3cfd663a536 | # Dataset Card for "sdu_multilang"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tomashs/sdu_multilang | [
"region:us"
]
| 2023-11-14T14:47:12+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "dev", "path": "data/dev-*"}]}], "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "acronym", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "ID", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 8988245, "num_examples": 24599}, {"name": "dev", "num_bytes": 1099706, "num_examples": 3006}], "download_size": 4512009, "dataset_size": 10087951}} | 2023-11-14T14:47:19+00:00 | []
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6c359c9fffb6daa633fa07cb364b62ea35741e54 | # Dataset Card for "find_first_sent_train_10_eval_10_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_first_sent_train_10_eval_10_recite | [
"region:us"
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| 2023-11-14T14:55:53+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 47313, "num_examples": 30}, {"name": "validation", "num_bytes": 15770, "num_examples": 10}], "download_size": 0, "dataset_size": 63083}} | 2023-11-14T15:21:03+00:00 | []
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f2ff386b7e96e1cd742e1113df191edbb0a93a30 | # Dataset Card for "find_second_sent_train_10_eval_10_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_second_sent_train_10_eval_10_recite | [
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| 2023-11-14T14:56:03+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 46952, "num_examples": 30}, {"name": "validation", "num_bytes": 15637, "num_examples": 10}], "download_size": 0, "dataset_size": 62589}} | 2023-11-14T15:21:06+00:00 | []
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081c2fa2f3d668a5b940ddeec1eb561fca0ab978 | # Dataset Card for "find_last_sent_train_10_eval_10_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_last_sent_train_10_eval_10_recite | [
"region:us"
]
| 2023-11-14T14:56:12+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 46959, "num_examples": 30}, {"name": "validation", "num_bytes": 15620, "num_examples": 10}], "download_size": 0, "dataset_size": 62579}} | 2023-11-14T15:21:09+00:00 | []
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More Information needed | [
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504f83596bb0c5eecb2e697f9275e0835ad93223 | # Dataset Card for "find_first_sent_train_30_eval_10_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_first_sent_train_30_eval_10_recite | [
"region:us"
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| 2023-11-14T14:57:39+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 111799, "num_examples": 70}, {"name": "validation", "num_bytes": 18607, "num_examples": 10}], "download_size": 0, "dataset_size": 130406}} | 2023-11-14T15:21:54+00:00 | []
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4c6a5993fb435c9ff6b4cf10837f794b174ea18f | # Dataset Card for "find_second_sent_train_30_eval_10_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_second_sent_train_30_eval_10_recite | [
"region:us"
]
| 2023-11-14T14:57:46+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 110734, "num_examples": 70}, {"name": "validation", "num_bytes": 18909, "num_examples": 10}], "download_size": 0, "dataset_size": 129643}} | 2023-11-14T15:21:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "find_second_sent_train_30_eval_10_recite"
More Information needed | [
"# Dataset Card for \"find_second_sent_train_30_eval_10_recite\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"find_second_sent_train_30_eval_10_recite\"\n\nMore Information needed"
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28
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"passage: TAGS\n#region-us \n# Dataset Card for \"find_second_sent_train_30_eval_10_recite\"\n\nMore Information needed"
]
|
c31da77b151f2eeab5ab8239f466688dc98ac789 | # Dataset Card for "test_ds_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Back-up/test_ds_v3 | [
"region:us"
]
| 2023-11-14T14:57:46+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "struct": [{"name": "response", "dtype": "string"}]}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "instruction", "dtype": "string"}, {"name": "prompt_name", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "max_ratio", "dtype": "float64"}, {"name": "paragraph_similar", "dtype": "string"}, {"name": "start_index", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 21511788, "num_examples": 7597}], "download_size": 8245485, "dataset_size": 21511788}} | 2023-11-14T14:57:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test_ds_v3"
More Information needed | [
"# Dataset Card for \"test_ds_v3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test_ds_v3\"\n\nMore Information needed"
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16
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"passage: TAGS\n#region-us \n# Dataset Card for \"test_ds_v3\"\n\nMore Information needed"
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|
0d15faf7a71730e84827c3350c4c9bfef67898d6 | # Dataset Card for "find_last_sent_train_30_eval_10_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_last_sent_train_30_eval_10_recite | [
"region:us"
]
| 2023-11-14T14:57:53+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 110758, "num_examples": 70}, {"name": "validation", "num_bytes": 18755, "num_examples": 10}], "download_size": 0, "dataset_size": 129513}} | 2023-11-14T15:22:00+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "find_last_sent_train_30_eval_10_recite"
More Information needed | [
"# Dataset Card for \"find_last_sent_train_30_eval_10_recite\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"find_last_sent_train_30_eval_10_recite\"\n\nMore Information needed"
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28
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"passage: TAGS\n#region-us \n# Dataset Card for \"find_last_sent_train_30_eval_10_recite\"\n\nMore Information needed"
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|
a54917df4d21fb8d41f4698259f76c6f2123e168 | # Dataset Card for "find_first_sent_train_50_eval_10_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_first_sent_train_50_eval_10_recite | [
"region:us"
]
| 2023-11-14T14:59:27+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 170837, "num_examples": 110}, {"name": "validation", "num_bytes": 15661, "num_examples": 10}], "download_size": 0, "dataset_size": 186498}} | 2023-11-14T15:05:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "find_first_sent_train_50_eval_10_recite"
More Information needed | [
"# Dataset Card for \"find_first_sent_train_50_eval_10_recite\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"find_first_sent_train_50_eval_10_recite\"\n\nMore Information needed"
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29
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"passage: TAGS\n#region-us \n# Dataset Card for \"find_first_sent_train_50_eval_10_recite\"\n\nMore Information needed"
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|
c62f7ad6189f0cf55fed5979e00e5314cb6f0abe | # Dataset Card for "find_second_sent_train_50_eval_10_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_second_sent_train_50_eval_10_recite | [
"region:us"
]
| 2023-11-14T14:59:34+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 169106, "num_examples": 110}, {"name": "validation", "num_bytes": 15705, "num_examples": 10}], "download_size": 0, "dataset_size": 184811}} | 2023-11-14T15:05:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "find_second_sent_train_50_eval_10_recite"
More Information needed | [
"# Dataset Card for \"find_second_sent_train_50_eval_10_recite\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"find_second_sent_train_50_eval_10_recite\"\n\nMore Information needed"
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28
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"passage: TAGS\n#region-us \n# Dataset Card for \"find_second_sent_train_50_eval_10_recite\"\n\nMore Information needed"
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|
957775950f5c3570a1387005636e4c966817861e | # Dataset Card for "find_last_sent_train_50_eval_10_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_last_sent_train_50_eval_10_recite | [
"region:us"
]
| 2023-11-14T14:59:41+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 169382, "num_examples": 110}, {"name": "validation", "num_bytes": 15595, "num_examples": 10}], "download_size": 0, "dataset_size": 184977}} | 2023-11-14T15:05:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "find_last_sent_train_50_eval_10_recite"
More Information needed | [
"# Dataset Card for \"find_last_sent_train_50_eval_10_recite\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"find_last_sent_train_50_eval_10_recite\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"find_last_sent_train_50_eval_10_recite\"\n\nMore Information needed"
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|
786f287fc590a7abed07e145399f4bf01e0fe7ae | # Dataset Card for "find_first_sent_train_100_eval_10_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_first_sent_train_100_eval_10_recite | [
"region:us"
]
| 2023-11-14T15:06:08+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 340962, "num_examples": 210}, {"name": "validation", "num_bytes": 18119, "num_examples": 10}], "download_size": 0, "dataset_size": 359081}} | 2023-11-14T15:07:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "find_first_sent_train_100_eval_10_recite"
More Information needed | [
"# Dataset Card for \"find_first_sent_train_100_eval_10_recite\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"find_first_sent_train_100_eval_10_recite\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"find_first_sent_train_100_eval_10_recite\"\n\nMore Information needed"
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|
12dc5e9e61392d19d2045cb480f37303821f0804 | # Dataset Card for "find_second_sent_train_100_eval_10_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_second_sent_train_100_eval_10_recite | [
"region:us"
]
| 2023-11-14T15:06:19+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 339545, "num_examples": 210}, {"name": "validation", "num_bytes": 17697, "num_examples": 10}], "download_size": 0, "dataset_size": 357242}} | 2023-11-14T15:07:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "find_second_sent_train_100_eval_10_recite"
More Information needed | [
"# Dataset Card for \"find_second_sent_train_100_eval_10_recite\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"find_second_sent_train_100_eval_10_recite\"\n\nMore Information needed"
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28
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"passage: TAGS\n#region-us \n# Dataset Card for \"find_second_sent_train_100_eval_10_recite\"\n\nMore Information needed"
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|
5c66852f309b8837505c80890ffd97336a35e7e5 | # Dataset Card for "find_last_sent_train_100_eval_10_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_last_sent_train_100_eval_10_recite | [
"region:us"
]
| 2023-11-14T15:06:27+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 339936, "num_examples": 210}, {"name": "validation", "num_bytes": 17991, "num_examples": 10}], "download_size": 0, "dataset_size": 357927}} | 2023-11-14T15:07:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "find_last_sent_train_100_eval_10_recite"
More Information needed | [
"# Dataset Card for \"find_last_sent_train_100_eval_10_recite\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"find_last_sent_train_100_eval_10_recite\"\n\nMore Information needed"
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|
1ca2b23004738e6e6c810da268180497b755ef10 | # Dataset Card for "ip2p-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | SidXXD/ip2p-mini | [
"region:us"
]
| 2023-11-14T15:06:55+00:00 | {"dataset_info": {"features": [{"name": "original_image", "dtype": "image"}, {"name": "edit_prompt", "dtype": "string"}, {"name": "edited_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 671634393.546, "num_examples": 1158}], "download_size": 670291124, "dataset_size": 671634393.546}} | 2023-11-14T15:13:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ip2p-mini"
More Information needed | [
"# Dataset Card for \"ip2p-mini\"\n\nMore Information needed"
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| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ip2p-mini\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"ip2p-mini\"\n\nMore Information needed"
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|
24af48a52b9f52019a6b866a1859dc95e7b2ce87 | # Dataset Card for "adhoc_quad"
## Dataset Summary
The German Ad-Hoc Question Answering Dataset (AdHocQuAD) is a reading comprehension dataset for German financial texts. It is a machine generated dataset, where ChatGPT (Version 3.5) is used to ask questions on a set of German Ad-Hoc announcements. The answer to every question is a segment of text, or span, from the corresponding reading passage.
## Supported Tasks and Leaderboards
extractive-qa, closed-domain-qa, open-domain-qa, text-retrieval: This dataset is intended to be used for open-domain-qa, but can also be used for information retrieval tasks.
## Languages
The texts in the dataset are in German (de).
# Dataset Structure
## Data Instances
A sample from the training set is provided below:
{
"context": "This is a test context with eight words.",
"id": "1",
"question": "How many words contains the context?",
"answers": {
"answer_start": [28],
"text": ["eight"]
}
}
## Data Fields
id: a string feature.
context: a string feature.
question: a string feature.
answers: a dictionary feature containing:
text: a string feature.
answer_start: a int32 feature.
# Additional Information
## Details on the Generation of the Ad-Hoc QuAD Database
To construct the ad-hoc QuAD database, I use 9,132 German ad-hoc announcements as context strings. Announcements exceeding 15 sentences are truncated to ensure compatibility with BERT's input limitations in subsequent applications.
After that, there is a need to identify questions and appropriate answers that reference the given ad-hoc announcements. Given that manual generation of questions and answers is both resource-intensive and time-consuming, I employ the OpenAI's ChatGPT model (gpt-3.5-turbo).
In a first step, I ask ChatGPT to generate three suitable questions for a given announcement. The prompt looks as follows:
Create three questions for the following text.
It should be possible to answer the question with a substring of the input text.
The questions should ask for different aspects of the input.
The questions should be in German.
Text: <<context>>
Question:
In the pursuit of creating an extractive QuAD task, it is imperative to instruct the model such that every question can be answered using a substring from the provided announcement. This strategy aims to prevent the model from generating open-ended questions or those requiring external knowledge not present in the announcement. Additionally, the model is directed to address various aspects of the announcement to minimize question redundancy. Notably, despite the context strings being in German, ChatGPT occasionally formulates questions in English. To counteract this, explicit instructions are given to ensure questions are posed in German. Employing this methodology yields 9,132 unique context-question pairs.
In a second step, I use ChatGPT again to extract the substring that answers to question to a specific context string. The respective prompt is given by:
You have given a text and a question to that text. Find the answer as a substring of the input text.
It is crucial that the answer is contained exactly as a substring in the input text, even if this implies that the answer is not a full sentence.
Example:
Text: 'Herr Müller ist 37 Jahre alt.'
Question: 'Wie alt ist Herr Müller?'
Answer: '37 Jahre'
Text: <<context>>
Question: <<question>>
Answer:
Evaluations of the method of extracting substrings from a specified context to answer a posed question via ChatGPT indicated a recurrent issue: ChatGPT frequently transformed the substring into a complete sentence, thereby compromising the extractive nature of the resultant database. Emphasizing the necessity for extractive answers, coupled with a demonstrative example, markedly enhanced the outcomes. However, of the responses generated by ChatGPT, 1,725 are not given as substrings of the context, leading to a final ad-hoc QuAD database size of 7,407.
The code for creating the dataset can be found [here](https://github.com/FinTexIFB/AdHocQuAD).
## Dataset Curators
The dataset was created by Moritz Scherrmann using ChatGPT 3.5 turbo
## Citation Information
@misc{scherrmann2023german,
title={German FinBERT: A German Pre-trained Language Model},
author={Moritz Scherrmann},
year={2023},
eprint={2311.08793},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
| scherrmann/adhoc_quad | [
"arxiv:2311.08793",
"region:us"
]
| 2023-11-14T15:13:31+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 10365360, "num_examples": 6659}, {"name": "validation", "num_bytes": 1157605, "num_examples": 748}], "download_size": 3088466, "dataset_size": 11522965}} | 2023-11-16T09:24:41+00:00 | [
"2311.08793"
]
| []
| TAGS
#arxiv-2311.08793 #region-us
| # Dataset Card for "adhoc_quad"
## Dataset Summary
The German Ad-Hoc Question Answering Dataset (AdHocQuAD) is a reading comprehension dataset for German financial texts. It is a machine generated dataset, where ChatGPT (Version 3.5) is used to ask questions on a set of German Ad-Hoc announcements. The answer to every question is a segment of text, or span, from the corresponding reading passage.
## Supported Tasks and Leaderboards
extractive-qa, closed-domain-qa, open-domain-qa, text-retrieval: This dataset is intended to be used for open-domain-qa, but can also be used for information retrieval tasks.
## Languages
The texts in the dataset are in German (de).
# Dataset Structure
## Data Instances
A sample from the training set is provided below:
{
"context": "This is a test context with eight words.",
"id": "1",
"question": "How many words contains the context?",
"answers": {
"answer_start": [28],
"text": ["eight"]
}
}
## Data Fields
id: a string feature.
context: a string feature.
question: a string feature.
answers: a dictionary feature containing:
text: a string feature.
answer_start: a int32 feature.
# Additional Information
## Details on the Generation of the Ad-Hoc QuAD Database
To construct the ad-hoc QuAD database, I use 9,132 German ad-hoc announcements as context strings. Announcements exceeding 15 sentences are truncated to ensure compatibility with BERT's input limitations in subsequent applications.
After that, there is a need to identify questions and appropriate answers that reference the given ad-hoc announcements. Given that manual generation of questions and answers is both resource-intensive and time-consuming, I employ the OpenAI's ChatGPT model (gpt-3.5-turbo).
In a first step, I ask ChatGPT to generate three suitable questions for a given announcement. The prompt looks as follows:
Create three questions for the following text.
It should be possible to answer the question with a substring of the input text.
The questions should ask for different aspects of the input.
The questions should be in German.
Text: <<context>>
Question:
In the pursuit of creating an extractive QuAD task, it is imperative to instruct the model such that every question can be answered using a substring from the provided announcement. This strategy aims to prevent the model from generating open-ended questions or those requiring external knowledge not present in the announcement. Additionally, the model is directed to address various aspects of the announcement to minimize question redundancy. Notably, despite the context strings being in German, ChatGPT occasionally formulates questions in English. To counteract this, explicit instructions are given to ensure questions are posed in German. Employing this methodology yields 9,132 unique context-question pairs.
In a second step, I use ChatGPT again to extract the substring that answers to question to a specific context string. The respective prompt is given by:
You have given a text and a question to that text. Find the answer as a substring of the input text.
It is crucial that the answer is contained exactly as a substring in the input text, even if this implies that the answer is not a full sentence.
Example:
Text: 'Herr Müller ist 37 Jahre alt.'
Question: 'Wie alt ist Herr Müller?'
