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eefdda4d2e12c44d378985f482d49c382741f17d | # Dataset Card for "miniwob_snippets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LucasThil/miniwob_snippets | [
"region:us"
]
| 2023-02-22T14:47:30+00:00 | {"dataset_info": {"features": [{"name": "episodes", "dtype": "string"}, {"name": "refs", "dtype": "int64"}, {"name": "click", "dtype": "int64"}, {"name": "dblclick", "dtype": "int64"}, {"name": "keydown", "dtype": "int64"}, {"name": "keypress", "dtype": "int64"}, {"name": "keyup", "dtype": "int64"}, {"name": "mousedown", "dtype": "int64"}, {"name": "mouseup", "dtype": "int64"}, {"name": "scroll", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 674906155, "num_examples": 587268}, {"name": "test", "num_bytes": 84528980, "num_examples": 73420}, {"name": "validate", "num_bytes": 84695448, "num_examples": 73318}], "download_size": 140471741, "dataset_size": 844130583}} | 2023-02-22T14:48:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "miniwob_snippets"
More Information needed | [
"# Dataset Card for \"miniwob_snippets\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"miniwob_snippets\"\n\nMore Information needed"
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e66078af2f3f16a3fd9cd658712472264aca7dda | # Dataset Card for "patched_test_p_40_f_SPOUT_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_40_f_SPOUT_m1_predictions | [
"region:us"
]
| 2023-02-22T15:14:36+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 484629878, "num_examples": 1470999}], "download_size": 49491513, "dataset_size": 484629878}} | 2023-02-22T15:14:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_40_f_SPOUT_m1_predictions"
More Information needed | [
"# Dataset Card for \"patched_test_p_40_f_SPOUT_m1_predictions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_40_f_SPOUT_m1_predictions\"\n\nMore Information needed"
]
| [
6,
30
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_40_f_SPOUT_m1_predictions\"\n\nMore Information needed"
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## Dataset Description
- **Homepage:https://huggingface.co/datasets/jjiiaa/mj-prompts/
- **Repository:https://huggingface.co/datasets/jjiiaa/mj-prompts/
### Dataset Summary
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## Dataset Structure
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|
3a9c4576cbc5fe65a49f115e7e0bd87b647d31c1 | # Dataset Card for "patched_test_p_10_f_ATCaseOTCase_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_10_f_ATCaseOTCase_m1_predictions | [
"region:us"
]
| 2023-02-22T15:39:25+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 53142996, "num_examples": 143667}], "download_size": 5106512, "dataset_size": 53142996}} | 2023-02-22T15:39:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_10_f_ATCaseOTCase_m1_predictions"
More Information needed | [
"# Dataset Card for \"patched_test_p_10_f_ATCaseOTCase_m1_predictions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_10_f_ATCaseOTCase_m1_predictions\"\n\nMore Information needed"
]
| [
6,
34
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_10_f_ATCaseOTCase_m1_predictions\"\n\nMore Information needed"
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092d9f4a5b013b5714b30e4866f218f623ad07c2 | # Dataset Card for "patched_test_p_20_f_ATCaseOTCase_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_20_f_ATCaseOTCase_m1_predictions | [
"region:us"
]
| 2023-02-22T15:59:09+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 51507146, "num_examples": 139207}], "download_size": 4945248, "dataset_size": 51507146}} | 2023-02-22T15:59:15+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_20_f_ATCaseOTCase_m1_predictions"
More Information needed | [
"# Dataset Card for \"patched_test_p_20_f_ATCaseOTCase_m1_predictions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_20_f_ATCaseOTCase_m1_predictions\"\n\nMore Information needed"
]
| [
6,
34
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_20_f_ATCaseOTCase_m1_predictions\"\n\nMore Information needed"
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ab096821b26270a5167851e62cc818b206e1ff9b | # Dataset Card for "patched_test_p_40_f_ATCaseOTCase_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_40_f_ATCaseOTCase_m1_predictions | [
"region:us"
]
| 2023-02-22T16:17:43+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 48235460, "num_examples": 130287}], "download_size": 4618804, "dataset_size": 48235460}} | 2023-02-22T16:17:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_40_f_ATCaseOTCase_m1_predictions"
More Information needed | [
"# Dataset Card for \"patched_test_p_40_f_ATCaseOTCase_m1_predictions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_40_f_ATCaseOTCase_m1_predictions\"\n\nMore Information needed"
]
| [
6,
34
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_40_f_ATCaseOTCase_m1_predictions\"\n\nMore Information needed"
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|
6650abf12d61c4310d2b499a8ce3c78d6a1cef01 | This is a dataset collected from all the texts available at Corpus Corporum, which includes probably all the literary works ever written in Latin. The dataset is split in two parts: preprocessed with basic cltk tools, ready for work, and raw text data. It must be noted, however, that the latter contains text in Greek, Hebrew, and other languages, with references and contractions | Fece228/latin-literature-dataset-170M | [
"size_categories:100M<n<1B",
"language:la",
"text",
"linguistics",
"NLP",
"Latin",
"literature",
"region:us"
]
| 2023-02-22T17:36:43+00:00 | {"language": ["la"], "size_categories": ["100M<n<1B"], "tags": ["text", "linguistics", "NLP", "Latin", "literature"]} | 2023-02-23T09:05:13+00:00 | []
| [
"la"
]
| TAGS
#size_categories-100M<n<1B #language-Latin #text #linguistics #NLP #Latin #literature #region-us
| This is a dataset collected from all the texts available at Corpus Corporum, which includes probably all the literary works ever written in Latin. The dataset is split in two parts: preprocessed with basic cltk tools, ready for work, and raw text data. It must be noted, however, that the latter contains text in Greek, Hebrew, and other languages, with references and contractions | []
| [
"TAGS\n#size_categories-100M<n<1B #language-Latin #text #linguistics #NLP #Latin #literature #region-us \n"
]
| [
39
]
| [
"passage: TAGS\n#size_categories-100M<n<1B #language-Latin #text #linguistics #NLP #Latin #literature #region-us \n"
]
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|
6fae1b9da6a531dee81874424fee9dc37639ca34 | # Dataset Card for "mr_trial3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bhatvineet/mr_trial3 | [
"region:us"
]
| 2023-02-22T17:44:23+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcriptions", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 1009711800.042, "num_examples": 4179}, {"name": "test", "num_bytes": 359681461.83, "num_examples": 1393}], "download_size": 1379902601, "dataset_size": 1369393261.872}} | 2023-02-22T17:47:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mr_trial3"
More Information needed | [
"# Dataset Card for \"mr_trial3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mr_trial3\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mr_trial3\"\n\nMore Information needed"
]
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|
efd19dcce878ce3893afb1f30a830cb0d72fa9d5 | # Dataset Card for "fooset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roszcz/fooset | [
"region:us"
]
| 2023-02-22T18:50:26+00:00 | {"dataset_info": {"features": [{"name": "pitch", "sequence": "int64"}, {"name": "velocity", "sequence": "int64"}, {"name": "start", "sequence": "float64"}, {"name": "end", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 2814000, "num_examples": 875}], "download_size": 0, "dataset_size": 2814000}} | 2023-02-22T19:12:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "fooset"
More Information needed | [
"# Dataset Card for \"fooset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"fooset\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"fooset\"\n\nMore Information needed"
]
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|
74a3957c2d3397ceeb7c85db5fc53da00f46fb02 |
# Fork of [SirNeural/flan_v2](https://huggingface.co/datasets/SirNeural/flan_v2)
just in case it gets deleted.
# Dataset Card for Flan V2
## Dataset Description
- **Homepage:** https://ai.googleblog.com/2023/02/the-flan-collection-advancing-open.html
- **Repository:** https://github.com/google-research/FLAN/tree/main/flan/v2
- **Paper:** https://arxiv.org/abs/2301.13688
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a processed version of the Flan V2 dataset.
I'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.
The authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.
This current version I've processed is missing a few datasets compared to the main branch of the flan v2 repo:
- cs-en WMT translation task requires manual download and I wasn't able to get the credentials
- q_re_cc dataset preprocessing for the dialog task wasn't working
-
These are minor hits to the total size of the collection (orders of MB compared to GB) but once those are fixed I will upload a complete version.
## Dataset Structure
### Data Instances
Flan 2021 (flan), P3 (t0), Super-Natural Instructions (niv2), Chain-of-thought (cot), and Dialog (dialog)
### Data Fields
Instruction data comes in a few formats:
- Few Shot (fs)
- Zero Shot (zs)
- Options Provided in context (i.e. multiple choice pick one) (opt)
- No Options Provided (noopt)
Each combination of the above tasks + formats are saved as a JSONL with following schema `{"input": ..., "target": ..., "task": ...}`
### Data Splits
Everything is saved as a train split
| philschmid/flanv2 | [
"license:apache-2.0",
"flan",
"flan 2022",
"flan v2",
"arxiv:2301.13688",
"region:us"
]
| 2023-02-22T19:38:58+00:00 | {"license": "apache-2.0", "pretty_name": "Flan v2", "tags": ["flan", "flan 2022", "flan v2"], "duplicated_from": "SirNeural/flan_v2"} | 2023-02-22T19:39:49+00:00 | [
"2301.13688"
]
| []
| TAGS
#license-apache-2.0 #flan #flan 2022 #flan v2 #arxiv-2301.13688 #region-us
|
# Fork of SirNeural/flan_v2
just in case it gets deleted.
# Dataset Card for Flan V2
## Dataset Description
- Homepage: URL
- Repository: URL
- Paper: URL
- Leaderboard:
- Point of Contact:
### Dataset Summary
This is a processed version of the Flan V2 dataset.
I'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.
The authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.
This current version I've processed is missing a few datasets compared to the main branch of the flan v2 repo:
- cs-en WMT translation task requires manual download and I wasn't able to get the credentials
- q_re_cc dataset preprocessing for the dialog task wasn't working
-
These are minor hits to the total size of the collection (orders of MB compared to GB) but once those are fixed I will upload a complete version.
## Dataset Structure
### Data Instances
Flan 2021 (flan), P3 (t0), Super-Natural Instructions (niv2), Chain-of-thought (cot), and Dialog (dialog)
### Data Fields
Instruction data comes in a few formats:
- Few Shot (fs)
- Zero Shot (zs)
- Options Provided in context (i.e. multiple choice pick one) (opt)
- No Options Provided (noopt)
Each combination of the above tasks + formats are saved as a JSONL with following schema '{"input": ..., "target": ..., "task": ...}'
### Data Splits
Everything is saved as a train split
| [
"# Fork of SirNeural/flan_v2\n\njust in case it gets deleted.",
"# Dataset Card for Flan V2",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis is a processed version of the Flan V2 dataset.\n\nI'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.\n\nThe authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.\n\nThis current version I've processed is missing a few datasets compared to the main branch of the flan v2 repo:\n- cs-en WMT translation task requires manual download and I wasn't able to get the credentials\n- q_re_cc dataset preprocessing for the dialog task wasn't working\n- \nThese are minor hits to the total size of the collection (orders of MB compared to GB) but once those are fixed I will upload a complete version.",
"## Dataset Structure",
"### Data Instances\n\nFlan 2021 (flan), P3 (t0), Super-Natural Instructions (niv2), Chain-of-thought (cot), and Dialog (dialog)",
"### Data Fields\n\nInstruction data comes in a few formats:\n- Few Shot (fs)\n- Zero Shot (zs)\n- Options Provided in context (i.e. multiple choice pick one) (opt)\n- No Options Provided (noopt)\n\nEach combination of the above tasks + formats are saved as a JSONL with following schema '{\"input\": ..., \"target\": ..., \"task\": ...}'",
"### Data Splits\n\nEverything is saved as a train split"
]
| [
"TAGS\n#license-apache-2.0 #flan #flan 2022 #flan v2 #arxiv-2301.13688 #region-us \n",
"# Fork of SirNeural/flan_v2\n\njust in case it gets deleted.",
"# Dataset Card for Flan V2",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis is a processed version of the Flan V2 dataset.\n\nI'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.\n\nThe authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.\n\nThis current version I've processed is missing a few datasets compared to the main branch of the flan v2 repo:\n- cs-en WMT translation task requires manual download and I wasn't able to get the credentials\n- q_re_cc dataset preprocessing for the dialog task wasn't working\n- \nThese are minor hits to the total size of the collection (orders of MB compared to GB) but once those are fixed I will upload a complete version.",
"## Dataset Structure",
"### Data Instances\n\nFlan 2021 (flan), P3 (t0), Super-Natural Instructions (niv2), Chain-of-thought (cot), and Dialog (dialog)",
"### Data Fields\n\nInstruction data comes in a few formats:\n- Few Shot (fs)\n- Zero Shot (zs)\n- Options Provided in context (i.e. multiple choice pick one) (opt)\n- No Options Provided (noopt)\n\nEach combination of the above tasks + formats are saved as a JSONL with following schema '{\"input\": ..., \"target\": ..., \"task\": ...}'",
"### Data Splits\n\nEverything is saved as a train split"
]
| [
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| [
"passage: TAGS\n#license-apache-2.0 #flan #flan 2022 #flan v2 #arxiv-2301.13688 #region-us \n# Fork of SirNeural/flan_v2\n\njust in case it gets deleted.# Dataset Card for Flan V2## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis is a processed version of the Flan V2 dataset.\n\nI'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.\n\nThe authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.\n\nThis current version I've processed is missing a few datasets compared to the main branch of the flan v2 repo:\n- cs-en WMT translation task requires manual download and I wasn't able to get the credentials\n- q_re_cc dataset preprocessing for the dialog task wasn't working\n- \nThese are minor hits to the total size of the collection (orders of MB compared to GB) but once those are fixed I will upload a complete version.## Dataset Structure### Data Instances\n\nFlan 2021 (flan), P3 (t0), Super-Natural Instructions (niv2), Chain-of-thought (cot), and Dialog (dialog)### Data Fields\n\nInstruction data comes in a few formats:\n- Few Shot (fs)\n- Zero Shot (zs)\n- Options Provided in context (i.e. multiple choice pick one) (opt)\n- No Options Provided (noopt)\n\nEach combination of the above tasks + formats are saved as a JSONL with following schema '{\"input\": ..., \"target\": ..., \"task\": ...}'### Data Splits\n\nEverything is saved as a train split"
]
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|
f86ef92911c4a65f0d1fbaf0b8b329d4d9f2e1b0 | # Dataset Card for "zambezivoice_lox_aug_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | zambezivoice/zambezivoice_lox_aug_text | [
"region:us"
]
| 2023-02-22T20:36:36+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 582481, "num_examples": 10397}], "download_size": 345760, "dataset_size": 582481}} | 2023-02-22T20:36:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "zambezivoice_lox_aug_text"
More Information needed | [
"# Dataset Card for \"zambezivoice_lox_aug_text\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"zambezivoice_lox_aug_text\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"zambezivoice_lox_aug_text\"\n\nMore Information needed"
]
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|
e8965d28bde931c77eb93e2ade7f0dba3c305b43 | # Dataset Card for "zambezivoice_loz_aug_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | zambezivoice/zambezivoice_loz_aug_text | [
"region:us"
]
| 2023-02-22T21:06:14+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 582481, "num_examples": 10397}], "download_size": 345760, "dataset_size": 582481}} | 2023-02-22T21:06:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "zambezivoice_loz_aug_text"
More Information needed | [
"# Dataset Card for \"zambezivoice_loz_aug_text\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"zambezivoice_loz_aug_text\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"zambezivoice_loz_aug_text\"\n\nMore Information needed"
]
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|
e9e70b44cd92f2ad0e9f0312d24c393481815020 | # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_10_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_10_open_ended | [
"region:us"
]
| 2023-02-22T21:09:43+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 1619, "num_examples": 10}], "download_size": 2691, "dataset_size": 1619}} | 2023-02-22T21:09:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_10_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_10_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_10_open_ended\"\n\nMore Information needed"
]
| [
6,
52
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_10_open_ended\"\n\nMore Information needed"
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bbb5318a8738b37eae604a503dbe9de3d42c81c3 | # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_20_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_20_open_ended | [
"region:us"
]
| 2023-02-22T21:14:19+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 3065, "num_examples": 20}], "download_size": 4018, "dataset_size": 3065}} | 2023-02-23T00:19:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_20_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_20_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_20_open_ended\"\n\nMore Information needed"
]
| [
6,
52
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_20_open_ended\"\n\nMore Information needed"
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0.06835954636335373,
0.01159136462956667,
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-0.021918881684541702,
-0.07978517562150955,
-0.04210798442363739,
0.030872991308569908,
0.1118791326880455,
0.03256528079509735,
0.04701627790927887,
-0.025957565754652023,
0.12314888089895248,
0.10685160756111145,
0.05530258268117905,
-0.02080422453582287,
-0.09452126920223236
]
|
2ec2eaaad35068d323cf1b4ea911fc3d1aa9cf68 | # Dataset Card for ESA Hubble Deep Space Images & Captions
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Examples](#examples)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [ESA Hubble](https://esahubble.org/)
- **Repository:** [Hubble Diffusion repository](https://github.com/Supermaxman/hubble-diffusion)
- **Point of Contact:** [Maxwell Weinzierl](mailto:[email protected])
### Dataset Summary
The ESA Hubble Deep Space Images & Captions dataset is composed primarily of Hubble deep space scans as high-resolution images,
along with textual descriptions written by ESA/Hubble. Metadata is also included, which enables more detailed filtering and understanding of massive space scans.
The purpose of this dataset is to enable text-to-image generation methods for generating high-quality deep space scans from prompts.
Check out [Hubble Diffusion v2](https://huggingface.co/Supermaxman/hubble-diffusion-2) for an example of a model trained on this dataset!
### Examples
#### A grazing encounter between two spiral galaxies
> In the direction of the constellation Canis Major, two spiral galaxies pass by each other like majestic ships in the night. The near-collision has been caught in images taken by the NASA/ESA Hubble Space Telescope and its Wide Field Planetary Camera 2.
>
>
> Credit: NASA/ESA and The Hubble Heritage Team (STScI)
#### The magnificent starburst galaxy Messier 82
> This mosaic image of the magnificent starburst galaxy, Messier 82 (M82) is the sharpest wide-angle view ever obtained of M82. It is a galaxy remarkable for its webs of shredded clouds and flame-like plumes of glowing hydrogen blasting out from its central regions where young stars are being born 10 times faster than they are inside in our Milky Way Galaxy.
>
>
> Credit: NASA, ESA and the Hubble Heritage Team (STScI/AURA). Acknowledgment: J. Gallagher (University of Wisconsin), M. Mountain (STScI) and P. Puxley (NSF).
#### Extreme star cluster bursts into life in new Hubble image
> The star-forming region NGC 3603 - seen here in the latest Hubble Space Telescope image - contains one of the most impressive massive young star clusters in the Milky Way. Bathed in gas and dust the cluster formed in a huge rush of star formation thought to have occurred around a million years ago. The hot blue stars at the core are responsible for carving out a huge cavity in the gas seen to the right of the star cluster in NGC 3603's centre.
>
>
> Credit: NASA, ESA and the Hubble Heritage (STScI/AURA)-ESA/Hubble Collaboration
#### Statistics
- There are a total of 2,706 deep space images
- The complete uncompressed size of the dataset is 120 GB, so definitely make use of [Streaming](https://huggingface.co/docs/datasets/stream)
- The average image is 44 MB, while the max image size is 432 MB
- The average image has a height of 2,881 pixels, and an average width of 3,267 pixels
### Supported Tasks and Leaderboards
- `text-to-image`: The dataset can be used to train a model for conditional image generation from text. A conditional text-to-image generation model is presented with a text prompt, and is asked to generate an image which aligns with that text prompt. Model performance is typically measured by human judgement, as it is difficult to automatically measure the quality of generated images and how closely they match the text prompt. An example of a text-to-image model is [Stable Diffusion v2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1). An example of a text-to-image model trained on this dataset is [Hubble Diffusion v2](https://huggingface.co/Supermaxman/hubble-diffusion-2).
### Languages
The text describing the images in the dataset is in English, as written by the writers from ESA/Hubble at [https://esahubble.org/](https://esahubble.org/). The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
A typical data point comprises a high-quality deep space scan as an image, along with a textual description of that image produced by ESA/Hubble.
The textual description was derived by combining the `title` and the `description` of the image from the ESA/Hubble website.
Additionally, each data point also contains significant metadata about the image, such as the type of image, credits, the URL, the release date, and more.
An example looks as follows:
```json
{
"image": "<encoded image>",
"text":"A grazing encounter between two spiral galaxies: In the direction of the constellation Canis Major, two spiral galaxies pass by each other like majestic ships in the night. The near-collision has been caught in images taken by the NASA/ESA Hubble Space Telescope and its Wide Field Planetary Camera 2.",
"id":"opo9941a",
"title":"A grazing encounter between two spiral galaxies",
"description":"In the direction of the constellation Canis Major, two spiral galaxies pass by each other like majestic ships in the night. The near-collision has been caught in images taken by the NASA/ESA Hubble Space Telescope and its Wide Field Planetary Camera 2.",
"credits":"NASA/ESA and The Hubble Heritage Team (STScI)",
"url":"https://esahubble.org/images/opo9941a/",
"Id":"opo9941a",
"Type":"Local Universe : Galaxy : Type : Interacting",
"Release date":"4 November 1999, 07:00",
"Size":"2907 x 1486 px",
"Name":"IC 2163, NGC 2207",
"Distance":"110 million light years",
"Constellation":"Canis Major",
"Category":"Galaxies",
"Position (RA)":"6 16 25.10",
"Position (Dec)":"-21° 22' 34.62\"",
"Field of view":"4.82 x 2.47 arcminutes",
"Orientation":"North is 191.2\u00b0 right of vertical",
"width":2907,
"height":1486,
"file_size":12959406,
"crop_w":0,
"crop_h":0,
"cropped":false
}
```
### Data Fields
- `image`: encoded RGB `.png` image of the deep space scan
- `text`: text description of image, a combination of `title` + ': ' + `description`
- `id`: id of the image from ESA/Hubble
- `title`: textual title of image from ESA/Hubble URL
- `description`: textual description of image from ESA/Hubble URL
- `credits`: required credits for each image from ESA/Hubble URL
- `url`: ESA/Hubble URL
- `Id`: id of the image from ESA/Hubble (from website metadata)
- `Type`: type of deep space scan
- `Release date`: release date of deep space scan
- `Size`: size of original image
- `Name`: name of celestial entities present in image
- `Distance`: distance from celestial entities present in image
- `Constellation`: constellation of celestial entities present in image
- `Category`: category of celestial entities present in image
- `Position (RA)`: coordinates for deep space scan used by Hubble telescope
- `Position (Dec)`: coordinates for deep space scan used by Hubble telescope
- `Field of view`: coordinates for deep space scan used by Hubble telescope
- `Orientation`: coordinates for deep space scan used by Hubble telescope
- `width`: width of image, same if the image did not need to be cropped, but otherwise could differ from `Size`
- `height`: height of image, same if the image did not need to be cropped, but otherwise could differ from `Size`
- `file_size`: `width` x `height` x 3 bytes, used to estimate size of raw images
- `crop_w`: width starting point of image if cropped, otherwise 0
- `crop_h`: height starting point of image if cropped, otherwise 0
- `cropped`: whether this image needed to be cropped or not
### Data Splits
The data is only provided in a single training split, as the purpose of the dataset is additional fine-tuning for the task of `text-to-image` generation.
## Dataset Creation
### Curation Rationale
The ESA Hubble Deep Space Images & Captions dataset was built to provide ease of access to extremely high-quality Hubble deep space scans.
Images from the Hubble telescope have already inspired millions, and the hope is that this dataset can be used to create inspiring models and approaches to further push interest in space & cosmology.
### Source Data
#### Initial Data Collection
All images were collected from [https://esahubble.org/](https://esahubble.org/).
Fullsize Original images & metadata were crawled from the ESA Hubble website using [Scrapy](https://scrapy.org/).
Images were downloaded as `.tiff` files, while
additional metadata was later collected for each image using the following [code](https://github.com/Supermaxman/hubble-diffusion).
As the ESA Hubble website collects images from a wide variety of sources, images were filtered to try to avoid any non-space scan images as follows:
The ESA Hubble [Advanced Image Search](http://esahubble.org/images/archive/search) enables the following filtering parameters:
- images with Minimum size greater than or equal to 400x300
- Ranking greater than or equal to Fair or better
- Type containing 'Observation'
This reduced significantly the number of images which had nothing to do with Hubble deep space scans.
A total of around 3,000 space images were collected with this method.
#### Filtering
Further automatic and manual filtering was performed to remove the following:
- improperly classified images
- space renders
- diagrams with text
- images of celestial bodies within our solar system
- images with too low a resolution
This brought the total number of deep space images down to 2,593.