Answer: '37 Jahre'
Text: <<context>>
Question: <<question>>
Answer:
Evaluations of the method of extracting substrings from a specified context to answer a posed question via ChatGPT indicated a recurrent issue: ChatGPT frequently transformed the substring into a complete sentence, thereby compromising the extractive nature of the resultant database. Emphasizing the necessity for extractive answers, coupled with a demonstrative example, markedly enhanced the outcomes. However, of the responses generated by ChatGPT, 1,725 are not given as substrings of the context, leading to a final ad-hoc QuAD database size of 7,407.
The code for creating the dataset can be found here.
## Dataset Curators
The dataset was created by Moritz Scherrmann using ChatGPT 3.5 turbo
@misc{scherrmann2023german,
title={German FinBERT: A German Pre-trained Language Model},
author={Moritz Scherrmann},
year={2023},
eprint={2311.08793},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
| [
"# Dataset Card for \"adhoc_quad\"",
"## Dataset Summary\n\nThe German Ad-Hoc Question Answering Dataset (AdHocQuAD) is a reading comprehension dataset for German financial texts. It is a machine generated dataset, where ChatGPT (Version 3.5) is used to ask questions on a set of German Ad-Hoc announcements. The answer to every question is a segment of text, or span, from the corresponding reading passage.",
"## Supported Tasks and Leaderboards\n\nextractive-qa, closed-domain-qa, open-domain-qa, text-retrieval: This dataset is intended to be used for open-domain-qa, but can also be used for information retrieval tasks.",
"## Languages\n\nThe texts in the dataset are in German (de).",
"# Dataset Structure",
"## Data Instances\n\nA sample from the training set is provided below:\n\n {\n \"context\": \"This is a test context with eight words.\",\n \"id\": \"1\",\n \"question\": \"How many words contains the context?\",\n \"answers\": {\n \"answer_start\": [28],\n \"text\": [\"eight\"]\n }\n }",
"## Data Fields\n\n id: a string feature.\n context: a string feature.\n question: a string feature.\n answers: a dictionary feature containing:\n text: a string feature.\n answer_start: a int32 feature.",
"# Additional Information",
"## Details on the Generation of the Ad-Hoc QuAD Database\nTo construct the ad-hoc QuAD database, I use 9,132 German ad-hoc announcements as context strings. Announcements exceeding 15 sentences are truncated to ensure compatibility with BERT's input limitations in subsequent applications.\n\nAfter that, there is a need to identify questions and appropriate answers that reference the given ad-hoc announcements. Given that manual generation of questions and answers is both resource-intensive and time-consuming, I employ the OpenAI's ChatGPT model (gpt-3.5-turbo).\n\nIn a first step, I ask ChatGPT to generate three suitable questions for a given announcement. The prompt looks as follows:\n\n Create three questions for the following text. \n It should be possible to answer the question with a substring of the input text. \n The questions should ask for different aspects of the input. \n The questions should be in German.\n \n Text: <<context>>\n Question:\n\nIn the pursuit of creating an extractive QuAD task, it is imperative to instruct the model such that every question can be answered using a substring from the provided announcement. This strategy aims to prevent the model from generating open-ended questions or those requiring external knowledge not present in the announcement. Additionally, the model is directed to address various aspects of the announcement to minimize question redundancy. Notably, despite the context strings being in German, ChatGPT occasionally formulates questions in English. To counteract this, explicit instructions are given to ensure questions are posed in German. Employing this methodology yields 9,132 unique context-question pairs.\n\nIn a second step, I use ChatGPT again to extract the substring that answers to question to a specific context string. The respective prompt is given by:\n\n You have given a text and a question to that text. Find the answer as a substring of the input text. \n It is crucial that the answer is contained exactly as a substring in the input text, even if this implies that the answer is not a full sentence. \n \n Example:\n Text: 'Herr Müller ist 37 Jahre alt.'\n Question: 'Wie alt ist Herr Müller?'\n Answer: '37 Jahre'\n \n Text: <<context>>\n Question: <<question>>\n Answer:\n\nEvaluations of the method of extracting substrings from a specified context to answer a posed question via ChatGPT indicated a recurrent issue: ChatGPT frequently transformed the substring into a complete sentence, thereby compromising the extractive nature of the resultant database. Emphasizing the necessity for extractive answers, coupled with a demonstrative example, markedly enhanced the outcomes. However, of the responses generated by ChatGPT, 1,725 are not given as substrings of the context, leading to a final ad-hoc QuAD database size of 7,407.\n\nThe code for creating the dataset can be found here.",
"## Dataset Curators\n\nThe dataset was created by Moritz Scherrmann using ChatGPT 3.5 turbo\n\n\n @misc{scherrmann2023german,\n title={German FinBERT: A German Pre-trained Language Model}, \n author={Moritz Scherrmann},\n year={2023},\n eprint={2311.08793},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n }"
]
| [
"TAGS\n#arxiv-2311.08793 #region-us \n",
"# Dataset Card for \"adhoc_quad\"",
"## Dataset Summary\n\nThe German Ad-Hoc Question Answering Dataset (AdHocQuAD) is a reading comprehension dataset for German financial texts. It is a machine generated dataset, where ChatGPT (Version 3.5) is used to ask questions on a set of German Ad-Hoc announcements. The answer to every question is a segment of text, or span, from the corresponding reading passage.",
"## Supported Tasks and Leaderboards\n\nextractive-qa, closed-domain-qa, open-domain-qa, text-retrieval: This dataset is intended to be used for open-domain-qa, but can also be used for information retrieval tasks.",
"## Languages\n\nThe texts in the dataset are in German (de).",
"# Dataset Structure",
"## Data Instances\n\nA sample from the training set is provided below:\n\n {\n \"context\": \"This is a test context with eight words.\",\n \"id\": \"1\",\n \"question\": \"How many words contains the context?\",\n \"answers\": {\n \"answer_start\": [28],\n \"text\": [\"eight\"]\n }\n }",
"## Data Fields\n\n id: a string feature.\n context: a string feature.\n question: a string feature.\n answers: a dictionary feature containing:\n text: a string feature.\n answer_start: a int32 feature.",
"# Additional Information",
"## Details on the Generation of the Ad-Hoc QuAD Database\nTo construct the ad-hoc QuAD database, I use 9,132 German ad-hoc announcements as context strings. Announcements exceeding 15 sentences are truncated to ensure compatibility with BERT's input limitations in subsequent applications.\n\nAfter that, there is a need to identify questions and appropriate answers that reference the given ad-hoc announcements. Given that manual generation of questions and answers is both resource-intensive and time-consuming, I employ the OpenAI's ChatGPT model (gpt-3.5-turbo).\n\nIn a first step, I ask ChatGPT to generate three suitable questions for a given announcement. The prompt looks as follows:\n\n Create three questions for the following text. \n It should be possible to answer the question with a substring of the input text. \n The questions should ask for different aspects of the input. \n The questions should be in German.\n \n Text: <<context>>\n Question:\n\nIn the pursuit of creating an extractive QuAD task, it is imperative to instruct the model such that every question can be answered using a substring from the provided announcement. This strategy aims to prevent the model from generating open-ended questions or those requiring external knowledge not present in the announcement. Additionally, the model is directed to address various aspects of the announcement to minimize question redundancy. Notably, despite the context strings being in German, ChatGPT occasionally formulates questions in English. To counteract this, explicit instructions are given to ensure questions are posed in German. Employing this methodology yields 9,132 unique context-question pairs.\n\nIn a second step, I use ChatGPT again to extract the substring that answers to question to a specific context string. The respective prompt is given by:\n\n You have given a text and a question to that text. Find the answer as a substring of the input text. \n It is crucial that the answer is contained exactly as a substring in the input text, even if this implies that the answer is not a full sentence. \n \n Example:\n Text: 'Herr Müller ist 37 Jahre alt.'\n Question: 'Wie alt ist Herr Müller?'\n Answer: '37 Jahre'\n \n Text: <<context>>\n Question: <<question>>\n Answer:\n\nEvaluations of the method of extracting substrings from a specified context to answer a posed question via ChatGPT indicated a recurrent issue: ChatGPT frequently transformed the substring into a complete sentence, thereby compromising the extractive nature of the resultant database. Emphasizing the necessity for extractive answers, coupled with a demonstrative example, markedly enhanced the outcomes. However, of the responses generated by ChatGPT, 1,725 are not given as substrings of the context, leading to a final ad-hoc QuAD database size of 7,407.\n\nThe code for creating the dataset can be found here.",
"## Dataset Curators\n\nThe dataset was created by Moritz Scherrmann using ChatGPT 3.5 turbo\n\n\n @misc{scherrmann2023german,\n title={German FinBERT: A German Pre-trained Language Model}, \n author={Moritz Scherrmann},\n year={2023},\n eprint={2311.08793},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n }"
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15,
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82,
49,
5,
656,
100
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| [
"passage: TAGS\n#arxiv-2311.08793 #region-us \n# Dataset Card for \"adhoc_quad\"## Dataset Summary\n\nThe German Ad-Hoc Question Answering Dataset (AdHocQuAD) is a reading comprehension dataset for German financial texts. It is a machine generated dataset, where ChatGPT (Version 3.5) is used to ask questions on a set of German Ad-Hoc announcements. The answer to every question is a segment of text, or span, from the corresponding reading passage.## Supported Tasks and Leaderboards\n\nextractive-qa, closed-domain-qa, open-domain-qa, text-retrieval: This dataset is intended to be used for open-domain-qa, but can also be used for information retrieval tasks.## Languages\n\nThe texts in the dataset are in German (de).# Dataset Structure## Data Instances\n\nA sample from the training set is provided below:\n\n {\n \"context\": \"This is a test context with eight words.\",\n \"id\": \"1\",\n \"question\": \"How many words contains the context?\",\n \"answers\": {\n \"answer_start\": [28],\n \"text\": [\"eight\"]\n }\n }## Data Fields\n\n id: a string feature.\n context: a string feature.\n question: a string feature.\n answers: a dictionary feature containing:\n text: a string feature.\n answer_start: a int32 feature.# Additional Information"
]
|
a21878d88eb04ba435cadb5a8ada63079ede27b4 |
# Stats for number of tokens:
```
+--------------+
|sum(num_words)|
+--------------+
| 34469983|
+--------------+
+---------------+
|sum(num_tokens)|
+---------------+
| 59332879|
+---------------+
+-----------------+
| avg(num_words)|
+-----------------+
|3895.353486269635|
+-----------------+
+-----------------+
| avg(num_tokens)|
+-----------------+
|6705.037744377896|
+-----------------+
``` | atom-in-the-universe/cc-pdf-extracted-100k | [
"license:apache-2.0",
"region:us"
]
| 2023-11-14T15:29:17+00:00 | {"license": "apache-2.0"} | 2023-11-14T15:41:47+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
|
# Stats for number of tokens:
| [
"# Stats for number of tokens:"
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14,
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]
|
17d845914b6f558724ce967e168b22671705d5fe |
# Dataset Card for Witches Warehouse
If you got linked to here, you probably know what you want. | KaraKaraWitch/WitchesWarehouse | [
"license:apache-2.0",
"region:us"
]
| 2023-11-14T15:29:39+00:00 | {"license": "apache-2.0", "pretty_name": "Witches Warehouse"} | 2023-11-14T15:55:41+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
|
# Dataset Card for Witches Warehouse
If you got linked to here, you probably know what you want. | [
"# Dataset Card for Witches Warehouse\n\nIf you got linked to here, you probably know what you want."
]
| [
"TAGS\n#license-apache-2.0 #region-us \n",
"# Dataset Card for Witches Warehouse\n\nIf you got linked to here, you probably know what you want."
]
| [
14,
24
]
| [
"passage: TAGS\n#license-apache-2.0 #region-us \n# Dataset Card for Witches Warehouse\n\nIf you got linked to here, you probably know what you want."
]
|
a6c99a3e7433423f3c9046a9611056bf6a3c3ac1 | # Dataset Card for "prm800k-llama-generator-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | parksimon0808/prm800k-llama-generator-v2 | [
"region:us"
]
| 2023-11-14T15:38:44+00:00 | {"dataset_info": {"features": [{"name": "texts", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "labels", "sequence": "int64"}, {"name": "answers", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 107067298, "num_examples": 16465}, {"name": "test", "num_bytes": 4626217, "num_examples": 773}], "download_size": 23595045, "dataset_size": 111693515}} | 2023-11-14T15:50:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "prm800k-llama-generator-v2"
More Information needed | [
"# Dataset Card for \"prm800k-llama-generator-v2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"prm800k-llama-generator-v2\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"prm800k-llama-generator-v2\"\n\nMore Information needed"
]
|
86e0ce9ce54e6b06574d6851d41220c186540840 | This dataset is a text file with music in ABC format. A compositions is separated from another by two '###' | Daniil-plotnikov/musicdataset | [
"music",
"region:us"
]
| 2023-11-14T15:39:43+00:00 | {"tags": ["music"]} | 2023-11-15T14:23:22+00:00 | []
| []
| TAGS
#music #region-us
| This dataset is a text file with music in ABC format. A compositions is separated from another by two '###' | []
| [
"TAGS\n#music #region-us \n"
]
| [
8
]
| [
"passage: TAGS\n#music #region-us \n"
]
|
0c68cd5ec7ffb364ed6e532dfd615f61ebc3f0ad | # Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jzzcutler/github-issues | [
"region:us"
]
| 2023-11-14T15:42:32+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "struct": [{"name": "avatar_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "login", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "labels", "list": [{"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "struct": [{"name": "avatar_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "login", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "assignees", "list": [{"name": "avatar_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "login", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "milestone", "struct": [{"name": "closed_at", "dtype": "string"}, {"name": "closed_issues", "dtype": "int64"}, {"name": "created_at", "dtype": "string"}, {"name": "creator", "struct": [{"name": "avatar_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "login", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "description", "dtype": "string"}, {"name": "due_on", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "labels_url", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "open_issues", "dtype": "int64"}, {"name": "state", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "updated_at", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "comments", "sequence": "string"}, {"name": "created_at", "dtype": "timestamp[ns, tz=UTC]"}, {"name": "updated_at", "dtype": "timestamp[ns, tz=UTC]"}, {"name": "closed_at", "dtype": "timestamp[ns, tz=UTC]"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "float64"}, {"name": "body", "dtype": "string"}, {"name": "reactions", "struct": [{"name": "+1", "dtype": "int64"}, {"name": "-1", "dtype": "int64"}, {"name": "confused", "dtype": "int64"}, {"name": "eyes", "dtype": "int64"}, {"name": "heart", "dtype": "int64"}, {"name": "hooray", "dtype": "int64"}, {"name": "laugh", "dtype": "int64"}, {"name": "rocket", "dtype": "int64"}, {"name": "total_count", "dtype": "int64"}, {"name": "url", "dtype": "string"}]}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "float64"}, {"name": "state_reason", "dtype": "string"}, {"name": "draft", "dtype": "float64"}, {"name": "pull_request", "struct": [{"name": "diff_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "merged_at", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 25458344, "num_examples": 4000}], "download_size": 7291887, "dataset_size": 25458344}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-14T15:42:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "github-issues"
More Information needed | [
"# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
|
713c26234a4d4aa33fd1fd079b29c3de0ca1760a | # Dataset Card for "HoC_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hippocrates/HoC_train | [
"region:us"
]
| 2023-11-14T16:02:06+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5195156, "num_examples": 1108}, {"name": "valid", "num_bytes": 730025, "num_examples": 157}, {"name": "test", "num_bytes": 1463778, "num_examples": 315}], "download_size": 2960505, "dataset_size": 7388959}} | 2023-11-14T16:02:09+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "HoC_train"
More Information needed | [
"# Dataset Card for \"HoC_train\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"HoC_train\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"HoC_train\"\n\nMore Information needed"
]
|
8c129aabbafe702a0b2aa2b77cd6aa6fbcdf92f1 | # Dataset Card for "clinical_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hippocrates/clinical_train | [
"region:us"
]
| 2023-11-14T16:03:22+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 52143984, "num_examples": 10000}, {"name": "valid", "num_bytes": 46961044, "num_examples": 8164}], "download_size": 42209790, "dataset_size": 99105028}} | 2023-11-14T18:29:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "clinical_train"
More Information needed | [
"# Dataset Card for \"clinical_train\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"clinical_train\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"clinical_train\"\n\nMore Information needed"
]
|
42d13210a545c32fe71920af1abefcacf530813d | # Dataset Card for "HoC_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hippocrates/HoC_test | [
"region:us"
]
| 2023-11-14T16:06:15+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "gold", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 3062367, "num_examples": 1108}, {"name": "valid", "num_bytes": 430760, "num_examples": 157}, {"name": "test", "num_bytes": 863678, "num_examples": 315}], "download_size": 1496963, "dataset_size": 4356805}} | 2023-11-14T16:06:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "HoC_test"
More Information needed | [
"# Dataset Card for \"HoC_test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"HoC_test\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"HoC_test\"\n\nMore Information needed"
]
|
9761689a41ee52974691238719df052534c5ec0a |
## Model Description
As part of the ITANONG project's 10 billion-token Tagalog dataset, we have introduced a collection of pre-trained embedding models. These models were trained using the Formal text dataset from the renowned corpus which has been thoroughly detailed in our paper. Details of the embedding models can be seen below:
| **Embedding Technique** | **Variant** | **Model File Format** | **Embedding Size** |
|:-----------------------:|:-----------:|:---------------------:|:------------------:|
| Word2Vec | Skipgram | .bin | 20 |
| Word2Vec | Skipgram | .bin | 30 |
| Word2Vec | Skipgram | .bin | 50 |
| Word2Vec | Skipgram | .bin | 100 |
| Word2Vec | Skipgram | .bin | 200 |
| Word2Vec | Skipgram | .bin | 300 |
| Word2Vec | Skipgram | .txt | 20 |
| Word2Vec | Skipgram | .txt | 30 |
| Word2Vec | Skipgram | .txt | 50 |
| Word2Vec | Skipgram | .txt | 100 |
| Word2Vec | Skipgram | .txt | 200 |
| Word2Vec | Skipgram | .txt | 300 |
| Word2Vec | CBOW | .bin | 20 |
| Word2Vec | CBOW | .bin | 30 |
| Word2Vec | CBOW | .bin | 50 |
| Word2Vec | CBOW | .bin | 100 |
| Word2Vec | CBOW | .bin | 200 |
| Word2Vec | CBOW | .bin | 300 |
| Word2Vec | CBOW | .txt | 20 |
| Word2Vec | CBOW | .txt | 30 |
| Word2Vec | CBOW | .txt | 50 |
| Word2Vec | CBOW | .txt | 100 |
| Word2Vec | CBOW | .txt | 200 |
| Word2Vec | CBOW | .txt | 300 |
| FastText | Skipgram | .bin | 20 |
| FastText | Skipgram | .bin | 30 |
| FastText | Skipgram | .bin | 50 |
| FastText | Skipgram | .bin | 100 |
| FastText | Skipgram | .bin | 200 |
| FastText | Skipgram | .bin | 300 |
| FastText | Skipgram | .txt | 20 |
| FastText | Skipgram | .txt | 30 |
| FastText | Skipgram | .txt | 50 |
| FastText | Skipgram | .txt | 100 |
| FastText | Skipgram | .txt | 200 |
| FastText | Skipgram | .txt | 300 |
| FastText | CBOW | .bin | 20 |
| FastText | CBOW | .bin | 30 |
| FastText | CBOW | .bin | 50 |
| FastText | CBOW | .bin | 100 |
| FastText | CBOW | .bin | 200 |
| FastText | CBOW | .bin | 300 |
| FastText | CBOW | .txt | 20 |
| FastText | CBOW | .txt | 30 |
| FastText | CBOW | .txt | 50 |
| FastText | CBOW | .txt | 100 |
| FastText | CBOW | .txt | 200 |
| FastText | CBOW | .txt | 300 |
## Training Details
This model was trained using an Nvidia V100-32GB GPU on DOST-ASTI Computing and Archiving Research Environment (COARE) - https://asti.dost.gov.ph/projects/coare/
### Training Data
The training dataset was compiled from both formal and informal sources, consisting of 194,001 instances from formal channels. More information on pre-processing and training parameters on our paper.
## Citation
Paper : iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Language
Bibtex:
```
@inproceedings{visperas-etal-2023-itanong,
title = "i{TANONG}-{DS} : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select {P}hilippine Languages",
author = "Visperas, Moses L. and
Borjal, Christalline Joie and
Adoptante, Aunhel John M and
Abacial, Danielle Shine R. and
Decano, Ma. Miciella and
Peramo, Elmer C",
editor = "Abbas, Mourad and
Freihat, Abed Alhakim",
booktitle = "Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023)",
month = dec,
year = "2023",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.icnlsp-1.34",
pages = "316--323",
}
``` | dost-asti/Embeddings | [
"task_categories:feature-extraction",
"region:us"
]
| 2023-11-14T16:16:29+00:00 | {"task_categories": ["feature-extraction"]} | 2024-02-16T08:02:30+00:00 | []
| []
| TAGS
#task_categories-feature-extraction #region-us
| Model Description
-----------------
As part of the ITANONG project's 10 billion-token Tagalog dataset, we have introduced a collection of pre-trained embedding models. These models were trained using the Formal text dataset from the renowned corpus which has been thoroughly detailed in our paper. Details of the embedding models can be seen below:
Training Details
----------------
This model was trained using an Nvidia V100-32GB GPU on DOST-ASTI Computing and Archiving Research Environment (COARE) - URL
### Training Data
The training dataset was compiled from both formal and informal sources, consisting of 194,001 instances from formal channels. More information on pre-processing and training parameters on our paper.
Paper : iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Language
Bibtex:
| [
"### Training Data\n\n\nThe training dataset was compiled from both formal and informal sources, consisting of 194,001 instances from formal channels. More information on pre-processing and training parameters on our paper.\n\n\nPaper : iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Language\n\n\nBibtex:"
]
| [
"TAGS\n#task_categories-feature-extraction #region-us \n",
"### Training Data\n\n\nThe training dataset was compiled from both formal and informal sources, consisting of 194,001 instances from formal channels. More information on pre-processing and training parameters on our paper.\n\n\nPaper : iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Language\n\n\nBibtex:"
]
| [
18,
81
]
| [
"passage: TAGS\n#task_categories-feature-extraction #region-us \n### Training Data\n\n\nThe training dataset was compiled from both formal and informal sources, consisting of 194,001 instances from formal channels. More information on pre-processing and training parameters on our paper.\n\n\nPaper : iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Language\n\n\nBibtex:"
]
|
94c2e60e4960c6a2399d9994bf5887b1534d6779 | # Dataset Card for "chemistry-bookshelves-merged"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | chemNLP/chemistry-bookshelves-merged | [
"region:us"
]
| 2023-11-14T16:21:47+00:00 | {"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 56206230, "num_examples": 7728}], "download_size": 25267751, "dataset_size": 56206230}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-14T16:24:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chemistry-bookshelves-merged"
More Information needed | [
"# Dataset Card for \"chemistry-bookshelves-merged\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chemistry-bookshelves-merged\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chemistry-bookshelves-merged\"\n\nMore Information needed"
]
|
57106f3029c9cb7c2c424d73185d158543357951 | # Ultrafeedback binarized dataset using the mean of preference ratings
## Introduction
This dataset contains the result of curation work performed by Argilla (using Argilla 😃).
After visually browsing around 200 examples using the sort and filter feature of Argilla, we noticed a strong mismatch between the `overall_score` in the original UF dataset (and the Zephyr train_prefs dataset) and the quality of the chosen response.
By adding the critique rationale to our Argilla Dataset, we confirmed the critique rationale was highly negative, whereas the rating was very high (the highest in fact: `10`). See screenshot below for one example of this issue. After some quick investigation, we identified hundreds of examples having the same issue and a potential bug on the UltraFeedback repo.
For context, [this is the corresponding example](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized/viewer/default/train_prefs?row=52108) within the `train_prefs` dataset with a `score_chosen` of `10`.
The dataset is fully open and browsable at https://huggingface.co/spaces/argilla/ultrafeedback-curator (credentials: owner/12345678). Try browsing by discarded or using the sort feature to find problematic records yourself.
## Dataset processing
1. We have identified a buggy behaviour of how `overall_score` was generated in the UltraFeedback dataset using the Critique Model, which caused very low quality (and rated) responses to get a very high score. The reason [is this line](https://github.com/OpenBMB/UltraFeedback/blob/e662fd291e5bdf9103a70c2496dc8f1fbcaefe7b/src/data_annotation/annotate_critique.py#L81) which will give a **`10` to responses that get a `1` from the Critique model**.
2. To **benefit from the preference data of UltraFeedback** (aspect-based preference data: honesty, instruction-following, etc.) and not the **Critique model** (which **evaluates a single response individually**), we have opted for not using `overall_score` and compute the mean of preference ratings instead.
3. We **select the best reponse based on this mean** (named `best_rated_response`), and keep the one based on the overall_score for comparison purposes
4. We **select a random response with lower mean rating** (or equal in the worst case scenario, for preference tuning we'll filter those cases out), named `random_response_for_best_rated`. This follows the method described in the Zephyr paper of picking a random response instead of the lowest rated response. In any case, we keep all completions for people looking at additional approaches. One could binarize the data differently, for example generating several pairs per row based on their ranking (as done on the OpenAI work).
5. We have tried to **keep all additional data for reproducibility**.
Please note that `*_best_overall` scores are in the `[1,10]` range and `*_best_rated` are in the `[1,5]` range.
Based on an initial analysis, using mean rating vs overall_score picks a different chosen response in ~30K examples (out of ~63K). Additionally, using overall_score results in picking responses from less powerful models more often. See the distribution below:
| argilla/ultrafeedback-binarized-curation | [
"region:us"
]
| 2023-11-14T16:47:09+00:00 | {"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "best_rated_is_different_from_best_overall", "dtype": "bool"}, {"name": "best_overall_model", "dtype": "string"}, {"name": "score_best_overall", "dtype": "float64"}, {"name": "best_rated_model", "dtype": "string"}, {"name": "score_best_rated", "dtype": "float64"}, {"name": "best_overall_score_response", "struct": [{"name": "annotations", "struct": [{"name": "helpfulness", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "Rationale For Rating", "dtype": "string"}, {"name": "Type", "sequence": "string"}]}, {"name": "honesty", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}]}, {"name": "instruction_following", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}]}, {"name": "truthfulness", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "Rationale For Rating", "dtype": "string"}, {"name": "Type", "sequence": "string"}]}]}, {"name": "average_rating", "dtype": "float64"}, {"name": "critique", "dtype": "string"}, {"name": "custom_system_prompt", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "overall_score", "dtype": "float64"}, {"name": "principle", "dtype": "string"}, {"name": "response", "dtype": "string"}]}, {"name": "random_response_for_best_overall", "struct": [{"name": "annotations", "struct": [{"name": "helpfulness", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "Rationale For Rating", "dtype": "string"}, {"name": "Type", "sequence": "string"}]}, {"name": "honesty", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}]}, {"name": "instruction_following", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}]}, {"name": "truthfulness", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "Rationale For Rating", "dtype": "string"}, {"name": "Type", "sequence": "string"}]}]}, {"name": "average_rating", "dtype": "float64"}, {"name": "critique", "dtype": "string"}, {"name": "custom_system_prompt", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "overall_score", "dtype": "float64"}, {"name": "principle", "dtype": "string"}, {"name": "response", "dtype": "string"}]}, {"name": "best_rated_response", "struct": [{"name": "annotations", "struct": [{"name": "helpfulness", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "Rationale For Rating", "dtype": "string"}, {"name": "Type", "sequence": "string"}]}, {"name": "honesty", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}]}, {"name": "instruction_following", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}]}, {"name": "truthfulness", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "Rationale For Rating", "dtype": "string"}, {"name": "Type", "sequence": "string"}]}]}, {"name": "average_rating", "dtype": "float64"}, {"name": "critique", "dtype": "string"}, {"name": "custom_system_prompt", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "overall_score", "dtype": "float64"}, {"name": "principle", "dtype": "string"}, {"name": "response", "dtype": "string"}]}, {"name": "random_response_for_best_rated", "struct": [{"name": "annotations", "struct": [{"name": "helpfulness", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "Rationale For Rating", "dtype": "string"}, {"name": "Type", "sequence": "string"}]}, {"name": "honesty", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}]}, {"name": "instruction_following", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}]}, {"name": "truthfulness", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "Rationale For Rating", "dtype": "string"}, {"name": "Type", "sequence": "string"}]}]}, {"name": "average_rating", "dtype": "float64"}, {"name": "critique", "dtype": "string"}, {"name": "custom_system_prompt", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "overall_score", "dtype": "float64"}, {"name": "principle", "dtype": "string"}, {"name": "response", "dtype": "string"}]}, {"name": "score_random_response_for_best_overall", "dtype": "float64"}, {"name": "score_random_response_for_rated", "dtype": "float64"}, {"name": "completions", "list": [{"name": "annotations", "struct": [{"name": "helpfulness", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "Rationale For Rating", "dtype": "string"}, {"name": "Type", "sequence": "string"}]}, {"name": "honesty", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}]}, {"name": "instruction_following", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}]}, {"name": "truthfulness", "struct": [{"name": "Rating", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "Rationale For Rating", "dtype": "string"}, {"name": "Type", "sequence": "string"}]}]}, {"name": "average_rating", "dtype": "float64"}, {"name": "critique", "dtype": "string"}, {"name": "custom_system_prompt", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "overall_score", "dtype": "float64"}, {"name": "principle", "dtype": "string"}, {"name": "response", "dtype": "string"}]}, {"name": "random_response_for_rated", "dtype": "float64"}, {"name": "best_overall_score_response_critique_sentiment", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 1642965325, "num_examples": 63967}], "download_size": 676228258, "dataset_size": 1642965325}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-28T17:03:54+00:00 | []
| []
| TAGS
#region-us
| # Ultrafeedback binarized dataset using the mean of preference ratings
## Introduction
This dataset contains the result of curation work performed by Argilla (using Argilla ).
After visually browsing around 200 examples using the sort and filter feature of Argilla, we noticed a strong mismatch between the 'overall_score' in the original UF dataset (and the Zephyr train_prefs dataset) and the quality of the chosen response.
By adding the critique rationale to our Argilla Dataset, we confirmed the critique rationale was highly negative, whereas the rating was very high (the highest in fact: '10'). See screenshot below for one example of this issue. After some quick investigation, we identified hundreds of examples having the same issue and a potential bug on the UltraFeedback repo.
!image/png
For context, this is the corresponding example within the 'train_prefs' dataset with a 'score_chosen' of '10'.
The dataset is fully open and browsable at URL (credentials: owner/12345678). Try browsing by discarded or using the sort feature to find problematic records yourself.
## Dataset processing
1. We have identified a buggy behaviour of how 'overall_score' was generated in the UltraFeedback dataset using the Critique Model, which caused very low quality (and rated) responses to get a very high score. The reason is this line which will give a '10' to responses that get a '1' from the Critique model.
2. To benefit from the preference data of UltraFeedback (aspect-based preference data: honesty, instruction-following, etc.) and not the Critique model (which evaluates a single response individually), we have opted for not using 'overall_score' and compute the mean of preference ratings instead.
3. We select the best reponse based on this mean (named 'best_rated_response'), and keep the one based on the overall_score for comparison purposes
4. We select a random response with lower mean rating (or equal in the worst case scenario, for preference tuning we'll filter those cases out), named 'random_response_for_best_rated'. This follows the method described in the Zephyr paper of picking a random response instead of the lowest rated response. In any case, we keep all completions for people looking at additional approaches. One could binarize the data differently, for example generating several pairs per row based on their ranking (as done on the OpenAI work).
5. We have tried to keep all additional data for reproducibility.
Please note that '*_best_overall' scores are in the '[1,10]' range and '*_best_rated' are in the '[1,5]' range.