This process was not perfect, and there likely remain some images in the dataset that should be removed in the future.
#### Preprocessing
Some of the deep space scans were as large as 34,372x19,345, with a bit depth of 24 (nearly 2 GB).
Unfortunately, these images were too large to upload easily
Therefore, images were automatically subdivided in half if they were above 12,000 pixels in either height or width.
Subdivided images were also tagged with additional metadata, such that users can reconstruct the original images if they would prefer.
Otherwise, metadata was copied across subdivided images.
Additionally, images were converted from RGB/RGBX `.tiff` to RGB `.png` files to avoid encoding issues.
This process resulted in 2,706 final deep space images.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help inspire people to be interested in astronomy.
A system that succeeds at text-to-image generation would be able to generate inspiring deep space scans, providing interesting and inspiring art for those interested in space. This dataset provides a starting-point for building such a system by providing text and image pairs for Hubble deep space scans.
### Discussion of Biases
It is unfortunate that we currently only have English captions for these deep space scans.
In the future, expanding these captions to more languages could help spread interest in astronomy far and wide.
Additionally, these captions may be too technical for the average person to effectively utilize for a text-to-image model.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
The dataset was initially created by all the wonderful researchers, engineers, scientists, and more behind the Hubble Telescope, NASA, and the ESA.
Maxwell Weinzierl collected, filtered, and preprocessed this data for ease of use.
### Licensing Information
ESA/Hubble images, videos and web texts are released under the [Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/)
and may on a non-exclusive basis be reproduced without fee provided they are clearly and visibly credited.
See [https://esahubble.org/copyright/](https://esahubble.org/copyright/) for additional conditions for reproduction and copyright.
### Citation Information
If you use this dataset, please cite it as:
```bibtex
@misc{weinzierl2023hubble,
author = {Weinzierl, Maxwell A.},
title = {ESA Hubble Deep Space Images & Captions},
year={2023},
howpublished= {\url{https://huggingface.co/datasets/Supermaxman/esa-hubble}}
}
```
### Contributions
Thanks to [@supermaxman](https://github.com/supermaxman) for adding this dataset.
| Supermaxman/esa-hubble | [
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"space",
"region:us"
]
| 2023-02-22T22:03:08+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"], "pretty_name": "ESA Hubble Deep Space Images & Captions", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "credits", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "Id", "dtype": "string"}, {"name": "Type", "dtype": "string"}, {"name": "Release date", "dtype": "string"}, {"name": "Related releases", "dtype": "string"}, {"name": "Size", "dtype": "string"}, {"name": "Name", "dtype": "string"}, {"name": "Distance", "dtype": "string"}, {"name": "Constellation", "dtype": "string"}, {"name": "Category", "dtype": "string"}, {"name": "Position (RA)", "dtype": "string"}, {"name": "Position (Dec)", "dtype": "string"}, {"name": "Field of view", "dtype": "string"}, {"name": "Orientation", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "file_size", "dtype": "int64"}, {"name": "crop_w", "dtype": "int64"}, {"name": "crop_h", "dtype": "int64"}, {"name": "cropped", "dtype": "bool"}, {"name": "Related science announcements", "dtype": "string"}, {"name": "Related announcements", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 94474695584.124, "num_examples": 2706}], "download_size": 61236366105, "dataset_size": 94474695584.124}, "tags": ["space"]} | 2023-02-26T13:20:26+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #space #region-us
| # Dataset Card for ESA Hubble Deep Space Images & Captions
## Table of Contents
- Dataset Description
- Dataset Summary
- Examples
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Homepage: ESA Hubble
- Repository: Hubble Diffusion repository
- Point of Contact: Maxwell Weinzierl
### Dataset Summary
The ESA Hubble Deep Space Images & Captions dataset is composed primarily of Hubble deep space scans as high-resolution images,
along with textual descriptions written by ESA/Hubble. Metadata is also included, which enables more detailed filtering and understanding of massive space scans.
The purpose of this dataset is to enable text-to-image generation methods for generating high-quality deep space scans from prompts.
Check out Hubble Diffusion v2 for an example of a model trained on this dataset!
### Examples
#### A grazing encounter between two spiral galaxies
> In the direction of the constellation Canis Major, two spiral galaxies pass by each other like majestic ships in the night. The near-collision has been caught in images taken by the NASA/ESA Hubble Space Telescope and its Wide Field Planetary Camera 2.
>!opo9941a
>
> Credit: NASA/ESA and The Hubble Heritage Team (STScI)
#### The magnificent starburst galaxy Messier 82
> This mosaic image of the magnificent starburst galaxy, Messier 82 (M82) is the sharpest wide-angle view ever obtained of M82. It is a galaxy remarkable for its webs of shredded clouds and flame-like plumes of glowing hydrogen blasting out from its central regions where young stars are being born 10 times faster than they are inside in our Milky Way Galaxy.
>!heic0604a
>
> Credit: NASA, ESA and the Hubble Heritage Team (STScI/AURA). Acknowledgment: J. Gallagher (University of Wisconsin), M. Mountain (STScI) and P. Puxley (NSF).
#### Extreme star cluster bursts into life in new Hubble image
> The star-forming region NGC 3603 - seen here in the latest Hubble Space Telescope image - contains one of the most impressive massive young star clusters in the Milky Way. Bathed in gas and dust the cluster formed in a huge rush of star formation thought to have occurred around a million years ago. The hot blue stars at the core are responsible for carving out a huge cavity in the gas seen to the right of the star cluster in NGC 3603's centre.
>!heic0715a
>
> Credit: NASA, ESA and the Hubble Heritage (STScI/AURA)-ESA/Hubble Collaboration
#### Statistics
- There are a total of 2,706 deep space images
- The complete uncompressed size of the dataset is 120 GB, so definitely make use of Streaming
- The average image is 44 MB, while the max image size is 432 MB
- The average image has a height of 2,881 pixels, and an average width of 3,267 pixels
### Supported Tasks and Leaderboards
- 'text-to-image': The dataset can be used to train a model for conditional image generation from text. A conditional text-to-image generation model is presented with a text prompt, and is asked to generate an image which aligns with that text prompt. Model performance is typically measured by human judgement, as it is difficult to automatically measure the quality of generated images and how closely they match the text prompt. An example of a text-to-image model is Stable Diffusion v2-1. An example of a text-to-image model trained on this dataset is Hubble Diffusion v2.
### Languages
The text describing the images in the dataset is in English, as written by the writers from ESA/Hubble at URL The associated BCP-47 code is 'en'.
## Dataset Structure
### Data Instances
A typical data point comprises a high-quality deep space scan as an image, along with a textual description of that image produced by ESA/Hubble.
The textual description was derived by combining the 'title' and the 'description' of the image from the ESA/Hubble website.
Additionally, each data point also contains significant metadata about the image, such as the type of image, credits, the URL, the release date, and more.
An example looks as follows:
### Data Fields
- 'image': encoded RGB '.png' image of the deep space scan
- 'text': text description of image, a combination of 'title' + ': ' + 'description'
- 'id': id of the image from ESA/Hubble
- 'title': textual title of image from ESA/Hubble URL
- 'description': textual description of image from ESA/Hubble URL
- 'credits': required credits for each image from ESA/Hubble URL
- 'url': ESA/Hubble URL
- 'Id': id of the image from ESA/Hubble (from website metadata)
- 'Type': type of deep space scan
- 'Release date': release date of deep space scan
- 'Size': size of original image
- 'Name': name of celestial entities present in image
- 'Distance': distance from celestial entities present in image
- 'Constellation': constellation of celestial entities present in image
- 'Category': category of celestial entities present in image
- 'Position (RA)': coordinates for deep space scan used by Hubble telescope
- 'Position (Dec)': coordinates for deep space scan used by Hubble telescope
- 'Field of view': coordinates for deep space scan used by Hubble telescope
- 'Orientation': coordinates for deep space scan used by Hubble telescope
- 'width': width of image, same if the image did not need to be cropped, but otherwise could differ from 'Size'
- 'height': height of image, same if the image did not need to be cropped, but otherwise could differ from 'Size'
- 'file_size': 'width' x 'height' x 3 bytes, used to estimate size of raw images
- 'crop_w': width starting point of image if cropped, otherwise 0
- 'crop_h': height starting point of image if cropped, otherwise 0
- 'cropped': whether this image needed to be cropped or not
### Data Splits
The data is only provided in a single training split, as the purpose of the dataset is additional fine-tuning for the task of 'text-to-image' generation.
## Dataset Creation
### Curation Rationale
The ESA Hubble Deep Space Images & Captions dataset was built to provide ease of access to extremely high-quality Hubble deep space scans.
Images from the Hubble telescope have already inspired millions, and the hope is that this dataset can be used to create inspiring models and approaches to further push interest in space & cosmology.
### Source Data
#### Initial Data Collection
All images were collected from URL
Fullsize Original images & metadata were crawled from the ESA Hubble website using Scrapy.
Images were downloaded as '.tiff' files, while
additional metadata was later collected for each image using the following code.
As the ESA Hubble website collects images from a wide variety of sources, images were filtered to try to avoid any non-space scan images as follows:
The ESA Hubble Advanced Image Search enables the following filtering parameters:
- images with Minimum size greater than or equal to 400x300
- Ranking greater than or equal to Fair or better
- Type containing 'Observation'
This reduced significantly the number of images which had nothing to do with Hubble deep space scans.
A total of around 3,000 space images were collected with this method.
#### Filtering
Further automatic and manual filtering was performed to remove the following:
- improperly classified images
- space renders
- diagrams with text
- images of celestial bodies within our solar system
- images with too low a resolution
This brought the total number of deep space images down to 2,593.
This process was not perfect, and there likely remain some images in the dataset that should be removed in the future.
#### Preprocessing
Some of the deep space scans were as large as 34,372x19,345, with a bit depth of 24 (nearly 2 GB).
Unfortunately, these images were too large to upload easily
Therefore, images were automatically subdivided in half if they were above 12,000 pixels in either height or width.
Subdivided images were also tagged with additional metadata, such that users can reconstruct the original images if they would prefer.
Otherwise, metadata was copied across subdivided images.
Additionally, images were converted from RGB/RGBX '.tiff' to RGB '.png' files to avoid encoding issues.
This process resulted in 2,706 final deep space images.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help inspire people to be interested in astronomy.
A system that succeeds at text-to-image generation would be able to generate inspiring deep space scans, providing interesting and inspiring art for those interested in space. This dataset provides a starting-point for building such a system by providing text and image pairs for Hubble deep space scans.
### Discussion of Biases
It is unfortunate that we currently only have English captions for these deep space scans.
In the future, expanding these captions to more languages could help spread interest in astronomy far and wide.
Additionally, these captions may be too technical for the average person to effectively utilize for a text-to-image model.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
The dataset was initially created by all the wonderful researchers, engineers, scientists, and more behind the Hubble Telescope, NASA, and the ESA.
Maxwell Weinzierl collected, filtered, and preprocessed this data for ease of use.
### Licensing Information
ESA/Hubble images, videos and web texts are released under the Creative Commons Attribution 4.0 International license
and may on a non-exclusive basis be reproduced without fee provided they are clearly and visibly credited.
See URL for additional conditions for reproduction and copyright.