Based on an initial analysis, using mean rating vs overall_score picks a different chosen response in ~30K examples (out of ~63K). Additionally, using overall_score results in picking responses from less powerful models more often. See the distribution below:
!image/png
| [
"# Ultrafeedback binarized dataset using the mean of preference ratings",
"## Introduction\n\nThis dataset contains the result of curation work performed by Argilla (using Argilla ). \n\n\nAfter visually browsing around 200 examples using the sort and filter feature of Argilla, we noticed a strong mismatch between the 'overall_score' in the original UF dataset (and the Zephyr train_prefs dataset) and the quality of the chosen response. \n\nBy adding the critique rationale to our Argilla Dataset, we confirmed the critique rationale was highly negative, whereas the rating was very high (the highest in fact: '10'). See screenshot below for one example of this issue. After some quick investigation, we identified hundreds of examples having the same issue and a potential bug on the UltraFeedback repo.\n\n\n\n!image/png\n\nFor context, this is the corresponding example within the 'train_prefs' dataset with a 'score_chosen' of '10'.\n\n\nThe dataset is fully open and browsable at URL (credentials: owner/12345678). Try browsing by discarded or using the sort feature to find problematic records yourself.",
"## Dataset processing\n\n1. We have identified a buggy behaviour of how 'overall_score' was generated in the UltraFeedback dataset using the Critique Model, which caused very low quality (and rated) responses to get a very high score. The reason is this line which will give a '10' to responses that get a '1' from the Critique model.\n2. To benefit from the preference data of UltraFeedback (aspect-based preference data: honesty, instruction-following, etc.) and not the Critique model (which evaluates a single response individually), we have opted for not using 'overall_score' and compute the mean of preference ratings instead.\n3. We select the best reponse based on this mean (named 'best_rated_response'), and keep the one based on the overall_score for comparison purposes\n4. We select a random response with lower mean rating (or equal in the worst case scenario, for preference tuning we'll filter those cases out), named 'random_response_for_best_rated'. This follows the method described in the Zephyr paper of picking a random response instead of the lowest rated response. In any case, we keep all completions for people looking at additional approaches. One could binarize the data differently, for example generating several pairs per row based on their ranking (as done on the OpenAI work).\n5. We have tried to keep all additional data for reproducibility.\n\nPlease note that '*_best_overall' scores are in the '[1,10]' range and '*_best_rated' are in the '[1,5]' range.\n\nBased on an initial analysis, using mean rating vs overall_score picks a different chosen response in ~30K examples (out of ~63K). Additionally, using overall_score results in picking responses from less powerful models more often. See the distribution below:\n\n\n\n!image/png"
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"## Dataset processing\n\n1. We have identified a buggy behaviour of how 'overall_score' was generated in the UltraFeedback dataset using the Critique Model, which caused very low quality (and rated) responses to get a very high score. The reason is this line which will give a '10' to responses that get a '1' from the Critique model.\n2. To benefit from the preference data of UltraFeedback (aspect-based preference data: honesty, instruction-following, etc.) and not the Critique model (which evaluates a single response individually), we have opted for not using 'overall_score' and compute the mean of preference ratings instead.\n3. We select the best reponse based on this mean (named 'best_rated_response'), and keep the one based on the overall_score for comparison purposes\n4. We select a random response with lower mean rating (or equal in the worst case scenario, for preference tuning we'll filter those cases out), named 'random_response_for_best_rated'. This follows the method described in the Zephyr paper of picking a random response instead of the lowest rated response. In any case, we keep all completions for people looking at additional approaches. One could binarize the data differently, for example generating several pairs per row based on their ranking (as done on the OpenAI work).\n5. We have tried to keep all additional data for reproducibility.\n\nPlease note that '*_best_overall' scores are in the '[1,10]' range and '*_best_rated' are in the '[1,5]' range.\n\nBased on an initial analysis, using mean rating vs overall_score picks a different chosen response in ~30K examples (out of ~63K). Additionally, using overall_score results in picking responses from less powerful models more often. See the distribution below:\n\n\n\n!image/png"
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"passage: TAGS\n#region-us \n# Ultrafeedback binarized dataset using the mean of preference ratings## Introduction\n\nThis dataset contains the result of curation work performed by Argilla (using Argilla ). \n\n\nAfter visually browsing around 200 examples using the sort and filter feature of Argilla, we noticed a strong mismatch between the 'overall_score' in the original UF dataset (and the Zephyr train_prefs dataset) and the quality of the chosen response. \n\nBy adding the critique rationale to our Argilla Dataset, we confirmed the critique rationale was highly negative, whereas the rating was very high (the highest in fact: '10'). See screenshot below for one example of this issue. After some quick investigation, we identified hundreds of examples having the same issue and a potential bug on the UltraFeedback repo.\n\n\n\n!image/png\n\nFor context, this is the corresponding example within the 'train_prefs' dataset with a 'score_chosen' of '10'.\n\n\nThe dataset is fully open and browsable at URL (credentials: owner/12345678). Try browsing by discarded or using the sort feature to find problematic records yourself."
]
|
2d70a8dc87c6f391b84458677a85d30bc4d0409c | # Dataset Card for "alt_pantheon"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mickume/alt_pantheon | [
"region:us"
]
| 2023-11-14T16:47:41+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 680549100, "num_examples": 3655263}], "download_size": 419978282, "dataset_size": 680549100}} | 2023-11-14T17:13:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "alt_pantheon"
More Information needed | [
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|
870c212a38d5440bd1cd0467548e874160f20dc8 | # Dataset Card for "ad_banner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | thangquoc/ad_banner | [
"region:us"
]
| 2023-11-14T16:59:58+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 86615696.13, "num_examples": 1362}], "download_size": 84006544, "dataset_size": 86615696.13}} | 2023-11-14T17:00:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ad_banner"
More Information needed | [
"# Dataset Card for \"ad_banner\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ad_banner\"\n\nMore Information needed"
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|
3e08bf718a14d07d5459617a2e4f5547b1f3373b | # Dataset Card for "source_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | FlyingFishzzz/source_test | [
"region:us"
]
| 2023-11-14T17:01:38+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_seg", "dtype": "image"}, {"name": "landmarks", "dtype": "string"}, {"name": "spiga", "sequence": {"sequence": "float64"}}, {"name": "spiga_seg", "dtype": "image"}, {"name": "image_caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 488488715.0, "num_examples": 1588}], "download_size": 487390223, "dataset_size": 488488715.0}} | 2023-11-14T17:35:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "source_test"
More Information needed | [
"# Dataset Card for \"source_test\"\n\nMore Information needed"
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|
e4f61df902d1e3ec30f3c54a7874f3aeb7b90301 | # Dataset Card for "destination_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | FlyingFishzzz/destination_test | [
"region:us"
]
| 2023-11-14T17:22:16+00:00 | {"dataset_info": {"features": [{"name": "target", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "landmarks", "dtype": "string"}, {"name": "condition", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 476552150.944, "num_examples": 1588}], "download_size": 475421663, "dataset_size": 476552150.944}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-14T17:47:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "destination_test"
More Information needed | [
"# Dataset Card for \"destination_test\"\n\nMore Information needed"
]
| [
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"# Dataset Card for \"destination_test\"\n\nMore Information needed"
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|
3cd9695e8042c5aee6cee9ca57e8f50c821823a0 | Nvidia Documentation Question and Answer pairs
Q&A dataset for LLM finetuning about the NVIDIA about SDKs and blogs
This dataset is obtained by generating Q&A pairs from a few NVIDIA websites such as development kits and guides. This data can be used to fine-tune any LLM for indulging knowledge about NVIDIA into them.
Source: https://www.kaggle.com/datasets/gondimalladeepesh/nvidia-documentation-question-and-answer-pairs | ajsbsd/nvidia-qa | [
"license:bsd",
"region:us"
]
| 2023-11-14T17:26:24+00:00 | {"license": "bsd"} | 2023-11-14T17:36:58+00:00 | []
| []
| TAGS
#license-bsd #region-us
| Nvidia Documentation Question and Answer pairs
Q&A dataset for LLM finetuning about the NVIDIA about SDKs and blogs
This dataset is obtained by generating Q&A pairs from a few NVIDIA websites such as development kits and guides. This data can be used to fine-tune any LLM for indulging knowledge about NVIDIA into them.
Source: URL | []
| [
"TAGS\n#license-bsd #region-us \n"
]
| [
12
]
| [
"passage: TAGS\n#license-bsd #region-us \n"
]
|
5f6d9586fd2fdeb0b1f4b20660d738a0fe215fbc | # Dataset Card for "validation_2000_cutoff_llama-2-7b-tyellow-2k-cutoff-LR1-clean-train_first_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Rewcifer/validation_2000_cutoff_llama-2-7b-tyellow-2k-cutoff-LR1-clean-train_first_100 | [
"region:us"
]
| 2023-11-14T17:35:46+00:00 | {"dataset_info": {"features": [{"name": "labels_and_findings", "dtype": "string"}, {"name": "prompts", "dtype": "string"}, {"name": "true_findings", "dtype": "string"}, {"name": "generated_texts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 895238, "num_examples": 100}], "download_size": 252291, "dataset_size": 895238}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-14T17:35:50+00:00 | []
| []
| TAGS
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| # Dataset Card for "validation_2000_cutoff_llama-2-7b-tyellow-2k-cutoff-LR1-clean-train_first_100"
More Information needed | [
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|
e697a877fbefa1cabaae565319dc14536801d1dc |
**SlimOrca-ShareGPT**
This dataset is in Vicuna/ShareGPT format. There are 517981 set of conversations. Each set having 2 conversations.
Original dataset was released by [Open-Orca](https://huggingface.co/datasets/Open-Orca/SlimOrca). I have refined it so that "system" is not present.
Idea is to check how this dataset will perform on Llama-2 & Mistral Models. I will relese both models very soon.
Will this dataset help to improve performance of fine tuned model?
All the credit goes to the Open-Orca team for releasing Orca & SlimOrca datasets. | ajibawa-2023/SlimOrca-ShareGPT | [
"task_categories:token-classification",
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"region:us"
]
| 2023-11-14T17:42:27+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["token-classification", "text-classification"], "pretty_name": "SoS"} | 2023-11-14T20:06:58+00:00 | []
| [
"en"
]
| TAGS
#task_categories-token-classification #task_categories-text-classification #size_categories-100K<n<1M #language-English #license-mit #region-us
|
SlimOrca-ShareGPT
This dataset is in Vicuna/ShareGPT format. There are 517981 set of conversations. Each set having 2 conversations.
Original dataset was released by Open-Orca. I have refined it so that "system" is not present.
Idea is to check how this dataset will perform on Llama-2 & Mistral Models. I will relese both models very soon.
Will this dataset help to improve performance of fine tuned model?
All the credit goes to the Open-Orca team for releasing Orca & SlimOrca datasets. | []
| [
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|
8d7ced5cb4f561e80fb4e6decfda2ea4fc9d5ec8 | ---
language:
- ru
This dataset is a texts in Russian and French | Glazastik/rutextdataset | [
"language:ru",
"language:fr",
"region:us"
]
| 2023-11-14T17:45:45+00:00 | {"language": ["ru", "fr"]} | 2023-11-14T17:46:48+00:00 | []
| [
"ru",
"fr"
]
| TAGS
#language-Russian #language-French #region-us
| ---
language:
- ru
This dataset is a texts in Russian and French | []
| [
"TAGS\n#language-Russian #language-French #region-us \n"
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| [
17
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| [
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|
5ccd7dc6a08ed0882058542eac4319ca5e202b1d |
# ReviewRobot Dataset
This dataset contains the raw dataset from the ReviewRobot work. It was curated by Wang et al. 2020 for the purpose of explainable peer review generation of research papers.
## Dataset Details
### Dataset Description
The raw research paper text (extracted using Grobid by the authors) and the peer reviews are made available here. Each paper can have multiple reviews, we only keep the longest review for each paper.
### Dataset Sources [optional]
- **Repository:** https://github.com/EagleW/ReviewRobot/tree/master
- **Paper:** ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis
## Citation
**BibTeX:**
@inproceedings{wang-etal-2020-reviewrobot,
title = "{R}eview{R}obot: Explainable Paper Review Generation based on Knowledge Synthesis",
author = "Wang, Qingyun and
Zeng, Qi and
Huang, Lifu and
Knight, Kevin and
Ji, Heng and
Rajani, Nazneen Fatema",
booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
month = dec,
year = "2020",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.inlg-1.44",
pages = "384--397"
} | shrutisingh/reviewrobot_reviews | [
"task_categories:text-generation",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-11-14T18:02:50+00:00 | {"language": ["en"], "license": "cc-by-sa-4.0", "task_categories": ["text-generation"]} | 2023-11-17T21:09:52+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #language-English #license-cc-by-sa-4.0 #region-us
|
# ReviewRobot Dataset
This dataset contains the raw dataset from the ReviewRobot work. It was curated by Wang et al. 2020 for the purpose of explainable peer review generation of research papers.
## Dataset Details
### Dataset Description
The raw research paper text (extracted using Grobid by the authors) and the peer reviews are made available here. Each paper can have multiple reviews, we only keep the longest review for each paper.
### Dataset Sources [optional]
- Repository: URL
- Paper: ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis
BibTeX:
@inproceedings{wang-etal-2020-reviewrobot,
title = "{R}eview{R}obot: Explainable Paper Review Generation based on Knowledge Synthesis",
author = "Wang, Qingyun and
Zeng, Qi and
Huang, Lifu and
Knight, Kevin and
Ji, Heng and
Rajani, Nazneen Fatema",
booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
month = dec,
year = "2020",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "URL
pages = "384--397"
} | [
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"### Dataset Sources [optional]\n\n- Repository: URL\n- Paper: ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis\n\n\nBibTeX:\n\n@inproceedings{wang-etal-2020-reviewrobot,\n title = \"{R}eview{R}obot: Explainable Paper Review Generation based on Knowledge Synthesis\",\n author = \"Wang, Qingyun and\n Zeng, Qi and\n Huang, Lifu and\n Knight, Kevin and\n Ji, Heng and\n Rajani, Nazneen Fatema\",\n booktitle = \"Proceedings of the 13th International Conference on Natural Language Generation\",\n month = dec,\n year = \"2020\",\n address = \"Dublin, Ireland\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"384--397\"\n}"
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"## Dataset Details",
"### Dataset Description\n\nThe raw research paper text (extracted using Grobid by the authors) and the peer reviews are made available here. Each paper can have multiple reviews, we only keep the longest review for each paper.",
"### Dataset Sources [optional]\n\n- Repository: URL\n- Paper: ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis\n\n\nBibTeX:\n\n@inproceedings{wang-etal-2020-reviewrobot,\n title = \"{R}eview{R}obot: Explainable Paper Review Generation based on Knowledge Synthesis\",\n author = \"Wang, Qingyun and\n Zeng, Qi and\n Huang, Lifu and\n Knight, Kevin and\n Ji, Heng and\n Rajani, Nazneen Fatema\",\n booktitle = \"Proceedings of the 13th International Conference on Natural Language Generation\",\n month = dec,\n year = \"2020\",\n address = \"Dublin, Ireland\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"384--397\"\n}"
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]
|
0ec945a3d2d5342a136a49804ab7685f84e58030 | # Dataset Card for "zelenskiy-speeches"
Speeches given by the president of Ukraine Volodymyr Zelensky
Languages: Ukrainian, English
Source: [president.gov.ua](https://www.president.gov.ua/news/speeches)
Auto-updated daily by Github Actions of [zelensky-speech-fetcher](https://github.com/medvedev/zelensky-speech-fetcher)
License: [CC BY-NC-ND 4.0 Deed](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en) | slava-medvedev/zelensky-speeches | [
"task_categories:summarization",
"task_categories:text-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:uk",
"language:en",
"license:cc-by-4.0",
"zelensky",
"ukraine",
"politics",
"region:us"
]
| 2023-11-14T18:43:21+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["uk", "en"], "license": "cc-by-4.0", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "task_categories": ["summarization", "text-classification"], "pretty_name": "Speeches given by the president of Ukraine Volodymyr Zelensky\nLanguage: Ukrainian\nSource: https://www.president.gov.ua/news/speeches", "dataset_info": {"features": [{"name": "date", "dtype": "int64"}, {"name": "link", "dtype": "string"}, {"name": "topic", "dtype": "string"}, {"name": "full_text", "dtype": "string"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14889401, "num_examples": 2068}], "download_size": 7488896, "dataset_size": 14889401}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["zelensky", "ukraine", "politics"]} | 2024-02-17T14:23:48+00:00 | []
| [
"uk",
"en"
]
| TAGS
#task_categories-summarization #task_categories-text-classification #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #language-Ukrainian #language-English #license-cc-by-4.0 #zelensky #ukraine #politics #region-us
| # Dataset Card for "zelenskiy-speeches"
Speeches given by the president of Ukraine Volodymyr Zelensky
Languages: Ukrainian, English
Source: URL
Auto-updated daily by Github Actions of zelensky-speech-fetcher
License: CC BY-NC-ND 4.0 Deed | [
"# Dataset Card for \"zelenskiy-speeches\"\n\nSpeeches given by the president of Ukraine Volodymyr Zelensky \nLanguages: Ukrainian, English \nSource: URL \nAuto-updated daily by Github Actions of zelensky-speech-fetcher \nLicense: CC BY-NC-ND 4.0 Deed"
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"# Dataset Card for \"zelenskiy-speeches\"\n\nSpeeches given by the president of Ukraine Volodymyr Zelensky \nLanguages: Ukrainian, English \nSource: URL \nAuto-updated daily by Github Actions of zelensky-speech-fetcher \nLicense: CC BY-NC-ND 4.0 Deed"
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|
01f8324dd46bf875f8e9cc77488a8f7d6b159a9e | Based on https://huggingface.co/datasets/Linkseed/hacker_news_with_comments | ristew/askhn | [
"region:us"
]
| 2023-11-14T19:00:43+00:00 | {} | 2023-11-14T20:41:41+00:00 | []
| []
| TAGS
#region-us
| Based on URL | []
| [
"TAGS\n#region-us \n"
]
| [
6
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| [
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|
aa01314457e69793bc5ff4aceba5130189b81ae7 |
# Dataset Card for Evaluation run of openaccess-ai-collective/mistral-7b-slimorcaboros
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/openaccess-ai-collective/mistral-7b-slimorcaboros
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [openaccess-ai-collective/mistral-7b-slimorcaboros](https://huggingface.co/openaccess-ai-collective/mistral-7b-slimorcaboros) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_openaccess-ai-collective__mistral-7b-slimorcaboros_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-14T19:06:13.668768](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__mistral-7b-slimorcaboros_public/blob/main/results_2023-11-14T19-06-13.668768.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6301042082006363,
"acc_stderr": 0.032164201740811346,
"acc_norm": 0.6380190670382948,
"acc_norm_stderr": 0.03283508976201021,
"mc1": 0.390452876376989,
"mc1_stderr": 0.01707823074343145,
"mc2": 0.5581158489169444,
"mc2_stderr": 0.01565820515437776,
"em": 0.03859060402684564,
"em_stderr": 0.001972579977587539,
"f1": 0.11617135067114018,
"f1_stderr": 0.0024204909854951134
},
"harness|arc:challenge|25": {
"acc": 0.6117747440273038,
"acc_stderr": 0.014241614207414054,
"acc_norm": 0.636518771331058,
"acc_norm_stderr": 0.014056207319068285
},
"harness|hellaswag|10": {
"acc": 0.650368452499502,
"acc_stderr": 0.004758790172436686,
"acc_norm": 0.8369846644094802,
"acc_norm_stderr": 0.0036862475593618512
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5851851851851851,
"acc_stderr": 0.04256193767901408,
"acc_norm": 0.5851851851851851,
"acc_norm_stderr": 0.04256193767901408
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6447368421052632,
"acc_stderr": 0.03894734487013316,
"acc_norm": 0.6447368421052632,
"acc_norm_stderr": 0.03894734487013316
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
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"harness|gsm8k|5": {
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}
}
```
### 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] | open-llm-leaderboard/details_openaccess-ai-collective__mistral-7b-slimorcaboros | [
"region:us"
]
| 2023-11-14T19:09:15+00:00 | {"pretty_name": "Evaluation run of openaccess-ai-collective/mistral-7b-slimorcaboros", "dataset_summary": "Dataset automatically created during the evaluation run of model [openaccess-ai-collective/mistral-7b-slimorcaboros](https://huggingface.co/openaccess-ai-collective/mistral-7b-slimorcaboros) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_openaccess-ai-collective__mistral-7b-slimorcaboros_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-14T19:06:13.668768](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__mistral-7b-slimorcaboros_public/blob/main/results_2023-11-14T19-06-13.668768.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6301042082006363,\n \"acc_stderr\": 0.032164201740811346,\n \"acc_norm\": 0.6380190670382948,\n \"acc_norm_stderr\": 0.03283508976201021,\n \"mc1\": 0.390452876376989,\n \"mc1_stderr\": 0.01707823074343145,\n \"mc2\": 0.5581158489169444,\n \"mc2_stderr\": 0.01565820515437776,\n \"em\": 0.03859060402684564,\n \"em_stderr\": 0.001972579977587539,\n \"f1\": 0.11617135067114018,\n \"f1_stderr\": 0.0024204909854951134\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6117747440273038,\n \"acc_stderr\": 0.014241614207414054,\n \"acc_norm\": 0.636518771331058,\n \"acc_norm_stderr\": 0.014056207319068285\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.650368452499502,\n \"acc_stderr\": 0.004758790172436686,\n \"acc_norm\": 0.8369846644094802,\n \"acc_norm_stderr\": 0.0036862475593618512\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5851851851851851,\n \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6447368421052632,\n \"acc_stderr\": 0.03894734487013316,\n \"acc_norm\": 0.6447368421052632,\n \"acc_norm_stderr\": 0.03894734487013316\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.660377358490566,\n \"acc_stderr\": 0.029146904747798328,\n \"acc_norm\": 0.660377358490566,\n \"acc_norm_stderr\": 0.029146904747798328\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6184971098265896,\n \"acc_stderr\": 0.03703851193099521,\n \"acc_norm\": 0.6184971098265896,\n \"acc_norm_stderr\": 0.03703851193099521\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 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| TAGS
#region-us
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# Dataset Card for Evaluation run of openaccess-ai-collective/mistral-7b-slimorcaboros
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model openaccess-ai-collective/mistral-7b-slimorcaboros on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-14T19:06:13.668768(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### 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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|
fb5ada871e95e1162399ae69711bff0752c9aba3 | # Dataset Card for "commonvoice_13_0_pt_48kHz_simplificado_augmented_white_noise"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | aomocelin/commonvoice_13_0_pt_48kHz_simplificado_augmented_white_noise | [
"region:us"
]
| 2023-11-14T19:15:12+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}, {"name": "sentence", "dtype": "string"}, {"name": "up_votes", "dtype": "int64"}, {"name": "down_votes", "dtype": "int64"}, {"name": "age", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "segment", "dtype": "string"}, {"name": "variant", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11720605773.5, "num_examples": 29020}, {"name": "test", "num_bytes": 281122914.928, "num_examples": 9072}], "download_size": 11993487504, "dataset_size": 12001728688.428}} | 2023-11-14T19:24:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "commonvoice_13_0_pt_48kHz_simplificado_augmented_white_noise"
More Information needed | [
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|
f0c2fa6607ca743d9331bdc2f7c9cf2ae83d2aa4 | # Dataset Card for "wine_type_bu"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Eitanli/wine_type_bu | [
"region:us"
]
| 2023-11-14T19:16:16+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "recipe", "dtype": "string"}, {"name": "wine_type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 110426494, "num_examples": 74465}], "download_size": 54694496, "dataset_size": 110426494}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-17T07:02:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wine_type_bu"
More Information needed | [
"# Dataset Card for \"wine_type_bu\"\n\nMore Information needed"
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|
49377d0bb343079548cb2e1d672569853d3ae1cb |
## Overview
The TwitCivility dataset is specifically developed to classify political incivility, focusing on multidimensional aspects of impoliteness and intolerance.
Detailed methodologies are outlined in our [paper](https://arxiv.org/abs/2305.14964).
## Languages
All text is written in English.
## Dataset Structure
### Data Fields
We release TwitCivility as a data frame with the following fields: <br />
**text**: This field contains the text (after preprocessing and anonymization) of the tweet. <br />
**impoliteness**: A binary indicator (1 or 0) representing the presence of impoliteness in the text. A value of 1 signifies impoliteness, while 0 indicates non-impoliteness. <br />
**intolerance**: Similarly, this binary value denotes the presence of intolerance in the text, with 1 indicating intolerance and 0 signifying non-intolerance. <br />
## Citation Information
```
@misc{incivility2023,
title={Detecting Multidimensional Political Incivility on Social Media},
author={Sagi Pendzel and Nir Lotan and Alon Zoizner and Einat Minkov},
year={2023},
eprint={2305.14964},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | incivility-UOH/TwitCivility | [
"task_categories:text-classification",
"language:en",
"license:mit",
"arxiv:2305.14964",
"region:us"
]
| 2023-11-14T19:25:02+00:00 | {"language": ["en"], "license": "mit", "task_categories": ["text-classification"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "impoliteness", "dtype": "int64"}, {"name": "intolerance", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2169574.4014020115, "num_examples": 10498}, {"name": "test", "num_bytes": 542703.5985979884, "num_examples": 2626}], "download_size": 1726706, "dataset_size": 2712278}} | 2023-11-16T16:12:02+00:00 | [
"2305.14964"
]
| [
"en"
]
| TAGS
#task_categories-text-classification #language-English #license-mit #arxiv-2305.14964 #region-us
|
## Overview
The TwitCivility dataset is specifically developed to classify political incivility, focusing on multidimensional aspects of impoliteness and intolerance.
Detailed methodologies are outlined in our paper.
## Languages
All text is written in English.
## Dataset Structure
### Data Fields
We release TwitCivility as a data frame with the following fields: <br />
text: This field contains the text (after preprocessing and anonymization) of the tweet. <br />
impoliteness: A binary indicator (1 or 0) representing the presence of impoliteness in the text. A value of 1 signifies impoliteness, while 0 indicates non-impoliteness. <br />
intolerance: Similarly, this binary value denotes the presence of intolerance in the text, with 1 indicating intolerance and 0 signifying non-intolerance. <br />
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"passage: TAGS\n#task_categories-text-classification #language-English #license-mit #arxiv-2305.14964 #region-us \n## Overview\nThe TwitCivility dataset is specifically developed to classify political incivility, focusing on multidimensional aspects of impoliteness and intolerance.\nDetailed methodologies are outlined in our paper.## Languages\nAll text is written in English.## Dataset Structure### Data Fields\nWe release TwitCivility as a data frame with the following fields: <br />\ntext: This field contains the text (after preprocessing and anonymization) of the tweet. <br />\nimpoliteness: A binary indicator (1 or 0) representing the presence of impoliteness in the text. A value of 1 signifies impoliteness, while 0 indicates non-impoliteness. <br />\nintolerance: Similarly, this binary value denotes the presence of intolerance in the text, with 1 indicating intolerance and 0 signifying non-intolerance. <br />"
]
|
569d67228ce6857035a031d358f0c1601534d3c6 | # Dataset Card for "multi_whole"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jlbaker361/multi_whole | [
"region:us"
]
| 2023-11-14T19:29:10+00:00 | {"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 203716, "num_examples": 10000}], "download_size": 107015, "dataset_size": 203716}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-14T19:29:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "multi_whole"
More Information needed | [
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|
125d2827c91e341e84f558d20b26aa9fa576e250 | ---
license: apache-2.0
# Dataset Card for Medical Question Answering Dataset
## 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 Description
### Dataset Summary
This dataset contains a collection of question-answer pairs related to various medical topics. The data is structured to provide comprehensive answers to specific medical questions, covering information, diagnosis, treatment, prevention, and susceptibility related to different health conditions.
### Supported Tasks and Leaderboards
The dataset is suitable for tasks like medical question answering, natural language understanding, and information retrieval in the healthcare domain.
### Languages
## Dataset Structure
### Data Instances
An example from the dataset:
- Question: "What are the treatments for acanthamoeba?"
- Answer: "Early diagnosis is essential for effective treatment of acanthamoeba..."
### Data Fields
- `question`: The medical question.
- `answer`: The answer to the medical question.
### Data Splits
The dataset is not split into training, validation, or test sets.
## Dataset Creation
### Curation Rationale
This dataset was created to facilitate research and development in medical question answering systems, aiming to improve access to medical information.
### Source Data
The data was compiled from various medical resources and designed to be comprehensive and informative.
### Annotations
Not applicable as the dataset consists of pre-existing question-answer pairs.
### Personal and Sensitive Information
Questions and answers do not contain personal information. However, users should be cautious when integrating this data into applications, considering privacy and ethical implications.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset can aid in developing systems that provide quick and accurate medical information, potentially improving healthcare outcomes.
### Discussion of Biases
There are no known biases in the dataset.
### Other Known Limitations
The dataset might is limited in scope regarding certain medical conditions.
## Additional Information
| Taylor658/med_train_nov23 | [
"region:us"
]
| 2023-11-14T19:45:19+00:00 | {} | 2023-12-06T07:46:19+00:00 | []
| []
| TAGS
#region-us
| ---
license: apache-2.0
# Dataset Card for Medical Question Answering Dataset
## 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 Description
### Dataset Summary
This dataset contains a collection of question-answer pairs related to various medical topics. The data is structured to provide comprehensive answers to specific medical questions, covering information, diagnosis, treatment, prevention, and susceptibility related to different health conditions.
### Supported Tasks and Leaderboards
The dataset is suitable for tasks like medical question answering, natural language understanding, and information retrieval in the healthcare domain.
### Languages
## Dataset Structure
### Data Instances
An example from the dataset:
- Question: "What are the treatments for acanthamoeba?"
- Answer: "Early diagnosis is essential for effective treatment of acanthamoeba..."
### Data Fields
- 'question': The medical question.
- 'answer': The answer to the medical question.
### Data Splits
The dataset is not split into training, validation, or test sets.
## Dataset Creation
### Curation Rationale
This dataset was created to facilitate research and development in medical question answering systems, aiming to improve access to medical information.
### Source Data
The data was compiled from various medical resources and designed to be comprehensive and informative.
### Annotations
Not applicable as the dataset consists of pre-existing question-answer pairs.
### Personal and Sensitive Information
Questions and answers do not contain personal information. However, users should be cautious when integrating this data into applications, considering privacy and ethical implications.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset can aid in developing systems that provide quick and accurate medical information, potentially improving healthcare outcomes.
### Discussion of Biases
There are no known biases in the dataset.
### Other Known Limitations
The dataset might is limited in scope regarding certain medical conditions.