If you use this dataset, please cite it as:
### Contributions
Thanks to @supermaxman for adding this dataset.
| [
"# Dataset Card for ESA Hubble Deep Space Images & Captions",
"## Table of Contents\n\n- Dataset Description\n - Dataset Summary\n - Examples\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: ESA Hubble\n- Repository: Hubble Diffusion repository\n- Point of Contact: Maxwell Weinzierl",
"### Dataset Summary\n\nThe ESA Hubble Deep Space Images & Captions dataset is composed primarily of Hubble deep space scans as high-resolution images,\nalong with textual descriptions written by ESA/Hubble. Metadata is also included, which enables more detailed filtering and understanding of massive space scans.\nThe purpose of this dataset is to enable text-to-image generation methods for generating high-quality deep space scans from prompts.\nCheck out Hubble Diffusion v2 for an example of a model trained on this dataset!",
"### Examples",
"#### A grazing encounter between two spiral galaxies\n\n> In the direction of the constellation Canis Major, two spiral galaxies pass by each other like majestic ships in the night. The near-collision has been caught in images taken by the NASA/ESA Hubble Space Telescope and its Wide Field Planetary Camera 2.\n>!opo9941a\n>\n> Credit: NASA/ESA and The Hubble Heritage Team (STScI)",
"#### The magnificent starburst galaxy Messier 82\n\n> This mosaic image of the magnificent starburst galaxy, Messier 82 (M82) is the sharpest wide-angle view ever obtained of M82. It is a galaxy remarkable for its webs of shredded clouds and flame-like plumes of glowing hydrogen blasting out from its central regions where young stars are being born 10 times faster than they are inside in our Milky Way Galaxy.\n>!heic0604a\n>\n> Credit: NASA, ESA and the Hubble Heritage Team (STScI/AURA). Acknowledgment: J. Gallagher (University of Wisconsin), M. Mountain (STScI) and P. Puxley (NSF).",
"#### Extreme star cluster bursts into life in new Hubble image\n\n> The star-forming region NGC 3603 - seen here in the latest Hubble Space Telescope image - contains one of the most impressive massive young star clusters in the Milky Way. Bathed in gas and dust the cluster formed in a huge rush of star formation thought to have occurred around a million years ago. The hot blue stars at the core are responsible for carving out a huge cavity in the gas seen to the right of the star cluster in NGC 3603's centre.\n>!heic0715a\n>\n> Credit: NASA, ESA and the Hubble Heritage (STScI/AURA)-ESA/Hubble Collaboration",
"#### Statistics\n\n- There are a total of 2,706 deep space images\n- The complete uncompressed size of the dataset is 120 GB, so definitely make use of Streaming\n- The average image is 44 MB, while the max image size is 432 MB\n- The average image has a height of 2,881 pixels, and an average width of 3,267 pixels",
"### Supported Tasks and Leaderboards\n\n- 'text-to-image': The dataset can be used to train a model for conditional image generation from text. A conditional text-to-image generation model is presented with a text prompt, and is asked to generate an image which aligns with that text prompt. Model performance is typically measured by human judgement, as it is difficult to automatically measure the quality of generated images and how closely they match the text prompt. An example of a text-to-image model is Stable Diffusion v2-1. An example of a text-to-image model trained on this dataset is Hubble Diffusion v2.",
"### Languages\n\nThe text describing the images in the dataset is in English, as written by the writers from ESA/Hubble at URL The associated BCP-47 code is 'en'.",
"## Dataset Structure",
"### Data Instances\n\nA typical data point comprises a high-quality deep space scan as an image, along with a textual description of that image produced by ESA/Hubble.\nThe textual description was derived by combining the 'title' and the 'description' of the image from the ESA/Hubble website.\nAdditionally, each data point also contains significant metadata about the image, such as the type of image, credits, the URL, the release date, and more.\n\nAn example looks as follows:",
"### Data Fields\n\n- 'image': encoded RGB '.png' image of the deep space scan\n- 'text': text description of image, a combination of 'title' + ': ' + 'description'\n- 'id': id of the image from ESA/Hubble\n- 'title': textual title of image from ESA/Hubble URL\n- 'description': textual description of image from ESA/Hubble URL\n- 'credits': required credits for each image from ESA/Hubble URL\n- 'url': ESA/Hubble URL\n- 'Id': id of the image from ESA/Hubble (from website metadata)\n- 'Type': type of deep space scan\n- 'Release date': release date of deep space scan\n- 'Size': size of original image\n- 'Name': name of celestial entities present in image\n- 'Distance': distance from celestial entities present in image\n- 'Constellation': constellation of celestial entities present in image\n- 'Category': category of celestial entities present in image\n- 'Position (RA)': coordinates for deep space scan used by Hubble telescope\n- 'Position (Dec)': coordinates for deep space scan used by Hubble telescope\n- 'Field of view': coordinates for deep space scan used by Hubble telescope\n- 'Orientation': coordinates for deep space scan used by Hubble telescope\n- 'width': width of image, same if the image did not need to be cropped, but otherwise could differ from 'Size'\n- 'height': height of image, same if the image did not need to be cropped, but otherwise could differ from 'Size'\n- 'file_size': 'width' x 'height' x 3 bytes, used to estimate size of raw images\n- 'crop_w': width starting point of image if cropped, otherwise 0\n- 'crop_h': height starting point of image if cropped, otherwise 0\n- 'cropped': whether this image needed to be cropped or not",
"### Data Splits\n\nThe data is only provided in a single training split, as the purpose of the dataset is additional fine-tuning for the task of 'text-to-image' generation.",
"## Dataset Creation",
"### Curation Rationale\n\nThe ESA Hubble Deep Space Images & Captions dataset was built to provide ease of access to extremely high-quality Hubble deep space scans.\nImages from the Hubble telescope have already inspired millions, and the hope is that this dataset can be used to create inspiring models and approaches to further push interest in space & cosmology.",
"### Source Data",
"#### Initial Data Collection\n\nAll images were collected from URL\nFullsize Original images & metadata were crawled from the ESA Hubble website using Scrapy.\nImages were downloaded as '.tiff' files, while\nadditional metadata was later collected for each image using the following code.\n\nAs the ESA Hubble website collects images from a wide variety of sources, images were filtered to try to avoid any non-space scan images as follows:\nThe ESA Hubble Advanced Image Search enables the following filtering parameters:\n\n- images with Minimum size greater than or equal to 400x300\n- Ranking greater than or equal to Fair or better\n- Type containing 'Observation'\n\nThis reduced significantly the number of images which had nothing to do with Hubble deep space scans.\nA total of around 3,000 space images were collected with this method.",
"#### Filtering\n\nFurther automatic and manual filtering was performed to remove the following:\n\n- improperly classified images\n- space renders\n- diagrams with text\n- images of celestial bodies within our solar system\n- images with too low a resolution\n\nThis brought the total number of deep space images down to 2,593.\nThis process was not perfect, and there likely remain some images in the dataset that should be removed in the future.",
"#### Preprocessing\n\nSome of the deep space scans were as large as 34,372x19,345, with a bit depth of 24 (nearly 2 GB).\nUnfortunately, these images were too large to upload easily\nTherefore, images were automatically subdivided in half if they were above 12,000 pixels in either height or width.\nSubdivided images were also tagged with additional metadata, such that users can reconstruct the original images if they would prefer.\nOtherwise, metadata was copied across subdivided images.\nAdditionally, images were converted from RGB/RGBX '.tiff' to RGB '.png' files to avoid encoding issues.\nThis process resulted in 2,706 final deep space images.",
"### Annotations\n\nThe dataset does not contain any additional annotations.",
"#### Annotation process\n\n[N/A]",
"#### Who are the annotators?\n\n[N/A]",
"### Personal and Sensitive Information\n\n[N/A]",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThe purpose of this dataset is to help inspire people to be interested in astronomy.\n\nA system that succeeds at text-to-image generation would be able to generate inspiring deep space scans, providing interesting and inspiring art for those interested in space. This dataset provides a starting-point for building such a system by providing text and image pairs for Hubble deep space scans.",
"### Discussion of Biases\n\nIt is unfortunate that we currently only have English captions for these deep space scans.\nIn the future, expanding these captions to more languages could help spread interest in astronomy far and wide.\n\nAdditionally, these captions may be too technical for the average person to effectively utilize for a text-to-image model.",
"### Other Known Limitations\n\n[N/A]",
"## Additional Information",
"### Dataset Curators\n\nThe dataset was initially created by all the wonderful researchers, engineers, scientists, and more behind the Hubble Telescope, NASA, and the ESA.\nMaxwell Weinzierl collected, filtered, and preprocessed this data for ease of use.",
"### Licensing Information\n\nESA/Hubble images, videos and web texts are released under the Creative Commons Attribution 4.0 International license\nand may on a non-exclusive basis be reproduced without fee provided they are clearly and visibly credited.\nSee URL for additional conditions for reproduction and copyright.\n\n\n\nIf you use this dataset, please cite it as:",
"### Contributions\n\nThanks to @supermaxman for adding this dataset."
]
| [
"TAGS\n#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #space #region-us \n",
"# Dataset Card for ESA Hubble Deep Space Images & Captions",
"## Table of Contents\n\n- Dataset Description\n - Dataset Summary\n - Examples\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: ESA Hubble\n- Repository: Hubble Diffusion repository\n- Point of Contact: Maxwell Weinzierl",
"### Dataset Summary\n\nThe ESA Hubble Deep Space Images & Captions dataset is composed primarily of Hubble deep space scans as high-resolution images,\nalong with textual descriptions written by ESA/Hubble. Metadata is also included, which enables more detailed filtering and understanding of massive space scans.\nThe purpose of this dataset is to enable text-to-image generation methods for generating high-quality deep space scans from prompts.\nCheck out Hubble Diffusion v2 for an example of a model trained on this dataset!",
"### Examples",
"#### A grazing encounter between two spiral galaxies\n\n> In the direction of the constellation Canis Major, two spiral galaxies pass by each other like majestic ships in the night. The near-collision has been caught in images taken by the NASA/ESA Hubble Space Telescope and its Wide Field Planetary Camera 2.\n>!opo9941a\n>\n> Credit: NASA/ESA and The Hubble Heritage Team (STScI)",
"#### The magnificent starburst galaxy Messier 82\n\n> This mosaic image of the magnificent starburst galaxy, Messier 82 (M82) is the sharpest wide-angle view ever obtained of M82. It is a galaxy remarkable for its webs of shredded clouds and flame-like plumes of glowing hydrogen blasting out from its central regions where young stars are being born 10 times faster than they are inside in our Milky Way Galaxy.\n>!heic0604a\n>\n> Credit: NASA, ESA and the Hubble Heritage Team (STScI/AURA). Acknowledgment: J. Gallagher (University of Wisconsin), M. Mountain (STScI) and P. Puxley (NSF).",
"#### Extreme star cluster bursts into life in new Hubble image\n\n> The star-forming region NGC 3603 - seen here in the latest Hubble Space Telescope image - contains one of the most impressive massive young star clusters in the Milky Way. Bathed in gas and dust the cluster formed in a huge rush of star formation thought to have occurred around a million years ago. The hot blue stars at the core are responsible for carving out a huge cavity in the gas seen to the right of the star cluster in NGC 3603's centre.\n>!heic0715a\n>\n> Credit: NASA, ESA and the Hubble Heritage (STScI/AURA)-ESA/Hubble Collaboration",
"#### Statistics\n\n- There are a total of 2,706 deep space images\n- The complete uncompressed size of the dataset is 120 GB, so definitely make use of Streaming\n- The average image is 44 MB, while the max image size is 432 MB\n- The average image has a height of 2,881 pixels, and an average width of 3,267 pixels",
"### Supported Tasks and Leaderboards\n\n- 'text-to-image': The dataset can be used to train a model for conditional image generation from text. A conditional text-to-image generation model is presented with a text prompt, and is asked to generate an image which aligns with that text prompt. Model performance is typically measured by human judgement, as it is difficult to automatically measure the quality of generated images and how closely they match the text prompt. An example of a text-to-image model is Stable Diffusion v2-1. An example of a text-to-image model trained on this dataset is Hubble Diffusion v2.",
"### Languages\n\nThe text describing the images in the dataset is in English, as written by the writers from ESA/Hubble at URL The associated BCP-47 code is 'en'.",
"## Dataset Structure",