## Additional Information
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"## Dataset Description",
"### Dataset Summary\n\nThis dataset contains a collection of question-answer pairs related to various medical topics. The data is structured to provide comprehensive answers to specific medical questions, covering information, diagnosis, treatment, prevention, and susceptibility related to different health conditions.",
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"## Dataset Creation",
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"### Source Data\n\nThe data was compiled from various medical resources and designed to be comprehensive and informative.",
"### Annotations\n\nNot applicable as the dataset consists of pre-existing question-answer pairs.",
"### Personal and Sensitive Information\n\nQuestions and answers do not contain personal information. However, users should be cautious when integrating this data into applications, considering privacy and ethical implications.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThis dataset can aid in developing systems that provide quick and accurate medical information, potentially improving healthcare outcomes.",
"### Discussion of Biases\n\nThere are no known biases in the dataset.",
"### Other Known Limitations\n\nThe dataset might is limited in scope regarding certain medical conditions.",
"## Additional Information"
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"## Dataset Description",
"### Dataset Summary\n\nThis dataset contains a collection of question-answer pairs related to various medical topics. The data is structured to provide comprehensive answers to specific medical questions, covering information, diagnosis, treatment, prevention, and susceptibility related to different health conditions.",
"### Supported Tasks and Leaderboards\n\nThe dataset is suitable for tasks like medical question answering, natural language understanding, and information retrieval in the healthcare domain.",
"### Languages",
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"### Data Instances\n\nAn example from the dataset:\n- Question: \"What are the treatments for acanthamoeba?\"\n- Answer: \"Early diagnosis is essential for effective treatment of acanthamoeba...\"",
"### Data Fields\n\n- 'question': The medical question.\n- 'answer': The answer to the medical question.",
"### Data Splits\n\nThe dataset is not split into training, validation, or test sets.",
"## Dataset Creation",
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"### Source Data\n\nThe data was compiled from various medical resources and designed to be comprehensive and informative.",
"### Annotations\n\nNot applicable as the dataset consists of pre-existing question-answer pairs.",
"### Personal and Sensitive Information\n\nQuestions and answers do not contain personal information. However, users should be cautious when integrating this data into applications, considering privacy and ethical implications.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThis dataset can aid in developing systems that provide quick and accurate medical information, potentially improving healthcare outcomes.",
"### Discussion of Biases\n\nThere are no known biases in the dataset.",
"### Other Known Limitations\n\nThe dataset might is limited in scope regarding certain medical conditions.",
"## Additional Information"
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]
|
a9e15c37b72492693a4581907e4623e233459fb1 |
# Dataset Card for Evaluation run of KoboldAI/LLaMA2-13B-Tiefighter
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_KoboldAI__LLaMA2-13B-Tiefighter_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-14T20:25:09.144693](https://huggingface.co/datasets/open-llm-leaderboard/details_KoboldAI__LLaMA2-13B-Tiefighter_public/blob/main/results_2023-11-14T20-25-09.144693.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.5460312696004332,
"acc_stderr": 0.03357446611113244,
"acc_norm": 0.5555362057698711,
"acc_norm_stderr": 0.03444530254256153,
"mc1": 0.379436964504284,
"mc1_stderr": 0.016987039266142985,
"mc2": 0.5301656358073983,
"mc2_stderr": 0.01568757011022921,
"em": 0.11115771812080537,
"em_stderr": 0.00321900621779521,
"f1": 0.1838915687919454,
"f1_stderr": 0.0033646558993111948
},
"harness|arc:challenge|25": {
"acc": 0.568259385665529,
"acc_stderr": 0.014474591427196202,
"acc_norm": 0.5989761092150171,
"acc_norm_stderr": 0.014322255790719867
},
"harness|hellaswag|10": {
"acc": 0.6500697072296355,
"acc_stderr": 0.004759729267943188,
"acc_norm": 0.8399721171081458,
"acc_norm_stderr": 0.003658826208101615
},
"harness|hendrycksTest-abstract_algebra|5": {
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"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
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"acc_norm": 0.4962962962962963,
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},
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"acc_norm": 0.5789473684210527,
"acc_norm_stderr": 0.04017901275981749
},
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"acc": 0.54,
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"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-clinical_knowledge|5": {
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"acc_norm": 0.6,
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},
"harness|hendrycksTest-college_biology|5": {
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},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252604
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.41,
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"acc_norm": 0.41,
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},
"harness|hendrycksTest-college_mathematics|5": {
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},
"harness|hendrycksTest-college_medicine|5": {
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},
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},
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"harness|hendrycksTest-marketing|5": {
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"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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"harness|hendrycksTest-moral_scenarios|5": {
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"harness|hendrycksTest-philosophy|5": {
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"harness|hendrycksTest-prehistory|5": {
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"acc_norm_stderr": 0.027163686038271146
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"harness|hendrycksTest-professional_accounting|5": {
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"acc_norm": 0.40070921985815605,
"acc_norm_stderr": 0.029233465745573083
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"harness|hendrycksTest-professional_law|5": {
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"acc_norm": 0.4335071707953064,
"acc_norm_stderr": 0.012656810383983965
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"harness|hendrycksTest-professional_medicine|5": {
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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"harness|gsm8k|5": {
"acc": 0.006823351023502654,
"acc_stderr": 0.002267537102254515
}
}
```
### 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] | open-llm-leaderboard/details_KoboldAI__LLaMA2-13B-Tiefighter | [
"region:us"
]
| 2023-11-14T20:28:13+00:00 | {"pretty_name": "Evaluation run of KoboldAI/LLaMA2-13B-Tiefighter", "dataset_summary": "Dataset automatically created during the evaluation run of model [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KoboldAI__LLaMA2-13B-Tiefighter_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-14T20:25:09.144693](https://huggingface.co/datasets/open-llm-leaderboard/details_KoboldAI__LLaMA2-13B-Tiefighter_public/blob/main/results_2023-11-14T20-25-09.144693.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5460312696004332,\n \"acc_stderr\": 0.03357446611113244,\n \"acc_norm\": 0.5555362057698711,\n \"acc_norm_stderr\": 0.03444530254256153,\n \"mc1\": 0.379436964504284,\n \"mc1_stderr\": 0.016987039266142985,\n \"mc2\": 0.5301656358073983,\n \"mc2_stderr\": 0.01568757011022921,\n \"em\": 0.11115771812080537,\n \"em_stderr\": 0.00321900621779521,\n \"f1\": 0.1838915687919454,\n \"f1_stderr\": 0.0033646558993111948\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.568259385665529,\n \"acc_stderr\": 0.014474591427196202,\n \"acc_norm\": 0.5989761092150171,\n \"acc_norm_stderr\": 0.014322255790719867\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6500697072296355,\n \"acc_stderr\": 0.004759729267943188,\n \"acc_norm\": 0.8399721171081458,\n \"acc_norm_stderr\": 0.003658826208101615\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4962962962962963,\n \"acc_stderr\": 0.04319223625811331,\n \"acc_norm\": 0.4962962962962963,\n \"acc_norm_stderr\": 0.04319223625811331\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5789473684210527,\n \"acc_stderr\": 0.04017901275981749,\n \"acc_norm\": 0.5789473684210527,\n \"acc_norm_stderr\": 0.04017901275981749\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.030151134457776285,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.030151134457776285\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5972222222222222,\n \"acc_stderr\": 0.04101405519842426,\n \"acc_norm\": 0.5972222222222222,\n \"acc_norm_stderr\": 0.04101405519842426\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5317919075144508,\n \"acc_stderr\": 0.03804749744364764,\n \"acc_norm\": 0.5317919075144508,\n \"acc_norm_stderr\": 0.03804749744364764\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3137254901960784,\n \"acc_stderr\": 0.04617034827006716,\n \"acc_norm\": 0.3137254901960784,\n \"acc_norm_stderr\": 0.04617034827006716\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.04793724854411021,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.04793724854411021\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.46808510638297873,\n \"acc_stderr\": 0.03261936918467382,\n \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.03261936918467382\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.35964912280701755,\n \"acc_stderr\": 0.045144961328736334,\n \"acc_norm\": 0.35964912280701755,\n \"acc_norm_stderr\": 0.045144961328736334\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.30952380952380953,\n \"acc_stderr\": 0.023809523809523857,\n \"acc_norm\": 0.30952380952380953,\n \"acc_norm_stderr\": 0.023809523809523857\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.29365079365079366,\n \"acc_stderr\": 0.04073524322147125,\n \"acc_norm\": 0.29365079365079366,\n \"acc_norm_stderr\": 0.04073524322147125\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6387096774193548,\n \"acc_stderr\": 0.027327548447957536,\n \"acc_norm\": 0.6387096774193548,\n \"acc_norm_stderr\": 0.027327548447957536\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.41379310344827586,\n \"acc_stderr\": 0.03465304488406795,\n \"acc_norm\": 0.41379310344827586,\n \"acc_norm_stderr\": 0.03465304488406795\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.036639749943912434,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.036639749943912434\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.702020202020202,\n \"acc_stderr\": 0.03258630383836556,\n \"acc_norm\": 0.702020202020202,\n \"acc_norm_stderr\": 0.03258630383836556\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.7772020725388601,\n \"acc_stderr\": 0.03003114797764154,\n \"acc_norm\": 0.7772020725388601,\n \"acc_norm_stderr\": 0.03003114797764154\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5051282051282051,\n \"acc_stderr\": 0.025349672906838653,\n \"acc_norm\": 0.5051282051282051,\n \"acc_norm_stderr\": 0.025349672906838653\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2962962962962963,\n \"acc_stderr\": 0.027840811495871937,\n \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.027840811495871937\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.5462184873949579,\n \"acc_stderr\": 0.03233943468182087,\n \"acc_norm\": 0.5462184873949579,\n \"acc_norm_stderr\": 0.03233943468182087\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7211009174311926,\n \"acc_stderr\": 0.019227468876463507,\n \"acc_norm\": 0.7211009174311926,\n \"acc_norm_stderr\": 0.019227468876463507\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.37962962962962965,\n \"acc_stderr\": 0.03309682581119035,\n \"acc_norm\": 0.37962962962962965,\n \"acc_norm_stderr\": 0.03309682581119035\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7468354430379747,\n \"acc_stderr\": 0.0283046579430353,\n \"acc_norm\": 0.7468354430379747,\n \"acc_norm_stderr\": 0.0283046579430353\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6636771300448431,\n \"acc_stderr\": 0.031708824268455005,\n \"acc_norm\": 0.6636771300448431,\n \"acc_norm_stderr\": 0.031708824268455005\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6183206106870229,\n \"acc_stderr\": 0.042607351576445594,\n \"acc_norm\": 0.6183206106870229,\n \"acc_norm_stderr\": 0.042607351576445594\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.043300437496507416,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.043300437496507416\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.656441717791411,\n \"acc_stderr\": 0.037311335196738925,\n \"acc_norm\": 0.656441717791411,\n \"acc_norm_stderr\": 0.037311335196738925\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.39285714285714285,\n \"acc_stderr\": 0.04635550135609976,\n \"acc_norm\": 0.39285714285714285,\n \"acc_norm_stderr\": 0.04635550135609976\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.6796116504854369,\n \"acc_stderr\": 0.04620284082280041,\n \"acc_norm\": 0.6796116504854369,\n \"acc_norm_stderr\": 0.04620284082280041\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8162393162393162,\n \"acc_stderr\": 0.025372139671722933,\n \"acc_norm\": 0.8162393162393162,\n \"acc_norm_stderr\": 0.025372139671722933\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.05021167315686779,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.05021167315686779\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7458492975734355,\n \"acc_stderr\": 0.015569254692045752,\n \"acc_norm\": 0.7458492975734355,\n \"acc_norm_stderr\": 0.015569254692045752\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6213872832369942,\n \"acc_stderr\": 0.026113749361310345,\n \"acc_norm\": 0.6213872832369942,\n \"acc_norm_stderr\": 0.026113749361310345\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.33631284916201115,\n \"acc_stderr\": 0.0158010037291459,\n \"acc_norm\": 0.33631284916201115,\n \"acc_norm_stderr\": 0.0158010037291459\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.027826109307283697,\n \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.027826109307283697\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6141479099678456,\n \"acc_stderr\": 0.027648149599751464,\n \"acc_norm\": 0.6141479099678456,\n \"acc_norm_stderr\": 0.027648149599751464\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.6080246913580247,\n \"acc_stderr\": 0.027163686038271146,\n \"acc_norm\": 0.6080246913580247,\n \"acc_norm_stderr\": 0.027163686038271146\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.40070921985815605,\n \"acc_stderr\": 0.029233465745573083,\n \"acc_norm\": 0.40070921985815605,\n \"acc_norm_stderr\": 0.029233465745573083\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4335071707953064,\n \"acc_stderr\": 0.012656810383983965,\n \"acc_norm\": 0.4335071707953064,\n \"acc_norm_stderr\": 0.012656810383983965\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5257352941176471,\n \"acc_stderr\": 0.030332578094555033,\n \"acc_norm\": 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{"config_name": "results", "data_files": [{"split": "2023_11_14T20_25_09.144693", "path": ["results_2023-11-14T20-25-09.144693.parquet"]}, {"split": "latest", "path": ["results_2023-11-14T20-25-09.144693.parquet"]}]}]} | 2023-11-14T20:29:00+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of KoboldAI/LLaMA2-13B-Tiefighter
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model KoboldAI/LLaMA2-13B-Tiefighter on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-14T20:25:09.144693(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### 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 Evaluation run of KoboldAI/LLaMA2-13B-Tiefighter",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model KoboldAI/LLaMA2-13B-Tiefighter on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
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|
1448ea4b072f6b53e80283f605d348a0b1ae7831 | # Dataset Card for "HumanPPI_reg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lhallee/HumanPPI_reg | [
"region:us"
]
| 2023-11-14T20:35:55+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "seqs", "dtype": "string"}, {"name": "labels", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 51590813, "num_examples": 26319}, {"name": "valid", "num_bytes": 475534, "num_examples": 234}, {"name": "test", "num_bytes": 343668, "num_examples": 180}], "download_size": 28561787, "dataset_size": 52410015}} | 2023-12-03T04:06:40+00:00 | []
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292f4f9ec4e868643a82f31d885b7d868a6dec44 | # Dataset Card for "HumanPPI_fold"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lhallee/HumanPPI_fold | [
"region:us"
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d8b9e0f9410bc014e0727d8129d24ccad1a7b701 |
# The Curio Tree Dataset
This dataset contains much of the tree inventory, images and stories data that was collected on the [Curio platform](https://www.youtube.com/@curio-xyz7991/videos) before it was sunset. The data was extraced from a number of database tables and includes;
- The inventory details of 2.5 millions trees from locations across the globe (location, species, diameter at breast height (DBH), height, vitality etc, where available)
- 27,288 images of trees that were uploaded onto the platform by our community and linked to individual trees and their species information etc.
- Notes (stories), tags and conversations linked to trees.
### Dataset Description
Curio was an environmental education and outreach platform that was predominantly focused on urban forestry. It connected the various stakeholders involved in the management of urban forestry with the public and importantly made all data uploaded via its web and mobile apps publicly available. The platform was live from March 2016 until August 2023 when the maintainence overheads made its ongoing availability infeasible. Curio was supported in its early stages by two European Space Agency projects, through the [New Commons](https://business.esa.int/projects/new-commons) and [Curio Canopy](https://business.esa.int/projects/curio-canopy). A sense of the platform and how it worked can be found via the videos on its supporting [youtube channel](https://www.youtube.com/@curio-xyz7991/videos)
This repository contains much of the tree inventory, images and stories data that was collected on the platform via our community, projects we helped support and open data tree inventories we uploaded onto the platform. We are keen to make this data available for research purposes in the hope it might be of benefit to others and to further the efforts of our community.
We have endeavored to name as many of those great projects and data sources that were hosted on the Curio platform in the attribution section below. If there are any omissions or errors please contact us.
A related project involved generating a high resolution map of tree canopy cover for the Greater London Authority. Details of that project and dataset can be found on the [London Datastore Curio Canopy page](https://data.london.gov.uk/dataset/curio-canopy).
- **Curated by:** Breadboard Labs
- **License:** cc-by-nc-4.0
### Dataset Sources and Attribution
Many people picked up the app and contributed to the data that was collected. Curio was also used to support many great projects and initiatives. We have endeavoured to mention many of those projects below along with the open data tree inventories we uploaded onto the platform.
#### Collaborative projects supported by Curio
- [Morton Arboretum](https://mortonarb.org/) - [Chicago Regional Tree Initiative](https://chicagorti.org/programs/)
- [Dublin City Council’s Parks, Biodiversity and Landscape Services](https://www.dublincity.ie/residential/parks) & [School of Geography at University College Dublin](https://www.ucd.ie/geography) - [Tree Mapping Dublin](https://mappinggreendublin.com/)
- [Sacramento Tree Foundation](https://sactree.org/) - [Save the Elms Program](https://sactree.org/programs/monitoring-elms/)
- [Cambridge City Council](https://www.cambridge.gov.uk/) - [Cambridge City Canopy Programme](https://www.cambridge.gov.uk/cambridge-canopy-project)
- [Municipality of Oslo Agency for Urban Environment](https://www.visitoslo.com/en/product/?tlp=593685) - Inventory and ecosystem services report hosting
- [Friends of Brunswick Park](http://www.friendsofbrunswickpark.co.uk/)
- [Exeter Trees](www.exetertrees.uk)
- [Wembley Park Limited](https://wembleypark.com/)
- [Washington Square Park Eco Projects](https://www.wspecoprojects.org/)
- [Coláiste Bríde Enniscorthy](https://www.colaistebride.ie/)
- [Enniscorthy Vocational College](https://www.enniscorthycc.ie/)
- [Mountshannon Arboretum](https://www.mountshannonarboretum.com/) - Forester Bernard Carey initiated the Mountshannon i-Tree project, in conjunction with UCD and UK-based consultancy Treeconomics.
- [Sidmouth Arboretum](http://sidmoutharboretum.org.uk/)
- [East Devon District Council](https://eastdevon.gov.uk/)
- [SLU](https://www.slu.se/en/) - Alnarp - Skåne Tree Inventory and support for and involvement in the New Commons and Curio Canopy projects
- [Malmö Stad](https://malmo.se/) - Malmö Tree Inventory and support for and involvement in the New Commons and Curio Canopy projects
- [Göteborgs Stad](https://goteborg.se/) -
- [Halmstad](https://www.halmstad.se/)
- [Hvilan](https://www.hvilanutbildning.se/)
- [Familjebostader](https://familjebostader.com/om-oss/)
#### Open Data Sources Attribution
- The Greater London Authority Datastore - [Local Authority Maintained Trees](https://data.london.gov.uk/dataset/local-authority-maintained-trees)
- NYC OpenData - [2015 Street Tree Census - Tree Data](https://data.cityofnewyork.us/Environment/2015-Street-Tree-Census-Tree-Data/uvpi-gqnh)
- Open Data BDN - [Street trees of the city of Barcelona](https://opendata-ajuntament.barcelona.cat/data/dataset/arbrat-viari)
- Open Data Bristol - [Trees](https://opendata.bristol.gov.uk/datasets/7a99218a4bf347ff948f0e5882406a8c)
- Open Data NI - [Belfast City Trees](https://admin.opendatani.gov.uk/dataset/belfast-trees)
- Denver Open data - [Tree Inventory](https://denvergov.org/opendata/dataset/city-and-county-of-denver-tree-inventory)
- Open Data DK - [City of Copenhagen Trees](https://www.opendata.dk/city-of-copenhagen/trae-basis-kommunale-traeer)
- Palo Alto Open Data - [Palo Alto Trees](https://data.cityofpaloalto.org/dataviews/73226/palo-alto-trees/)
- Fingal County Council Open Data - [Fingal County Council Trees](https://data.fingal.ie/maps/1e5f9db62e53443d946c15a1a06fd98b_0/explore)
- Data SA - [City of Adelaide Street Trees](https://data.sa.gov.au/data/dataset/street-trees)
- Open Data Boulder Colorado - [Tree Inventory Open Data](https://open-data.bouldercolorado.gov/datasets/dbbae8bdb0a44d17934243b88e85ef2b)
- Biodiversity Ireland - [Hertitage Trees Ireland](https://maps.biodiversityireland.ie/Dataset/27)
- Birmingham City Council Trees
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
The data is free to be used for research purposes subject to the cc-by-nc-4.0 licence and suitable attribution, please see the citation section below
Some potential uses might include;
- Investigations into urban tree biodiversity.
- The development of algorithms for extracting tree attributes via photos or streetview imagery.
- A tree species detection app.
- The detection trees of via satellite imagery.
- Species identfiication via hyperspectral tree.
It worth noting that for most use-cases cleaning, analysis and processing of data will be necessary. The completeness of tree inventory data varies greatly and users were not directed in anyway in terms of how to frame the photos they took and uploaded via the Curio app.
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
### TaggedTrees
Number of data points: 2,593,139
The details of an individual tree including its location, species, diameter at breast height (dbh), vitality etc. when available
### Images
Number of data points: 27,288
The details of images that were uploaded to the platform. The path to the actual image uploaded, this can be found in uploads directory. The details of what the image was attached to which usually was a ‘Story” that was then attached to a tree are also included.
### Uploads:
The set of images referenced in the images data file. The set of images was quite large even when zipped and so was broken up into 10gb chunks. Download each of the chunks and then run unzip on the uploads.zip file
A folder containing downsized versions of the images based on a fixed width has also been included - resized-uploads-width1200.zip
### Stories:
The details of a story that was attached to tree
### Notes:
The text included in a story/note about a tree.
### Conversations & Comments:
Comments grouped by conversations linked to a particular Story
### TreeSpecies
The tree species dictionary we built to support the platform. Each TaggedTree has a tree_species_id that references an entry in this dictionary when populated.
### TreeSpeciesAliases
The local names across multiple languages that can used to describe a species of tree contained in the TreeSpecies dictionary
### Tags and Taggings
Trees could be tagged with details such as diseased, monitored, newly_planted, apples, overhead cables etc. Anything at all really that could later be used to filter, group or identify trees of interest as well describe their state.
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
The goal of the Curio platform was to educate, engage and democratised access to environmenatal information. Making the data collected on the platform available in this form is seen as an extension of that mission.
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
All data was collected via the Curio app by its community. Where inventory data was uploaded in bulk we preprocessed the data to ensure details such as species information where mapped to the species dictionary we deinfed and that has been included in this release.
Before making the data available on this platform we decided to run face detection and blur any obvious, detectable faces found in the images that have been included.
<!-- #### Who are the source data producers? -->
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
<!-- #### Personal and Sensitive Information -->
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
<!-- ## Bias, Risks, and Limitations -->
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
@misc{CurioTreeData,
title = {The Curio Tree Dataset},
author = {Conor Nugent and Paul Hickey},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/datasets/BreadboardLabs/CurioTreeData}},
}
## Dataset Card Authors
Conor Nugent and Paul Hickey
## Dataset Card Contact
[Conor Nugent](https://www.linkedin.com/in/conor-nugent-5b02458/?originalSubdomain=ie) | BreadboardLabs/CurioTreeData | [
"size_categories:1M<n<10M",
"license:cc-by-nc-4.0",
"climate",
"trees",
"images",
"region:us"
]
| 2023-11-14T20:37:05+00:00 | {"license": "cc-by-nc-4.0", "size_categories": ["1M<n<10M"], "tags": ["climate", "trees", "images"]} | 2023-11-28T20:46:29+00:00 | []
| []
| TAGS
#size_categories-1M<n<10M #license-cc-by-nc-4.0 #climate #trees #images #region-us
|
# The Curio Tree Dataset
This dataset contains much of the tree inventory, images and stories data that was collected on the Curio platform before it was sunset. The data was extraced from a number of database tables and includes;
- The inventory details of 2.5 millions trees from locations across the globe (location, species, diameter at breast height (DBH), height, vitality etc, where available)
- 27,288 images of trees that were uploaded onto the platform by our community and linked to individual trees and their species information etc.
- Notes (stories), tags and conversations linked to trees.
### Dataset Description
Curio was an environmental education and outreach platform that was predominantly focused on urban forestry. It connected the various stakeholders involved in the management of urban forestry with the public and importantly made all data uploaded via its web and mobile apps publicly available. The platform was live from March 2016 until August 2023 when the maintainence overheads made its ongoing availability infeasible. Curio was supported in its early stages by two European Space Agency projects, through the New Commons and Curio Canopy. A sense of the platform and how it worked can be found via the videos on its supporting youtube channel
This repository contains much of the tree inventory, images and stories data that was collected on the platform via our community, projects we helped support and open data tree inventories we uploaded onto the platform. We are keen to make this data available for research purposes in the hope it might be of benefit to others and to further the efforts of our community.
We have endeavored to name as many of those great projects and data sources that were hosted on the Curio platform in the attribution section below. If there are any omissions or errors please contact us.
A related project involved generating a high resolution map of tree canopy cover for the Greater London Authority. Details of that project and dataset can be found on the London Datastore Curio Canopy page.
- Curated by: Breadboard Labs
- License: cc-by-nc-4.0
### Dataset Sources and Attribution
Many people picked up the app and contributed to the data that was collected. Curio was also used to support many great projects and initiatives. We have endeavoured to mention many of those projects below along with the open data tree inventories we uploaded onto the platform.
#### Collaborative projects supported by Curio
- Morton Arboretum - Chicago Regional Tree Initiative
- Dublin City Council’s Parks, Biodiversity and Landscape Services & School of Geography at University College Dublin - Tree Mapping Dublin
- Sacramento Tree Foundation - Save the Elms Program
- Cambridge City Council - Cambridge City Canopy Programme
- Municipality of Oslo Agency for Urban Environment - Inventory and ecosystem services report hosting
- Friends of Brunswick Park
- Exeter Trees
- Wembley Park Limited
- Washington Square Park Eco Projects
- Coláiste Bríde Enniscorthy
- Enniscorthy Vocational College
- Mountshannon Arboretum - Forester Bernard Carey initiated the Mountshannon i-Tree project, in conjunction with UCD and UK-based consultancy Treeconomics.
- Sidmouth Arboretum
- East Devon District Council
- SLU - Alnarp - Skåne Tree Inventory and support for and involvement in the New Commons and Curio Canopy projects
- Malmö Stad - Malmö Tree Inventory and support for and involvement in the New Commons and Curio Canopy projects
- Göteborgs Stad -
- Halmstad
- Hvilan
- Familjebostader
#### Open Data Sources Attribution
- The Greater London Authority Datastore - Local Authority Maintained Trees
- NYC OpenData - 2015 Street Tree Census - Tree Data
- Open Data BDN - Street trees of the city of Barcelona
- Open Data Bristol - Trees
- Open Data NI - Belfast City Trees
- Denver Open data - Tree Inventory
- Open Data DK - City of Copenhagen Trees
- Palo Alto Open Data - Palo Alto Trees
- Fingal County Council Open Data - Fingal County Council Trees
- Data SA - City of Adelaide Street Trees
- Open Data Boulder Colorado - Tree Inventory Open Data
- Biodiversity Ireland - Hertitage Trees Ireland
- Birmingham City Council Trees
## Uses
The data is free to be used for research purposes subject to the cc-by-nc-4.0 licence and suitable attribution, please see the citation section below
Some potential uses might include;
- Investigations into urban tree biodiversity.
- The development of algorithms for extracting tree attributes via photos or streetview imagery.
- A tree species detection app.
- The detection trees of via satellite imagery.
- Species identfiication via hyperspectral tree.
It worth noting that for most use-cases cleaning, analysis and processing of data will be necessary. The completeness of tree inventory data varies greatly and users were not directed in anyway in terms of how to frame the photos they took and uploaded via the Curio app.
## Dataset Structure
### TaggedTrees
Number of data points: 2,593,139
The details of an individual tree including its location, species, diameter at breast height (dbh), vitality etc. when available
### Images
Number of data points: 27,288
The details of images that were uploaded to the platform. The path to the actual image uploaded, this can be found in uploads directory. The details of what the image was attached to which usually was a ‘Story” that was then attached to a tree are also included.
### Uploads:
The set of images referenced in the images data file. The set of images was quite large even when zipped and so was broken up into 10gb chunks. Download each of the chunks and then run unzip on the URL file
A folder containing downsized versions of the images based on a fixed width has also been included - URL
### Stories:
The details of a story that was attached to tree
### Notes:
The text included in a story/note about a tree.
### Conversations & Comments:
Comments grouped by conversations linked to a particular Story
### TreeSpecies
The tree species dictionary we built to support the platform. Each TaggedTree has a tree_species_id that references an entry in this dictionary when populated.
### TreeSpeciesAliases
The local names across multiple languages that can used to describe a species of tree contained in the TreeSpecies dictionary
### Tags and Taggings
Trees could be tagged with details such as diseased, monitored, newly_planted, apples, overhead cables etc. Anything at all really that could later be used to filter, group or identify trees of interest as well describe their state.
## Dataset Creation
### Curation Rationale
The goal of the Curio platform was to educate, engage and democratised access to environmenatal information. Making the data collected on the platform available in this form is seen as an extension of that mission.
#### Data Collection and Processing
All data was collected via the Curio app by its community. Where inventory data was uploaded in bulk we preprocessed the data to ensure details such as species information where mapped to the species dictionary we deinfed and that has been included in this release.
Before making the data available on this platform we decided to run face detection and blur any obvious, detectable faces found in the images that have been included.
[optional]
@misc{CurioTreeData,
title = {The Curio Tree Dataset},
author = {Conor Nugent and Paul Hickey},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://URL
}
## Dataset Card Authors
Conor Nugent and Paul Hickey
## Dataset Card Contact
Conor Nugent | [
"# The Curio Tree Dataset\n\nThis dataset contains much of the tree inventory, images and stories data that was collected on the Curio platform before it was sunset. The data was extraced from a number of database tables and includes;\n\n- The inventory details of 2.5 millions trees from locations across the globe (location, species, diameter at breast height (DBH), height, vitality etc, where available)\n- 27,288 images of trees that were uploaded onto the platform by our community and linked to individual trees and their species information etc.\n- Notes (stories), tags and conversations linked to trees.",
"### Dataset Description\n\nCurio was an environmental education and outreach platform that was predominantly focused on urban forestry. It connected the various stakeholders involved in the management of urban forestry with the public and importantly made all data uploaded via its web and mobile apps publicly available. The platform was live from March 2016 until August 2023 when the maintainence overheads made its ongoing availability infeasible. Curio was supported in its early stages by two European Space Agency projects, through the New Commons and Curio Canopy. A sense of the platform and how it worked can be found via the videos on its supporting youtube channel\n\nThis repository contains much of the tree inventory, images and stories data that was collected on the platform via our community, projects we helped support and open data tree inventories we uploaded onto the platform. We are keen to make this data available for research purposes in the hope it might be of benefit to others and to further the efforts of our community. \n\nWe have endeavored to name as many of those great projects and data sources that were hosted on the Curio platform in the attribution section below. If there are any omissions or errors please contact us. \n\nA related project involved generating a high resolution map of tree canopy cover for the Greater London Authority. Details of that project and dataset can be found on the London Datastore Curio Canopy page. \n\n\n- Curated by: Breadboard Labs\n- License: cc-by-nc-4.0",
"### Dataset Sources and Attribution\n\nMany people picked up the app and contributed to the data that was collected. Curio was also used to support many great projects and initiatives. We have endeavoured to mention many of those projects below along with the open data tree inventories we uploaded onto the platform.",
"#### Collaborative projects supported by Curio\n\n- Morton Arboretum - Chicago Regional Tree Initiative \n\n- Dublin City Council’s Parks, Biodiversity and Landscape Services & School of Geography at University College Dublin - Tree Mapping Dublin\n\n- Sacramento Tree Foundation - Save the Elms Program\n\n- Cambridge City Council - Cambridge City Canopy Programme\n\n- Municipality of Oslo Agency for Urban Environment - Inventory and ecosystem services report hosting\n\n- Friends of Brunswick Park\n\n- Exeter Trees\n\n- Wembley Park Limited\n\n- Washington Square Park Eco Projects\n\n- Coláiste Bríde Enniscorthy\n\n- Enniscorthy Vocational College\n\n- Mountshannon Arboretum - Forester Bernard Carey initiated the Mountshannon i-Tree project, in conjunction with UCD and UK-based consultancy Treeconomics.\n\n- Sidmouth Arboretum\n\n- East Devon District Council \n\n- SLU - Alnarp - Skåne Tree Inventory and support for and involvement in the New Commons and Curio Canopy projects\n\n- Malmö Stad - Malmö Tree Inventory and support for and involvement in the New Commons and Curio Canopy projects\n\n- Göteborgs Stad -\n\n- Halmstad\n\n- Hvilan\n\n- Familjebostader",
"#### Open Data Sources Attribution\n\n- The Greater London Authority Datastore - Local Authority Maintained Trees \n\n- NYC OpenData - 2015 Street Tree Census - Tree Data\n\n- Open Data BDN - Street trees of the city of Barcelona\n\n- Open Data Bristol - Trees\n\n- Open Data NI - Belfast City Trees\n\n- Denver Open data - Tree Inventory\n\n- Open Data DK - City of Copenhagen Trees\n\n- Palo Alto Open Data - Palo Alto Trees\n\n- Fingal County Council Open Data - Fingal County Council Trees\n\n- Data SA - City of Adelaide Street Trees\n\n- Open Data Boulder Colorado - Tree Inventory Open Data\n\n- Biodiversity Ireland - Hertitage Trees Ireland\n\n- Birmingham City Council Trees",
"## Uses\n\n\n\nThe data is free to be used for research purposes subject to the cc-by-nc-4.0 licence and suitable attribution, please see the citation section below\n\nSome potential uses might include;\n\n- Investigations into urban tree biodiversity.\n- The development of algorithms for extracting tree attributes via photos or streetview imagery.\n- A tree species detection app.\n- The detection trees of via satellite imagery.\n- Species identfiication via hyperspectral tree.\n\n It worth noting that for most use-cases cleaning, analysis and processing of data will be necessary. The completeness of tree inventory data varies greatly and users were not directed in anyway in terms of how to frame the photos they took and uploaded via the Curio app.",
"## Dataset Structure",
"### TaggedTrees\n\nNumber of data points: 2,593,139\n\nThe details of an individual tree including its location, species, diameter at breast height (dbh), vitality etc. when available",
"### Images\n\nNumber of data points: 27,288\n\nThe details of images that were uploaded to the platform. The path to the actual image uploaded, this can be found in uploads directory. The details of what the image was attached to which usually was a ‘Story” that was then attached to a tree are also included.",
"### Uploads:\n\nThe set of images referenced in the images data file. The set of images was quite large even when zipped and so was broken up into 10gb chunks. Download each of the chunks and then run unzip on the URL file\n\nA folder containing downsized versions of the images based on a fixed width has also been included - URL",
"### Stories:\n\nThe details of a story that was attached to tree",
"### Notes:\n\nThe text included in a story/note about a tree.",
"### Conversations & Comments:\n\nComments grouped by conversations linked to a particular Story",
"### TreeSpecies\n\nThe tree species dictionary we built to support the platform. Each TaggedTree has a tree_species_id that references an entry in this dictionary when populated.",
"### TreeSpeciesAliases\n\nThe local names across multiple languages that can used to describe a species of tree contained in the TreeSpecies dictionary",
"### Tags and Taggings\n\nTrees could be tagged with details such as diseased, monitored, newly_planted, apples, overhead cables etc. Anything at all really that could later be used to filter, group or identify trees of interest as well describe their state.",
"## Dataset Creation",
"### Curation Rationale\n\n\n\nThe goal of the Curio platform was to educate, engage and democratised access to environmenatal information. Making the data collected on the platform available in this form is seen as an extension of that mission.",
"#### Data Collection and Processing\n\n\n\nAll data was collected via the Curio app by its community. Where inventory data was uploaded in bulk we preprocessed the data to ensure details such as species information where mapped to the species dictionary we deinfed and that has been included in this release.\n\n\nBefore making the data available on this platform we decided to run face detection and blur any obvious, detectable faces found in the images that have been included. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n[optional]\n\n\n\n@misc{CurioTreeData,\n title = {The Curio Tree Dataset},\n author = {Conor Nugent and Paul Hickey},\n year = {2023},\n publisher = {HuggingFace},\n journal = {HuggingFace repository},\n howpublished = {\\url{https://URL\n}",
"## Dataset Card Authors\n\nConor Nugent and Paul Hickey",
"## Dataset Card Contact\n\nConor Nugent"
]
| [
"TAGS\n#size_categories-1M<n<10M #license-cc-by-nc-4.0 #climate #trees #images #region-us \n",
"# The Curio Tree Dataset\n\nThis dataset contains much of the tree inventory, images and stories data that was collected on the Curio platform before it was sunset. The data was extraced from a number of database tables and includes;\n\n- The inventory details of 2.5 millions trees from locations across the globe (location, species, diameter at breast height (DBH), height, vitality etc, where available)\n- 27,288 images of trees that were uploaded onto the platform by our community and linked to individual trees and their species information etc.\n- Notes (stories), tags and conversations linked to trees.",
"### Dataset Description\n\nCurio was an environmental education and outreach platform that was predominantly focused on urban forestry. It connected the various stakeholders involved in the management of urban forestry with the public and importantly made all data uploaded via its web and mobile apps publicly available. The platform was live from March 2016 until August 2023 when the maintainence overheads made its ongoing availability infeasible. Curio was supported in its early stages by two European Space Agency projects, through the New Commons and Curio Canopy. A sense of the platform and how it worked can be found via the videos on its supporting youtube channel\n\nThis repository contains much of the tree inventory, images and stories data that was collected on the platform via our community, projects we helped support and open data tree inventories we uploaded onto the platform. We are keen to make this data available for research purposes in the hope it might be of benefit to others and to further the efforts of our community. \n\nWe have endeavored to name as many of those great projects and data sources that were hosted on the Curio platform in the attribution section below. If there are any omissions or errors please contact us. \n\nA related project involved generating a high resolution map of tree canopy cover for the Greater London Authority. Details of that project and dataset can be found on the London Datastore Curio Canopy page. \n\n\n- Curated by: Breadboard Labs\n- License: cc-by-nc-4.0",