"### Data Instances\n\nA typical data point comprises a high-quality deep space scan as an image, along with a textual description of that image produced by ESA/Hubble.\nThe textual description was derived by combining the 'title' and the 'description' of the image from the ESA/Hubble website.\nAdditionally, each data point also contains significant metadata about the image, such as the type of image, credits, the URL, the release date, and more.\n\nAn example looks as follows:",
"### Data Fields\n\n- 'image': encoded RGB '.png' image of the deep space scan\n- 'text': text description of image, a combination of 'title' + ': ' + 'description'\n- 'id': id of the image from ESA/Hubble\n- 'title': textual title of image from ESA/Hubble URL\n- 'description': textual description of image from ESA/Hubble URL\n- 'credits': required credits for each image from ESA/Hubble URL\n- 'url': ESA/Hubble URL\n- 'Id': id of the image from ESA/Hubble (from website metadata)\n- 'Type': type of deep space scan\n- 'Release date': release date of deep space scan\n- 'Size': size of original image\n- 'Name': name of celestial entities present in image\n- 'Distance': distance from celestial entities present in image\n- 'Constellation': constellation of celestial entities present in image\n- 'Category': category of celestial entities present in image\n- 'Position (RA)': coordinates for deep space scan used by Hubble telescope\n- 'Position (Dec)': coordinates for deep space scan used by Hubble telescope\n- 'Field of view': coordinates for deep space scan used by Hubble telescope\n- 'Orientation': coordinates for deep space scan used by Hubble telescope\n- 'width': width of image, same if the image did not need to be cropped, but otherwise could differ from 'Size'\n- 'height': height of image, same if the image did not need to be cropped, but otherwise could differ from 'Size'\n- 'file_size': 'width' x 'height' x 3 bytes, used to estimate size of raw images\n- 'crop_w': width starting point of image if cropped, otherwise 0\n- 'crop_h': height starting point of image if cropped, otherwise 0\n- 'cropped': whether this image needed to be cropped or not",
"### Data Splits\n\nThe data is only provided in a single training split, as the purpose of the dataset is additional fine-tuning for the task of 'text-to-image' generation.",
"## Dataset Creation",
"### Curation Rationale\n\nThe ESA Hubble Deep Space Images & Captions dataset was built to provide ease of access to extremely high-quality Hubble deep space scans.\nImages from the Hubble telescope have already inspired millions, and the hope is that this dataset can be used to create inspiring models and approaches to further push interest in space & cosmology.",
"### Source Data",
"#### Initial Data Collection\n\nAll images were collected from URL\nFullsize Original images & metadata were crawled from the ESA Hubble website using Scrapy.\nImages were downloaded as '.tiff' files, while\nadditional metadata was later collected for each image using the following code.\n\nAs the ESA Hubble website collects images from a wide variety of sources, images were filtered to try to avoid any non-space scan images as follows:\nThe ESA Hubble Advanced Image Search enables the following filtering parameters:\n\n- images with Minimum size greater than or equal to 400x300\n- Ranking greater than or equal to Fair or better\n- Type containing 'Observation'\n\nThis reduced significantly the number of images which had nothing to do with Hubble deep space scans.\nA total of around 3,000 space images were collected with this method.",
"#### Filtering\n\nFurther automatic and manual filtering was performed to remove the following:\n\n- improperly classified images\n- space renders\n- diagrams with text\n- images of celestial bodies within our solar system\n- images with too low a resolution\n\nThis brought the total number of deep space images down to 2,593.\nThis process was not perfect, and there likely remain some images in the dataset that should be removed in the future.",
"#### Preprocessing\n\nSome of the deep space scans were as large as 34,372x19,345, with a bit depth of 24 (nearly 2 GB).\nUnfortunately, these images were too large to upload easily\nTherefore, images were automatically subdivided in half if they were above 12,000 pixels in either height or width.\nSubdivided images were also tagged with additional metadata, such that users can reconstruct the original images if they would prefer.\nOtherwise, metadata was copied across subdivided images.\nAdditionally, images were converted from RGB/RGBX '.tiff' to RGB '.png' files to avoid encoding issues.\nThis process resulted in 2,706 final deep space images.",
"### Annotations\n\nThe dataset does not contain any additional annotations.",
"#### Annotation process\n\n[N/A]",
"#### Who are the annotators?\n\n[N/A]",
"### Personal and Sensitive Information\n\n[N/A]",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThe purpose of this dataset is to help inspire people to be interested in astronomy.\n\nA system that succeeds at text-to-image generation would be able to generate inspiring deep space scans, providing interesting and inspiring art for those interested in space. This dataset provides a starting-point for building such a system by providing text and image pairs for Hubble deep space scans.",
"### Discussion of Biases\n\nIt is unfortunate that we currently only have English captions for these deep space scans.\nIn the future, expanding these captions to more languages could help spread interest in astronomy far and wide.\n\nAdditionally, these captions may be too technical for the average person to effectively utilize for a text-to-image model.",
"### Other Known Limitations\n\n[N/A]",
"## Additional Information",
"### Dataset Curators\n\nThe dataset was initially created by all the wonderful researchers, engineers, scientists, and more behind the Hubble Telescope, NASA, and the ESA.\nMaxwell Weinzierl collected, filtered, and preprocessed this data for ease of use.",
"### Licensing Information\n\nESA/Hubble images, videos and web texts are released under the Creative Commons Attribution 4.0 International license\nand may on a non-exclusive basis be reproduced without fee provided they are clearly and visibly credited.\nSee URL for additional conditions for reproduction and copyright.\n\n\n\nIf you use this dataset, please cite it as:",
"### Contributions\n\nThanks to @supermaxman for adding this dataset."
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"passage: TAGS\n#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #space #region-us \n# Dataset Card for ESA Hubble Deep Space Images & Captions## Table of Contents\n\n- Dataset Description\n - Dataset Summary\n - Examples\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage: ESA Hubble\n- Repository: Hubble Diffusion repository\n- Point of Contact: Maxwell Weinzierl### Dataset Summary\n\nThe ESA Hubble Deep Space Images & Captions dataset is composed primarily of Hubble deep space scans as high-resolution images,\nalong with textual descriptions written by ESA/Hubble. Metadata is also included, which enables more detailed filtering and understanding of massive space scans.\nThe purpose of this dataset is to enable text-to-image generation methods for generating high-quality deep space scans from prompts.\nCheck out Hubble Diffusion v2 for an example of a model trained on this dataset!### Examples#### A grazing encounter between two spiral galaxies\n\n> In the direction of the constellation Canis Major, two spiral galaxies pass by each other like majestic ships in the night. The near-collision has been caught in images taken by the NASA/ESA Hubble Space Telescope and its Wide Field Planetary Camera 2.\n>!opo9941a\n>\n> Credit: NASA/ESA and The Hubble Heritage Team (STScI)",
"passage: #### The magnificent starburst galaxy Messier 82\n\n> This mosaic image of the magnificent starburst galaxy, Messier 82 (M82) is the sharpest wide-angle view ever obtained of M82. It is a galaxy remarkable for its webs of shredded clouds and flame-like plumes of glowing hydrogen blasting out from its central regions where young stars are being born 10 times faster than they are inside in our Milky Way Galaxy.\n>!heic0604a\n>\n> Credit: NASA, ESA and the Hubble Heritage Team (STScI/AURA). Acknowledgment: J. Gallagher (University of Wisconsin), M. Mountain (STScI) and P. Puxley (NSF).#### Extreme star cluster bursts into life in new Hubble image\n\n> The star-forming region NGC 3603 - seen here in the latest Hubble Space Telescope image - contains one of the most impressive massive young star clusters in the Milky Way. Bathed in gas and dust the cluster formed in a huge rush of star formation thought to have occurred around a million years ago. The hot blue stars at the core are responsible for carving out a huge cavity in the gas seen to the right of the star cluster in NGC 3603's centre.\n>!heic0715a\n>\n> Credit: NASA, ESA and the Hubble Heritage (STScI/AURA)-ESA/Hubble Collaboration#### Statistics\n\n- There are a total of 2,706 deep space images\n- The complete uncompressed size of the dataset is 120 GB, so definitely make use of Streaming\n- The average image is 44 MB, while the max image size is 432 MB\n- The average image has a height of 2,881 pixels, and an average width of 3,267 pixels### Supported Tasks and Leaderboards\n\n- 'text-to-image': The dataset can be used to train a model for conditional image generation from text. A conditional text-to-image generation model is presented with a text prompt, and is asked to generate an image which aligns with that text prompt. Model performance is typically measured by human judgement, as it is difficult to automatically measure the quality of generated images and how closely they match the text prompt. An example of a text-to-image model is Stable Diffusion v2-1. An example of a text-to-image model trained on this dataset is Hubble Diffusion v2.### Languages\n\nThe text describing the images in the dataset is in English, as written by the writers from ESA/Hubble at URL The associated BCP-47 code is 'en'.## Dataset Structure",
"passage: ### Data Instances\n\nA typical data point comprises a high-quality deep space scan as an image, along with a textual description of that image produced by ESA/Hubble.\nThe textual description was derived by combining the 'title' and the 'description' of the image from the ESA/Hubble website.\nAdditionally, each data point also contains significant metadata about the image, such as the type of image, credits, the URL, the release date, and more.\n\nAn example looks as follows:### Data Fields\n\n- 'image': encoded RGB '.png' image of the deep space scan\n- 'text': text description of image, a combination of 'title' + ': ' + 'description'\n- 'id': id of the image from ESA/Hubble\n- 'title': textual title of image from ESA/Hubble URL\n- 'description': textual description of image from ESA/Hubble URL\n- 'credits': required credits for each image from ESA/Hubble URL\n- 'url': ESA/Hubble URL\n- 'Id': id of the image from ESA/Hubble (from website metadata)\n- 'Type': type of deep space scan\n- 'Release date': release date of deep space scan\n- 'Size': size of original image\n- 'Name': name of celestial entities present in image\n- 'Distance': distance from celestial entities present in image\n- 'Constellation': constellation of celestial entities present in image\n- 'Category': category of celestial entities present in image\n- 'Position (RA)': coordinates for deep space scan used by Hubble telescope\n- 'Position (Dec)': coordinates for deep space scan used by Hubble telescope\n- 'Field of view': coordinates for deep space scan used by Hubble telescope\n- 'Orientation': coordinates for deep space scan used by Hubble telescope\n- 'width': width of image, same if the image did not need to be cropped, but otherwise could differ from 'Size'\n- 'height': height of image, same if the image did not need to be cropped, but otherwise could differ from 'Size'\n- 'file_size': 'width' x 'height' x 3 bytes, used to estimate size of raw images\n- 'crop_w': width starting point of image if cropped, otherwise 0\n- 'crop_h': height starting point of image if cropped, otherwise 0\n- 'cropped': whether this image needed to be cropped or not",
"passage: ### Data Splits\n\nThe data is only provided in a single training split, as the purpose of the dataset is additional fine-tuning for the task of 'text-to-image' generation.## Dataset Creation### Curation Rationale\n\nThe ESA Hubble Deep Space Images & Captions dataset was built to provide ease of access to extremely high-quality Hubble deep space scans.\nImages from the Hubble telescope have already inspired millions, and the hope is that this dataset can be used to create inspiring models and approaches to further push interest in space & cosmology.### Source Data#### Initial Data Collection\n\nAll images were collected from URL\nFullsize Original images & metadata were crawled from the ESA Hubble website using Scrapy.\nImages were downloaded as '.tiff' files, while\nadditional metadata was later collected for each image using the following code.\n\nAs the ESA Hubble website collects images from a wide variety of sources, images were filtered to try to avoid any non-space scan images as follows:\nThe ESA Hubble Advanced Image Search enables the following filtering parameters:\n\n- images with Minimum size greater than or equal to 400x300\n- Ranking greater than or equal to Fair or better\n- Type containing 'Observation'\n\nThis reduced significantly the number of images which had nothing to do with Hubble deep space scans.\nA total of around 3,000 space images were collected with this method.#### Filtering\n\nFurther automatic and manual filtering was performed to remove the following:\n\n- improperly classified images\n- space renders\n- diagrams with text\n- images of celestial bodies within our solar system\n- images with too low a resolution\n\nThis brought the total number of deep space images down to 2,593.\nThis process was not perfect, and there likely remain some images in the dataset that should be removed in the future."