"### Dataset Sources and Attribution\n\nMany people picked up the app and contributed to the data that was collected. Curio was also used to support many great projects and initiatives. We have endeavoured to mention many of those projects below along with the open data tree inventories we uploaded onto the platform.",
"#### Collaborative projects supported by Curio\n\n- Morton Arboretum - Chicago Regional Tree Initiative \n\n- Dublin City Council’s Parks, Biodiversity and Landscape Services & School of Geography at University College Dublin - Tree Mapping Dublin\n\n- Sacramento Tree Foundation - Save the Elms Program\n\n- Cambridge City Council - Cambridge City Canopy Programme\n\n- Municipality of Oslo Agency for Urban Environment - Inventory and ecosystem services report hosting\n\n- Friends of Brunswick Park\n\n- Exeter Trees\n\n- Wembley Park Limited\n\n- Washington Square Park Eco Projects\n\n- Coláiste Bríde Enniscorthy\n\n- Enniscorthy Vocational College\n\n- Mountshannon Arboretum - Forester Bernard Carey initiated the Mountshannon i-Tree project, in conjunction with UCD and UK-based consultancy Treeconomics.\n\n- Sidmouth Arboretum\n\n- East Devon District Council \n\n- SLU - Alnarp - Skåne Tree Inventory and support for and involvement in the New Commons and Curio Canopy projects\n\n- Malmö Stad - Malmö Tree Inventory and support for and involvement in the New Commons and Curio Canopy projects\n\n- Göteborgs Stad -\n\n- Halmstad\n\n- Hvilan\n\n- Familjebostader",
"#### Open Data Sources Attribution\n\n- The Greater London Authority Datastore - Local Authority Maintained Trees \n\n- NYC OpenData - 2015 Street Tree Census - Tree Data\n\n- Open Data BDN - Street trees of the city of Barcelona\n\n- Open Data Bristol - Trees\n\n- Open Data NI - Belfast City Trees\n\n- Denver Open data - Tree Inventory\n\n- Open Data DK - City of Copenhagen Trees\n\n- Palo Alto Open Data - Palo Alto Trees\n\n- Fingal County Council Open Data - Fingal County Council Trees\n\n- Data SA - City of Adelaide Street Trees\n\n- Open Data Boulder Colorado - Tree Inventory Open Data\n\n- Biodiversity Ireland - Hertitage Trees Ireland\n\n- Birmingham City Council Trees",
"## Uses\n\n\n\nThe data is free to be used for research purposes subject to the cc-by-nc-4.0 licence and suitable attribution, please see the citation section below\n\nSome potential uses might include;\n\n- Investigations into urban tree biodiversity.\n- The development of algorithms for extracting tree attributes via photos or streetview imagery.\n- A tree species detection app.\n- The detection trees of via satellite imagery.\n- Species identfiication via hyperspectral tree.\n\n It worth noting that for most use-cases cleaning, analysis and processing of data will be necessary. The completeness of tree inventory data varies greatly and users were not directed in anyway in terms of how to frame the photos they took and uploaded via the Curio app.",
"## Dataset Structure",
"### TaggedTrees\n\nNumber of data points: 2,593,139\n\nThe details of an individual tree including its location, species, diameter at breast height (dbh), vitality etc. when available",
"### Images\n\nNumber of data points: 27,288\n\nThe details of images that were uploaded to the platform. The path to the actual image uploaded, this can be found in uploads directory. The details of what the image was attached to which usually was a ‘Story” that was then attached to a tree are also included.",
"### Uploads:\n\nThe set of images referenced in the images data file. The set of images was quite large even when zipped and so was broken up into 10gb chunks. Download each of the chunks and then run unzip on the URL file\n\nA folder containing downsized versions of the images based on a fixed width has also been included - URL",
"### Stories:\n\nThe details of a story that was attached to tree",
"### Notes:\n\nThe text included in a story/note about a tree.",
"### Conversations & Comments:\n\nComments grouped by conversations linked to a particular Story",
"### TreeSpecies\n\nThe tree species dictionary we built to support the platform. Each TaggedTree has a tree_species_id that references an entry in this dictionary when populated.",
"### TreeSpeciesAliases\n\nThe local names across multiple languages that can used to describe a species of tree contained in the TreeSpecies dictionary",
"### Tags and Taggings\n\nTrees could be tagged with details such as diseased, monitored, newly_planted, apples, overhead cables etc. Anything at all really that could later be used to filter, group or identify trees of interest as well describe their state.",
"## Dataset Creation",
"### Curation Rationale\n\n\n\nThe goal of the Curio platform was to educate, engage and democratised access to environmenatal information. Making the data collected on the platform available in this form is seen as an extension of that mission.",
"#### Data Collection and Processing\n\n\n\nAll data was collected via the Curio app by its community. Where inventory data was uploaded in bulk we preprocessed the data to ensure details such as species information where mapped to the species dictionary we deinfed and that has been included in this release.\n\n\nBefore making the data available on this platform we decided to run face detection and blur any obvious, detectable faces found in the images that have been included. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n[optional]\n\n\n\n@misc{CurioTreeData,\n title = {The Curio Tree Dataset},\n author = {Conor Nugent and Paul Hickey},\n year = {2023},\n publisher = {HuggingFace},\n journal = {HuggingFace repository},\n howpublished = {\\url{https://URL\n}",
"## Dataset Card Authors\n\nConor Nugent and Paul Hickey",
"## Dataset Card Contact\n\nConor Nugent"
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"passage: TAGS\n#size_categories-1M<n<10M #license-cc-by-nc-4.0 #climate #trees #images #region-us \n# The Curio Tree Dataset\n\nThis dataset contains much of the tree inventory, images and stories data that was collected on the Curio platform before it was sunset. The data was extraced from a number of database tables and includes;\n\n- The inventory details of 2.5 millions trees from locations across the globe (location, species, diameter at breast height (DBH), height, vitality etc, where available)\n- 27,288 images of trees that were uploaded onto the platform by our community and linked to individual trees and their species information etc.\n- Notes (stories), tags and conversations linked to trees.",
"passage: ### Dataset Description\n\nCurio was an environmental education and outreach platform that was predominantly focused on urban forestry. It connected the various stakeholders involved in the management of urban forestry with the public and importantly made all data uploaded via its web and mobile apps publicly available. The platform was live from March 2016 until August 2023 when the maintainence overheads made its ongoing availability infeasible. Curio was supported in its early stages by two European Space Agency projects, through the New Commons and Curio Canopy. A sense of the platform and how it worked can be found via the videos on its supporting youtube channel\n\nThis repository contains much of the tree inventory, images and stories data that was collected on the platform via our community, projects we helped support and open data tree inventories we uploaded onto the platform. We are keen to make this data available for research purposes in the hope it might be of benefit to others and to further the efforts of our community. \n\nWe have endeavored to name as many of those great projects and data sources that were hosted on the Curio platform in the attribution section below. If there are any omissions or errors please contact us. \n\nA related project involved generating a high resolution map of tree canopy cover for the Greater London Authority. Details of that project and dataset can be found on the London Datastore Curio Canopy page. \n\n\n- Curated by: Breadboard Labs\n- License: cc-by-nc-4.0### Dataset Sources and Attribution\n\nMany people picked up the app and contributed to the data that was collected. Curio was also used to support many great projects and initiatives. We have endeavoured to mention many of those projects below along with the open data tree inventories we uploaded onto the platform.#### Collaborative projects supported by Curio\n\n- Morton Arboretum - Chicago Regional Tree Initiative \n\n- Dublin City Council’s Parks, Biodiversity and Landscape Services & School of Geography at University College Dublin - Tree Mapping Dublin\n\n- Sacramento Tree Foundation - Save the Elms Program\n\n- Cambridge City Council - Cambridge City Canopy Programme\n\n- Municipality of Oslo Agency for Urban Environment - Inventory and ecosystem services report hosting\n\n- Friends of Brunswick Park\n\n- Exeter Trees\n\n- Wembley Park Limited\n\n- Washington Square Park Eco Projects\n\n- Coláiste Bríde Enniscorthy\n\n- Enniscorthy Vocational College\n\n- Mountshannon Arboretum - Forester Bernard Carey initiated the Mountshannon i-Tree project, in conjunction with UCD and UK-based consultancy Treeconomics.\n\n- Sidmouth Arboretum\n\n- East Devon District Council \n\n- SLU - Alnarp - Skåne Tree Inventory and support for and involvement in the New Commons and Curio Canopy projects\n\n- Malmö Stad - Malmö Tree Inventory and support for and involvement in the New Commons and Curio Canopy projects\n\n- Göteborgs Stad -\n\n- Halmstad\n\n- Hvilan\n\n- Familjebostader#### Open Data Sources Attribution\n\n- The Greater London Authority Datastore - Local Authority Maintained Trees \n\n- NYC OpenData - 2015 Street Tree Census - Tree Data\n\n- Open Data BDN - Street trees of the city of Barcelona\n\n- Open Data Bristol - Trees\n\n- Open Data NI - Belfast City Trees\n\n- Denver Open data - Tree Inventory\n\n- Open Data DK - City of Copenhagen Trees\n\n- Palo Alto Open Data - Palo Alto Trees\n\n- Fingal County Council Open Data - Fingal County Council Trees\n\n- Data SA - City of Adelaide Street Trees\n\n- Open Data Boulder Colorado - Tree Inventory Open Data\n\n- Biodiversity Ireland - Hertitage Trees Ireland\n\n- Birmingham City Council Trees",
"passage: ## Uses\n\n\n\nThe data is free to be used for research purposes subject to the cc-by-nc-4.0 licence and suitable attribution, please see the citation section below\n\nSome potential uses might include;\n\n- Investigations into urban tree biodiversity.\n- The development of algorithms for extracting tree attributes via photos or streetview imagery.\n- A tree species detection app.\n- The detection trees of via satellite imagery.\n- Species identfiication via hyperspectral tree.\n\n It worth noting that for most use-cases cleaning, analysis and processing of data will be necessary. The completeness of tree inventory data varies greatly and users were not directed in anyway in terms of how to frame the photos they took and uploaded via the Curio app.## Dataset Structure### TaggedTrees\n\nNumber of data points: 2,593,139\n\nThe details of an individual tree including its location, species, diameter at breast height (dbh), vitality etc. when available### Images\n\nNumber of data points: 27,288\n\nThe details of images that were uploaded to the platform. The path to the actual image uploaded, this can be found in uploads directory. The details of what the image was attached to which usually was a ‘Story” that was then attached to a tree are also included.### Uploads:\n\nThe set of images referenced in the images data file. The set of images was quite large even when zipped and so was broken up into 10gb chunks. Download each of the chunks and then run unzip on the URL file\n\nA folder containing downsized versions of the images based on a fixed width has also been included - URL### Stories:\n\nThe details of a story that was attached to tree### Notes:\n\nThe text included in a story/note about a tree.### Conversations & Comments:\n\nComments grouped by conversations linked to a particular Story### TreeSpecies\n\nThe tree species dictionary we built to support the platform. Each TaggedTree has a tree_species_id that references an entry in this dictionary when populated.### TreeSpeciesAliases\n\nThe local names across multiple languages that can used to describe a species of tree contained in the TreeSpecies dictionary### Tags and Taggings\n\nTrees could be tagged with details such as diseased, monitored, newly_planted, apples, overhead cables etc. Anything at all really that could later be used to filter, group or identify trees of interest as well describe their state.## Dataset Creation### Curation Rationale\n\n\n\nThe goal of the Curio platform was to educate, engage and democratised access to environmenatal information. Making the data collected on the platform available in this form is seen as an extension of that mission."
]
|
8d1078ff36c4ecc9c0b985ffd556e064eb38628a | # Dataset Card for "NQLongAnswersForCustomLEDForQA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ekolasky/NQLongAnswersForCustomLEDForQA | [
"region:us"
]
| 2023-11-14T20:39:16+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "start_positions", "sequence": "int64"}, {"name": "end_positions", "sequence": "int64"}, {"name": "global_attention_mask", "sequence": "int64"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 1000140417, "num_examples": 12323}, {"name": "validation", "num_bytes": 47307511, "num_examples": 588}], "download_size": 119671635, "dataset_size": 1047447928}} | 2023-11-15T04:21:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "NQLongAnswersForCustomLEDForQA"
More Information needed | [
"# Dataset Card for \"NQLongAnswersForCustomLEDForQA\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"NQLongAnswersForCustomLEDForQA\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"NQLongAnswersForCustomLEDForQA\"\n\nMore Information needed"
]
|
785d0aa426d70215a3a468ca647577a16872a8d5 | # Dataset Card for "tmp_imdb_ft"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 5cp/tmp_imdb_ft | [
"region:us"
]
| 2023-11-14T21:04:22+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 21120329, "num_examples": 782}, {"name": "test", "num_bytes": 23158476, "num_examples": 858}], "download_size": 736300, "dataset_size": 44278805}} | 2023-11-14T21:23:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tmp_imdb_ft"
More Information needed | [
"# Dataset Card for \"tmp_imdb_ft\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tmp_imdb_ft\"\n\nMore Information needed"
]
| [
6,
17
]
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"passage: TAGS\n#region-us \n# Dataset Card for \"tmp_imdb_ft\"\n\nMore Information needed"
]
|
07fd794e1c4bb027a69f3afbd09696651745b5b8 | # Dataset Card for "instruct_dataset_mcq"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Supersaiyan1729/instruct_dataset_mcq | [
"region:us"
]
| 2023-11-14T21:23:09+00:00 | {"dataset_info": {"features": [{"name": "input_prompt", "dtype": "string"}, {"name": "input_output_prompt", "dtype": "string"}, {"name": "dataset", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 69681099, "num_examples": 48302}], "download_size": 29758222, "dataset_size": 69681099}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-14T21:23:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instruct_dataset_mcq"
More Information needed | [
"# Dataset Card for \"instruct_dataset_mcq\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instruct_dataset_mcq\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instruct_dataset_mcq\"\n\nMore Information needed"
]
|
988d1db37424d08eb99c4096e781a0faf6a26fd5 | # Dataset Card for "medium-articles-en"
`fabiochiu/medium-articles` filtered for `en` only and 100 GPT-4 tiktoken tokens or more. | BEE-spoke-data/medium-articles-en | [
"task_categories:text-classification",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"source_datasets:fabiochiu/medium-articles",
"language:en",
"license:mit",
"region:us"
]
| 2023-11-14T21:26:15+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "source_datasets": "fabiochiu/medium-articles", "task_categories": ["text-classification", "text-generation"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "authors", "dtype": "string"}, {"name": "timestamp", "dtype": "string"}, {"name": "tags", "dtype": "string"}, {"name": "token_count", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 930797692.9172074, "num_examples": 171340}, {"name": "validation", "num_bytes": 24494962.048346493, "num_examples": 4509}, {"name": "test", "num_bytes": 24494962.048346493, "num_examples": 4509}], "download_size": 615394671, "dataset_size": 979787617.0139004}} | 2023-11-14T21:36:02+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-text-generation #size_categories-100K<n<1M #source_datasets-fabiochiu/medium-articles #language-English #license-mit #region-us
| # Dataset Card for "medium-articles-en"
'fabiochiu/medium-articles' filtered for 'en' only and 100 GPT-4 tiktoken tokens or more. | [
"# Dataset Card for \"medium-articles-en\"\n\n\n'fabiochiu/medium-articles' filtered for 'en' only and 100 GPT-4 tiktoken tokens or more."
]
| [
"TAGS\n#task_categories-text-classification #task_categories-text-generation #size_categories-100K<n<1M #source_datasets-fabiochiu/medium-articles #language-English #license-mit #region-us \n",
"# Dataset Card for \"medium-articles-en\"\n\n\n'fabiochiu/medium-articles' filtered for 'en' only and 100 GPT-4 tiktoken tokens or more."
]
| [
66,
46
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-text-generation #size_categories-100K<n<1M #source_datasets-fabiochiu/medium-articles #language-English #license-mit #region-us \n# Dataset Card for \"medium-articles-en\"\n\n\n'fabiochiu/medium-articles' filtered for 'en' only and 100 GPT-4 tiktoken tokens or more."
]
|
9c84c9b769300c51edf8f39db134e17b07add011 | # Dataset Card for "mrpc_llama_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | VitaliiVrublevskyi/mrpc_llama_2 | [
"region:us"
]
| 2023-11-14T21:27:37+00:00 | {"dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6571266, "num_examples": 27739}, {"name": "validation", "num_bytes": 109143, "num_examples": 408}, {"name": "test", "num_bytes": 456210, "num_examples": 1725}], "download_size": 1534204, "dataset_size": 7136619}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2023-11-14T21:27:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mrpc_llama_2"
More Information needed | [
"# Dataset Card for \"mrpc_llama_2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mrpc_llama_2\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mrpc_llama_2\"\n\nMore Information needed"
]
|
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