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|
40818e91d4a73bc0e96fc16da7d64b54f80d4515 | # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_ns_20_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_ns_20_open_ended | [
"region:us"
]
| 2023-02-22T22:37:21+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 3172, "num_examples": 20}], "download_size": 3386, "dataset_size": 3172}} | 2023-02-22T22:37:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_ns_20_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_ns_20_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_ns_20_open_ended\"\n\nMore Information needed"
]
| [
6,
49
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_ns_20_open_ended\"\n\nMore Information needed"
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|
49b99a8de1585ab3ec4a815d53b769e5495bf463 | # Dataset Card for "wikisource-yellow"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Zombely/wikisource-yellow | [
"region:us"
]
| 2023-02-22T22:39:55+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train_1", "num_bytes": 12984998648.244, "num_examples": 9998}, {"name": "train_2", "num_bytes": 16071270493.0, "num_examples": 10000}, {"name": "train_3", "num_bytes": 15496290078.0, "num_examples": 10000}, {"name": "train_4", "num_bytes": 8549111534.0, "num_examples": 10000}, {"name": "train_5", "num_bytes": 13382018606.0, "num_examples": 10000}, {"name": "train_6", "num_bytes": 16871883641.979, "num_examples": 9959}, {"name": "train_7", "num_bytes": 15199574685.0, "num_examples": 10000}, {"name": "train_8", "num_bytes": 13887271412.0, "num_examples": 10000}, {"name": "train_9", "num_bytes": 15434064354.0, "num_examples": 10000}, {"name": "train_10", "num_bytes": 7874718803.82, "num_examples": 6969}, {"name": "validation", "num_bytes": 12645144007.93, "num_examples": 7745}], "download_size": 13454099590, "dataset_size": 131524462621.994}} | 2023-03-07T13:50:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikisource-yellow"
More Information needed | [
"# Dataset Card for \"wikisource-yellow\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikisource-yellow\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikisource-yellow\"\n\nMore Information needed"
]
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|
fb45b1f4523e45c512498ab8171968703bba4539 | # Dataset Card for "stackoverflow-python-with-meta-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | suriyagunasekar/stackoverflow-python-with-meta-data | [
"region:us"
]
| 2023-02-22T23:21:53+00:00 | {"dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_scores", "sequence": "int32"}, {"name": "non_answers", "sequence": "string"}, {"name": "non_answers_scores", "sequence": "int32"}, {"name": "tags", "sequence": "string"}, {"name": "name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9114535831, "num_examples": 1745972}], "download_size": 4753108665, "dataset_size": 9114535831}} | 2023-02-22T23:36:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "stackoverflow-python-with-meta-data"
More Information needed | [
"# Dataset Card for \"stackoverflow-python-with-meta-data\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"stackoverflow-python-with-meta-data\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"stackoverflow-python-with-meta-data\"\n\nMore Information needed"
]
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|
222aa8c5c676cb970c37f6446d5199c4e75d361b | # Dataset Card for "dwnews"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tillschwoerer/dwnews | [
"region:us"
]
| 2023-02-22T23:26:33+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Business", "1": "Catastrophe", "2": "Conflicts", "3": "Crime", "4": "Culture", "5": "Health", "6": "Nature and Environment", "7": "Politics", "8": "Society", "9": "Sports"}}}}], "splits": [{"name": "train", "num_bytes": 6675633, "num_examples": 1598}, {"name": "validation", "num_bytes": 807911, "num_examples": 200}, {"name": "test", "num_bytes": 807911, "num_examples": 200}], "download_size": 5058088, "dataset_size": 8291455}} | 2023-02-22T23:26:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dwnews"
More Information needed | [
"# Dataset Card for \"dwnews\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"dwnews\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"dwnews\"\n\nMore Information needed"
]
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|
4b551fcc2cab566b6a9174f1e979341112d02584 | # Dataset Card for "xsum_clean_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | venetis/xsum_clean_text | [
"region:us"
]
| 2023-02-22T23:56:36+00:00 | {"dataset_info": {"features": [{"name": "document", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 479206363, "num_examples": 204045}, {"name": "validation", "num_bytes": 26292877, "num_examples": 11332}, {"name": "test", "num_bytes": 26756141, "num_examples": 11334}], "download_size": 338049038, "dataset_size": 532255381}} | 2023-02-22T23:56:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "xsum_clean_text"
More Information needed | [
"# Dataset Card for \"xsum_clean_text\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"xsum_clean_text\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"xsum_clean_text\"\n\nMore Information needed"
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|
d4d16d7f8c78740d0032f214a18a461131cfc597 | # Dataset Card for "the-stack-smol-filtered-python-docstrings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | calum/the-stack-smol-python-docstrings | [
"region:us"
]
| 2023-02-23T01:04:12+00:00 | {"dataset_info": {"features": [{"name": "body", "dtype": "string"}, {"name": "body_hash", "dtype": "int64"}, {"name": "docstring", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "repository_name", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "body_without_docstring", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 33019111.239729874, "num_examples": 24616}], "download_size": 0, "dataset_size": 33019111.239729874}} | 2023-02-23T01:43:55+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "the-stack-smol-filtered-python-docstrings"
More Information needed | [
"# Dataset Card for \"the-stack-smol-filtered-python-docstrings\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"the-stack-smol-filtered-python-docstrings\"\n\nMore Information needed"
]
| [
6,
27
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"the-stack-smol-filtered-python-docstrings\"\n\nMore Information needed"
]
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|
b62eb16078c37471dae98030e0749bc1174e9dce | # Dataset Card for "commonsenseqa_with_content_words"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liujqian/commonsenseqa_with_content_words | [
"region:us"
]
| 2023-02-23T01:26:14+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_concept", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}, {"name": "question_content_words", "sequence": "string"}, {"name": "choice_0_content_words", "sequence": "string"}, {"name": "choice_1_content_words", "sequence": "string"}, {"name": "choice_2_content_words", "sequence": "string"}, {"name": "choice_3_content_words", "sequence": "string"}, {"name": "choice_4_content_words", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 3595329, "num_examples": 9741}, {"name": "validation", "num_bytes": 446090, "num_examples": 1221}, {"name": "test", "num_bytes": 419929, "num_examples": 1140}], "download_size": 2361458, "dataset_size": 4461348}} | 2023-02-24T01:21:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "commonsenseqa_with_content_words"
More Information needed | [
"# Dataset Card for \"commonsenseqa_with_content_words\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"commonsenseqa_with_content_words\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"commonsenseqa_with_content_words\"\n\nMore Information needed"
]
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|
3f0d3db9bd3f74672db5c96008ebf4ed2a8c5549 | # Dataset Card for "ms_marco_large_sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nschantz21/ms_marco_large_sample | [
"region:us"
]
| 2023-02-23T02:19:59+00:00 | {"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "qid", "dtype": "int64"}, {"name": "iteration", "dtype": "int64"}, {"name": "pid", "dtype": "int64"}, {"name": "relevancy", "dtype": "int64"}, {"name": "query", "dtype": "string"}, {"name": "passage", "dtype": "string"}, {"name": "passage_class", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 64530835, "num_examples": 53276}], "download_size": 34349985, "dataset_size": 64530835}} | 2023-02-23T02:20:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ms_marco_large_sample"
More Information needed | [
"# Dataset Card for \"ms_marco_large_sample\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ms_marco_large_sample\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ms_marco_large_sample\"\n\nMore Information needed"
]
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|
24659faeb9d171f40e17d81306280475879b761a | # Dataset Card for "stackoverflow-with-meta-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | suriyagunasekar/stackoverflow-with-meta-data | [
"region:us"
]
| 2023-02-23T02:29:47+00:00 | {"dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_scores", "sequence": "int32"}, {"name": "non_answers", "sequence": "string"}, {"name": "non_answers_scores", "sequence": "int32"}, {"name": "tags", "sequence": "string"}, {"name": "name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 104739824581, "num_examples": 19904590}], "download_size": 0, "dataset_size": 104739824581}} | 2023-02-23T06:04:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "stackoverflow-with-meta-data"
More Information needed | [
"# Dataset Card for \"stackoverflow-with-meta-data\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"stackoverflow-with-meta-data\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"stackoverflow-with-meta-data\"\n\nMore Information needed"
]
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|
f6519abdebc57e49c73ea438b7b0f6ba41a1c114 | # Dataset Card for "XOM"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | thercyl/XOM | [
"region:us"
]
| 2023-02-23T02:36:40+00:00 | {"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "float64"}, {"name": "Ticker", "dtype": "string"}, {"name": "Year", "dtype": "string"}, {"name": "Text", "dtype": "string"}, {"name": "Embedding", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4309445, "num_examples": 131}], "download_size": 2623140, "dataset_size": 4309445}} | 2023-02-23T02:36:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "XOM"
More Information needed | [
"# Dataset Card for \"XOM\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"XOM\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"XOM\"\n\nMore Information needed"
]
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|
6adf6167b80edbae70456db7e3b42d57826cbbc7 | # Dataset Card for "UNH"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | thercyl/UNH | [
"region:us"
]
| 2023-02-23T02:41:59+00:00 | {"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "float64"}, {"name": "Ticker", "dtype": "string"}, {"name": "Year", "dtype": "string"}, {"name": "Text", "dtype": "string"}, {"name": "Embedding", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 72142710, "num_examples": 2069}], "download_size": 41968517, "dataset_size": 72142710}} | 2023-02-23T02:42:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "UNH"
More Information needed | [
"# Dataset Card for \"UNH\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"UNH\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"UNH\"\n\nMore Information needed"
]
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|
6658355c8302ecd97c0c3c3fb7aaed259e2ce74b | # Dataset Card for "JNJ"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | thercyl/JNJ | [
"region:us"
]
| 2023-02-23T02:44:46+00:00 | {"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "float64"}, {"name": "Ticker", "dtype": "string"}, {"name": "Year", "dtype": "string"}, {"name": "Text", "dtype": "string"}, {"name": "Embedding", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 41199241, "num_examples": 1185}], "download_size": 22949643, "dataset_size": 41199241}} | 2023-02-23T02:44:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "JNJ"
More Information needed | [
"# Dataset Card for \"JNJ\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"JNJ\"\n\nMore Information needed"
]
| [
6,
12
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"JNJ\"\n\nMore Information needed"
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|
d6990a4b60ae77e0fd9bb3d0aafa238ab8b7b573 | # Dataset Card for "VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_100_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_100_open_ended | [
"region:us"
]
| 2023-02-23T02:56:20+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_32", "num_bytes": 14423, "num_examples": 100}], "download_size": 8281, "dataset_size": 14423}} | 2023-02-23T02:56:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_100_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_100_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_100_open_ended\"\n\nMore Information needed"
]
| [
6,
49
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_100_open_ended\"\n\nMore Information needed"
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|
5ab5a9d7f5ee0bd4a56411bc3fc1dca151a4f308 | # Dataset Card for "cv.3.image_object_detection.detect_illustration"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lansinuote/cv.3.image_object_detection.detect_illustration | [
"region:us"
]
| 2023-02-23T02:57:14+00:00 | {"dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "list": [{"name": "category_id", "dtype": {"class_label": {"names": {"0": "early_printed_illustration"}}}}, {"name": "image_id", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "segmentation", "list": {"list": "float32"}}, {"name": "iscrowd", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 894127063.61973, "num_examples": 6800}, {"name": "test", "num_bytes": 25952722.812344998, "num_examples": 200}], "download_size": 0, "dataset_size": 920079786.432075}} | 2023-02-23T03:03:09+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cv.3.image_object_detection.detect_illustration"
More Information needed | [
"# Dataset Card for \"cv.3.image_object_detection.detect_illustration\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cv.3.image_object_detection.detect_illustration\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cv.3.image_object_detection.detect_illustration\"\n\nMore Information needed"
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|
a6f692bdf623a10012c94d95d5bda4e92610d9f7 | # Dataset Card for "cv.3.image_object_detection"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lansinuote/cv.3.image_object_detection | [
"region:us"
]
| 2023-02-23T02:57:22+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "digits", "sequence": [{"name": "bbox", "sequence": "int32", "length": 4}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1", "2": "2", "3": "3", "4": "4", "5": "5", "6": "6", "7": "7", "8": "8", "9": "9"}}}}]}], "splits": [{"name": "train", "num_bytes": 67463846.91850163, "num_examples": 6646}, {"name": "test", "num_bytes": 690276.6069133481, "num_examples": 68}], "download_size": 60342937, "dataset_size": 68154123.52541497}} | 2023-02-23T02:58:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cv.3.image_object_detection"
More Information needed | [
"# Dataset Card for \"cv.3.image_object_detection\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cv.3.image_object_detection\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cv.3.image_object_detection\"\n\nMore Information needed"
]
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2d7cc3065a785ff6b2a30abd6bac416b864528fc | # Dataset Card for "VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_200_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_200_open_ended | [
"region:us"
]
| 2023-02-23T03:02:10+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_32", "num_bytes": 29928, "num_examples": 200}], "download_size": 14434, "dataset_size": 29928}} | 2023-02-23T03:02:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_200_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_200_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_200_open_ended\"\n\nMore Information needed"
]
| [
6,
49
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_200_open_ended\"\n\nMore Information needed"
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|
83d6213934243ac22178af1550263d734d4a6178 | # Dataset Card for "VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_500_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_500_open_ended | [
"region:us"
]
| 2023-02-23T03:17:04+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 72950, "num_examples": 500}], "download_size": 28917, "dataset_size": 72950}} | 2023-02-23T03:17:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_500_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_500_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_500_open_ended\"\n\nMore Information needed"
]
| [
6,
50
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_500_open_ended\"\n\nMore Information needed"
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|
3d97b383d844bca2affd943b21099599f7840490 | # Dataset Card for "au.1.audio_classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lansinuote/au.1.audio_classification | [
"region:us"
]
| 2023-02-23T03:18:16+00:00 | {"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "yes", "1": "no", "2": "up", "3": "down", "4": "left", "5": "right", "6": "on", "7": "off", "8": "stop", "9": "go", "10": "_silence_", "11": "_unknown_"}}}}, {"name": "input_values", "sequence": "float32"}, {"name": "attention_mask", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 3018873312, "num_examples": 23582}, {"name": "test", "num_bytes": 12801600, "num_examples": 100}], "download_size": 1130643070, "dataset_size": 3031674912}} | 2023-02-23T05:41:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "au.1.audio_classification"
More Information needed | [
"# Dataset Card for \"au.1.audio_classification\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"au.1.audio_classification\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"au.1.audio_classification\"\n\nMore Information needed"
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|
a7cef05ebf01331f5df30e4ec6fc848bcccf1e85 | # Dataset Card for "META"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | thercyl/META | [
"region:us"
]
| 2023-02-23T03:21:10+00:00 | {"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "float64"}, {"name": "Ticker", "dtype": "string"}, {"name": "Year", "dtype": "string"}, {"name": "Text", "dtype": "string"}, {"name": "Embedding", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 86462290, "num_examples": 2473}], "download_size": 48256052, "dataset_size": 86462290}} | 2023-02-23T03:21:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "META"
More Information needed | [
"# Dataset Card for \"META\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"META\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"META\"\n\nMore Information needed"
]
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27c05fbe349950c8661ac8a375879553eb9e67b1 | # Dataset Card for "VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_500_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_500_open_ended | [
"region:us"
]
| 2023-02-23T03:23:21+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_32", "num_bytes": 73169, "num_examples": 500}], "download_size": 29281, "dataset_size": 73169}} | 2023-02-23T03:23:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_500_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_500_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_500_open_ended\"\n\nMore Information needed"
]
| [
6,
49
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_500_open_ended\"\n\nMore Information needed"
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|
0d32717d8e43c059b3d1a20da65e9c664a33ee6a | # Dataset Card for "au.2.speech_recognition"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lansinuote/au.2.speech_recognition | [
"region:us"
]
| 2023-02-23T03:50:26+00:00 | {"dataset_info": {"features": [{"name": "transcription", "dtype": "string"}, {"name": "raw_speech", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 233406389.45149255, "num_examples": 482}, {"name": "test", "num_bytes": 26149263.548507463, "num_examples": 54}], "download_size": 260816894, "dataset_size": 259555653.0}} | 2023-02-23T10:32:05+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "au.2.speech_recognition"
More Information needed | [
"# Dataset Card for \"au.2.speech_recognition\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"au.2.speech_recognition\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"au.2.speech_recognition\"\n\nMore Information needed"
]
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|
3289c8fe06c36508e04f7d457e0be52d4966466c | # Dataset Card for "VQAv2_validation_facebook_opt_13b_mode_VQAv2_visclues_detection_ns_400_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_validation_facebook_opt_13b_mode_VQAv2_visclues_detection_ns_400_open_ended | [
"region:us"
]
| 2023-02-23T04:27:06+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_32", "num_bytes": 59835, "num_examples": 400}], "download_size": 0, "dataset_size": 59835}} | 2023-02-23T04:40:09+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_validation_facebook_opt_13b_mode_VQAv2_visclues_detection_ns_400_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_validation_facebook_opt_13b_mode_VQAv2_visclues_detection_ns_400_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_validation_facebook_opt_13b_mode_VQAv2_visclues_detection_ns_400_open_ended\"\n\nMore Information needed"
]
| [
6,
49
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_validation_facebook_opt_13b_mode_VQAv2_visclues_detection_ns_400_open_ended\"\n\nMore Information needed"
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|
fca9a9494565824c6c08a1a883c83b5b485c9cc5 |
dataset_info:
- features:
- name: labels
- dtype: class_label
- '0': Negative
- '1': Positive
- name: Review
- dtype: string
---
splits:
- name: train
- num_examples: 15000
- Positive : 50%
- Negative : 50%
- name: test
- num_examples: 1000
- Positive : 87.7%
- Negative : 17.3%
- The ratio of positive and negative data is the same as the ratio of the original data.
---
task_categories:
- text-classification
# Dataset Card for "review_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | seokwoni/review_subset | [
"region:us"
]
| 2023-02-23T05:03:41+00:00 | {} | 2023-02-23T05:46:22+00:00 | []
| []
| TAGS
#region-us
|
dataset_info:
- features:
- name: labels
- dtype: class_label
- '0': Negative
- '1': Positive
- name: Review
- dtype: string
---
splits:
- name: train
- num_examples: 15000
- Positive : 50%
- Negative : 50%
- name: test
- num_examples: 1000
- Positive : 87.7%
- Negative : 17.3%
- The ratio of positive and negative data is the same as the ratio of the original data.
---
task_categories:
- text-classification
# Dataset Card for "review_subset"
More Information needed | [
"# Dataset Card for \"review_subset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"review_subset\"\n\nMore Information needed"
]
| [
6,
14
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"review_subset\"\n\nMore Information needed"
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|
730a475851dc17466b4e6005de8f5a73da7ae188 |
A dataset of SD-generated images of anime neko characters vs. non_neko characters. | ongknsro/nekos-nonnekos | [
"task_categories:image-classification",
"size_categories:1K<n<10K",
"doi:10.57967/hf/1715",
"region:us"
]
| 2023-02-23T05:19:18+00:00 | {"size_categories": ["1K<n<10K"], "task_categories": ["image-classification"], "pretty_name": "neko or non_neko"} | 2023-05-19T08:30:59+00:00 | []
| []
| TAGS
#task_categories-image-classification #size_categories-1K<n<10K #doi-10.57967/hf/1715 #region-us
|
A dataset of SD-generated images of anime neko characters vs. non_neko characters. | []
| [
"TAGS\n#task_categories-image-classification #size_categories-1K<n<10K #doi-10.57967/hf/1715 #region-us \n"
]
| [
41
]
| [
"passage: TAGS\n#task_categories-image-classification #size_categories-1K<n<10K #doi-10.57967/hf/1715 #region-us \n"
]
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|
26b64629894a67ea0c7410f6e8bccbd686f4fbff | # Dataset Card for "speechocean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | siegels/speechocean | [
"region:us"
]
| 2023-02-23T05:49:09+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "file_id", "dtype": "int64"}, {"name": "accuracy", "dtype": "int64"}, {"name": "completeness", "dtype": "float64"}, {"name": "fluency", "dtype": "int64"}, {"name": "prosodic", "dtype": "int64"}, {"name": "words", "list": [{"name": "accuracy", "dtype": "int64"}, {"name": "phones", "sequence": "string"}, {"name": "phones-accuracy", "sequence": "float64"}, {"name": "stress", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "total", "dtype": "int64"}]}, {"name": "total", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "speaker_id", "dtype": "int64"}, {"name": "file_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 576478346.0, "num_examples": 5000}], "download_size": 611300052, "dataset_size": 576478346.0}} | 2023-02-23T05:50:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "speechocean"
More Information needed | [
"# Dataset Card for \"speechocean\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"speechocean\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"speechocean\"\n\nMore Information needed"
]
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|
69a35e273947b0b9f9c2969a9a93af0ac382248c | # Dataset Card for "simplewiki2023-minilml6v2-avgembeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lsb/simplewiki2023-minilml6v2-avgembeddings | [
"region:us"
]
| 2023-02-23T06:11:53+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "avg_embed", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 622429552, "num_examples": 225332}], "download_size": 328121680, "dataset_size": 622429552}} | 2023-02-23T06:17:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "simplewiki2023-minilml6v2-avgembeddings"
More Information needed | [
"# Dataset Card for \"simplewiki2023-minilml6v2-avgembeddings\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"simplewiki2023-minilml6v2-avgembeddings\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"simplewiki2023-minilml6v2-avgembeddings\"\n\nMore Information needed"
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|
e192c171229e7cf3fb43896c551d40058475d6a7 |
# Dataset Card for UTS_Text
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
The UTS_Text dataset is a collection of 100,000 sentences sourced from various news articles.
Out of the 10,000 sentences in the dataset, 5,000 sentences have a length ranging from 50 to 150, while the other 5,000 sentences have a length ranging from 20 to 50. This distribution of sentence lengths provides a diverse range of text samples that can be used to train and test natural language processing models.
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
| name | train | validation | test |
|---------|--------:|-----------:|-------:|
| small | 1600 | 200 | 200 |
| base | 8000 | 1000 | 1000 |
| large | 95000 | 2500 | 2500 |
## Dataset Creation
### Curation Rationale
### Source Data
### Annotations
## Additional Information
### Licensing Information
The dataset is released under Apache 2.0.
### Citation Information
### Contributions
| undertheseanlp/UTS_Text | [
"task_categories:text-generation",
"annotations_creators:no-annotation",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:vi",
"license:apache-2.0",
"region:us"
]
| 2023-02-23T06:19:46+00:00 | {"annotations_creators": ["no-annotation"], "language": ["vi"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "UTS_Text"} | 2023-03-03T03:29:39+00:00 | []
| [
"vi"
]
| TAGS
#task_categories-text-generation #annotations_creators-no-annotation #multilinguality-monolingual #size_categories-10K<n<100K #language-Vietnamese #license-apache-2.0 #region-us
| Dataset Card for UTS\_Text
==========================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
* Additional Information
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
The UTS\_Text dataset is a collection of 100,000 sentences sourced from various news articles.
Out of the 10,000 sentences in the dataset, 5,000 sentences have a length ranging from 50 to 150, while the other 5,000 sentences have a length ranging from 20 to 50. This distribution of sentence lengths provides a diverse range of text samples that can be used to train and test natural language processing models.
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
Dataset Structure
-----------------
### Data Instances
### Data Fields
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
### Annotations
Additional Information
----------------------
### Licensing Information
The dataset is released under Apache 2.0.
### Contributions
| [
"### Dataset Summary",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"### Annotations\n\n\nAdditional Information\n----------------------",
"### Licensing Information\n\n\nThe dataset is released under Apache 2.0.",
"### Contributions"
]
| [
"TAGS\n#task_categories-text-generation #annotations_creators-no-annotation #multilinguality-monolingual #size_categories-10K<n<100K #language-Vietnamese #license-apache-2.0 #region-us \n",
"### Dataset Summary",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"### Annotations\n\n\nAdditional Information\n----------------------",
"### Licensing Information\n\n\nThe dataset is released under Apache 2.0.",
"### Contributions"
]
| [
65,
6,
10,
11,
6,
5,
11,
7,
4,
12,
16,
5
]
| [
"passage: TAGS\n#task_categories-text-generation #annotations_creators-no-annotation #multilinguality-monolingual #size_categories-10K<n<100K #language-Vietnamese #license-apache-2.0 #region-us \n### Dataset Summary### Supported Tasks and Leaderboards### Languages\n\n\nDataset Structure\n-----------------### Data Instances### Data Fields### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data### Annotations\n\n\nAdditional Information\n----------------------### Licensing Information\n\n\nThe dataset is released under Apache 2.0.### Contributions"
]
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|
71bec5f33ec24000aed0fa8656a9adaa9a776f43 | # Dataset Card for "snli-french"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kseth919/snli-french | [
"size_categories:1M<n<10M",
"language:fr",
"nli",
"fnli",
"snli-french",
"region:us"
]
| 2023-02-23T06:58:34+00:00 | {"language": ["fr"], "size_categories": ["1M<n<10M"], "pretty_name": "SNLI-French", "dataset_info": {"features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 60781325, "num_examples": 549367}, {"name": "dev", "num_bytes": 1097461, "num_examples": 9842}, {"name": "test", "num_bytes": 1092127, "num_examples": 9824}], "download_size": 0, "dataset_size": 62970913}, "tags": ["nli", "fnli", "snli-french"]} | 2023-02-26T06:39:35+00:00 | []
| [
"fr"
]
| TAGS
#size_categories-1M<n<10M #language-French #nli #fnli #snli-french #region-us
| # Dataset Card for "snli-french"
More Information needed | [
"# Dataset Card for \"snli-french\"\n\nMore Information needed"
]
| [
"TAGS\n#size_categories-1M<n<10M #language-French #nli #fnli #snli-french #region-us \n",
"# Dataset Card for \"snli-french\"\n\nMore Information needed"
]
| [
38,
17
]
| [
"passage: TAGS\n#size_categories-1M<n<10M #language-French #nli #fnli #snli-french #region-us \n# Dataset Card for \"snli-french\"\n\nMore Information needed"
]
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|
c8e8ba7bc87bd371fe25ae6ec8231f080ee53065 | # Dataset Card for "review_subset_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | seokwoni/review_subset_test | [
"region:us"
]
| 2023-02-23T07:00:27+00:00 | {"dataset_info": {"features": [{"name": "labels", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "Review", "dtype": "string"}, {"name": "en_review", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 627736, "num_examples": 1000}, {"name": "train", "num_bytes": 627736, "num_examples": 1000}], "download_size": 769994, "dataset_size": 1255472}} | 2023-02-23T09:41:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "review_subset_test"
More Information needed | [
"# Dataset Card for \"review_subset_test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"review_subset_test\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"review_subset_test\"\n\nMore Information needed"
]
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|
38e97fe0c9f3b01a0d0bb762e9398e32b2269df8 | # Dataset Card for "instructpix2pix-1000-samples"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
The dataset was created using the code from [this repository](https://github.com/sayakpaul/instruct-pix2pix-dataset). | fusing/instructpix2pix-1000-samples | [
"region:us"
]
| 2023-02-23T07:05:45+00:00 | {"dataset_info": {"features": [{"name": "input_image", "dtype": "image"}, {"name": "edit_prompt", "dtype": "string"}, {"name": "edited_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 416880759.0, "num_examples": 1000}], "download_size": 416899514, "dataset_size": 416880759.0}} | 2023-02-23T07:08:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructpix2pix-1000-samples"
More Information needed
The dataset was created using the code from this repository. | [
"# Dataset Card for \"instructpix2pix-1000-samples\"\n\nMore Information needed\n\nThe dataset was created using the code from this repository."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructpix2pix-1000-samples\"\n\nMore Information needed\n\nThe dataset was created using the code from this repository."
]
| [
6,
34
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructpix2pix-1000-samples\"\n\nMore Information needed\n\nThe dataset was created using the code from this repository."
]
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|
87bbe4e14515fc8110f6ecd992ee07b52b161aed | # Dataset Card for "mari-russian-parallel-corpora"
```
@inproceedings{
title={Mari-Russian parallel corpora},
author={Andrei Chemyshev, Gennadii Sabantsev, Nadezhda Timofeeva, Vasilii Semenov},
year={2023}
}
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | AigizK/mari-russian-parallel-corpora | [
"task_categories:translation",
"language:mhr",
"language:ru",
"license:cc-by-4.0",
"region:us"
]
| 2023-02-23T07:06:37+00:00 | {"language": ["mhr", "ru"], "license": "cc-by-4.0", "task_categories": ["translation"], "dataset_info": {"features": [{"name": "mhr", "dtype": "string"}, {"name": "rus", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 79751117, "num_examples": 386707}], "download_size": 39195604, "dataset_size": 79751117}} | 2023-11-11T17:56:56+00:00 | []
| [
"mhr",
"ru"
]
| TAGS
#task_categories-translation #language-Eastern Mari #language-Russian #license-cc-by-4.0 #region-us
| # Dataset Card for "mari-russian-parallel-corpora"
More Information needed | [
"# Dataset Card for \"mari-russian-parallel-corpora\"\n\n\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-translation #language-Eastern Mari #language-Russian #license-cc-by-4.0 #region-us \n",
"# Dataset Card for \"mari-russian-parallel-corpora\"\n\n\n\nMore Information needed"
]
| [
36,
20
]
| [
"passage: TAGS\n#task_categories-translation #language-Eastern Mari #language-Russian #license-cc-by-4.0 #region-us \n# Dataset Card for \"mari-russian-parallel-corpora\"\n\n\n\nMore Information needed"
]
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|
70e3fa1915be9a8daebec5e840f20df9a8e18793 | AG's News Topic Classification Dataset
Version 3, Updated 09/09/2015
ORIGIN
AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .
The AG's news topic classification dataset is constructed by Xiang Zhang ([email protected]) from the dataset above. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
DESCRIPTION
The AG's news topic classification dataset is constructed by choosing 4 largest classes from the original corpus. Each class contains 30,000 training samples and 1,900 testing samples. The total number of training samples is 120,000 and testing 7,600.
The file classes.txt contains a list of classes corresponding to each label.
The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 3 columns in them, corresponding to class index (1 to 4), title and description. The title and description are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".
CLASS NAME INFORMATION
1: World
2: Sports
3: Business
4: Sci/Tech
JSONL FORMAT
Instead of preserving csv format, I change the format to jsonl, which doesn't consider complicated rule about doublequote and escaping.
| sh0416/ag_news | [
"task_categories:text-classification",
"language:en",
"region:us"
]
| 2023-02-23T07:13:31+00:00 | {"language": ["en"], "task_categories": ["text-classification"]} | 2023-02-23T07:22:32+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #language-English #region-us
| AG's News Topic Classification Dataset
Version 3, Updated 09/09/2015
ORIGIN
AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link URL .
The AG's news topic classification dataset is constructed by Xiang Zhang (URL@URL) from the dataset above. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
DESCRIPTION
The AG's news topic classification dataset is constructed by choosing 4 largest classes from the original corpus. Each class contains 30,000 training samples and 1,900 testing samples. The total number of training samples is 120,000 and testing 7,600.
The file URL contains a list of classes corresponding to each label.
The files URL and URL contain all the training samples as comma-sparated values. There are 3 columns in them, corresponding to class index (1 to 4), title and description. The title and description are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".
CLASS NAME INFORMATION
1: World
2: Sports
3: Business
4: Sci/Tech
JSONL FORMAT
Instead of preserving csv format, I change the format to jsonl, which doesn't consider complicated rule about doublequote and escaping.
| []
| [
"TAGS\n#task_categories-text-classification #language-English #region-us \n"
]
| [
21
]
| [
"passage: TAGS\n#task_categories-text-classification #language-English #region-us \n"
]
| [
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|
69efd929dbee9cdae69094956c7a15647629ba16 | # Dataset Card for "diffusion.1.unconditional"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lansinuote/diffusion.1.unconditional | [
"region:us"
]
| 2023-02-23T07:19:40+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 346842007.375, "num_examples": 8189}], "download_size": 0, "dataset_size": 346842007.375}} | 2023-02-23T10:50:05+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "diffusion.1.unconditional"
More Information needed | [
"# Dataset Card for \"diffusion.1.unconditional\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"diffusion.1.unconditional\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"diffusion.1.unconditional\"\n\nMore Information needed"
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|
f68f4c4710f6487d78843948b86fbf7e74ea08eb | # Dataset Card for "monkey"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lrchang/monkey | [
"region:us"
]
| 2023-02-23T07:42:37+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11161808.0, "num_examples": 60}], "download_size": 11152774, "dataset_size": 11161808.0}} | 2023-02-23T08:04:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "monkey"
More Information needed | [
"# Dataset Card for \"monkey\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"monkey\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"monkey\"\n\nMore Information needed"
]
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|
bd6e16e0e820ed3e980b5556775f04f98ecb587d | # Dataset Card for "common_voice_11_0_kk"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Shirali/common_voice_11_0_kk | [
"region:us"
]
| 2023-02-23T08:05:21+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 23176127.0, "num_examples": 794}, {"name": "test", "num_bytes": 11462386.0, "num_examples": 375}], "download_size": 33618293, "dataset_size": 34638513.0}} | 2023-02-23T08:10:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "common_voice_11_0_kk"
More Information needed | [
"# Dataset Card for \"common_voice_11_0_kk\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"common_voice_11_0_kk\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"common_voice_11_0_kk\"\n\nMore Information needed"
]
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|
916e1d43c31d02cab1e05bd0c1a4dd8f0544722f | # Dataset Card for "monkey_complex"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lrchang/monkey_complex | [
"region:us"
]
| 2023-02-23T08:09:09+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11163134.0, "num_examples": 60}], "download_size": 11153796, "dataset_size": 11163134.0}} | 2023-02-23T08:20:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "monkey_complex"
More Information needed | [
"# Dataset Card for \"monkey_complex\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"monkey_complex\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"monkey_complex\"\n\nMore Information needed"
]
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|
2c1a5aed75bcab52d504a9ee595839e5e27c86ad |
# Dataset Card for SuperEURLEX
This dataset contains over 4.6M Legal Documents from EURLEX with Annotations.
Over 3.7M of this 4.6M documents are also available in HTML format.
This dataset can be used for pretraining language models as well as for testing them on legal text classification tasks.
Use this dataset as follows:
```python
from datasets import load_dataset
config = "0.DE" # {sector}.{lang}[.html]
dataset = load_dataset("ddrg/super_eurlex", config, split='train')
```
## Dataset Details
### Dataset Description
This Dataset was scrapped from [EURLEX](https://eur-lex.europa.eu/homepage.html).
It contains more than 4.6M Legal Documents in Plain Text and over 3.7M In HTML Format.
Those Documents are separated by their language (This Dataset includes a total of 24 official European Languages)
and by their Sector.
#### The Table below shows the number of documents per language:
| | Raw | HTML |
|---:|--------:|--------:|
| BG | 29,778 | 27,718 |
| CS | 94,439 | 91,754 |
| DA | 398,559 | 300,488 |
| DE | 384,179 | 265,724 |
| EL | 167,502 | 117,009 |
| EN | 456,212 | 354,186 |
| ES | 253,821 | 201,400 |
| ET | 142,183 | 139,690 |
| FI | 238,143 | 214,206 |
| FR | 427,011 | 305,592 |
| GA | 19,673 | 19,437 |
| HR | 37,200 | 35,944 |
| HU | 69,275 | 66,334 |
| IT | 358,637 | 259,936 |
| LT | 62,975 | 61,139 |
| LV | 105,433 | 102,105 |
| MT | 46,695 | 43,969 |
| NL | 345,276 | 237,366 |
| PL | 146,502 | 143,490 |
| PT | 369,571 | 314,148 |
| RO | 47,398 | 45,317 |
| SK | 100,718 | 98,192 |
| SL | 170,583 | 166,646 |
| SV | 172,926 | 148,656 |
- **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]
- **Repository:** https://huggingface.co/datasets/ddrg/super_eurlex/tree/main
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
### As Corpus for:
- **Pretraining of Language Models with self supervised tasks** like Masked Language Modeling and Next Sentence Prediction
- Legal Text Analysis
### As Dataset for evaluation on the following task:
- *eurovoc*-Concepts Prediction i.e. which tags apply? (Muli-Label Classification (large Scale))
- Example for this task is given[below
- *subject-matter* Prediction i.e. which other tags apply (Multi-Label Classification)
- *form* Classification i.e. What Kind of Document is it? (Multi-Class)
- And more
### Example for Use Of EUROVOC-Concepts
```python
from datasets import load_dataset
import transformers as tr
from sklearn.preprocessing import MultiLabelBinarizer
import numpy as np
import evaluate
import uuid
# ==================== #
# Prepare Data #
# ==================== #
CONFIG = "3.EN" # {sector}.{lang}[.html]
MODEL_NAME = "distilroberta-base"
dataset = load_dataset("ddrg/super_eurlex", CONFIG, split='train')
tokenizer = tr.AutoTokenizer.from_pretrained(MODEL_NAME)
# Remove Unlabeled Columns
def remove_nulls(batch):
return [(sample != None) for sample in batch["eurovoc"]]
dataset = dataset.filter(remove_nulls, batched=True, keep_in_memory=True)
# Tokenize Text
def tokenize(batch):
return tokenizer(batch["text_cleaned"], truncation=True, padding="max_length")
# Keep in Memory is optional (The Dataset is large though and can easily use up alot of memory)
dataset = dataset.map(tokenize, batched=True, keep_in_memory=True)
# Create Label Column by encoding Eurovoc Concepts
encoder = MultiLabelBinarizer()
# List of all Possible Labels
eurovoc_concepts = dataset["eurovoc"]
encoder.fit(eurovoc_concepts)
def encode_labels(batch):
batch["label"] = encoder.transform(batch["eurovoc"])
return batch
dataset = dataset.map(encode_labels, batched=True, keep_in_memory=True)
# Split into train and Test set
dataset = dataset.train_test_split(0.2)
# ==================== #
# Load & Train Model #
# ==================== #
model = tr.AutoModelForSequenceClassification.from_pretrained(
MODEL_NAME,
num_labels=len(encoder.classes_),
problem_type="multi_label_classification",
)
metric = evaluate.load("JP-SystemsX/nDCG", experiment_id=uuid.uuid4())
def compute_metric(eval_pred):
predictions, labels = eval_pred
return metric.compute(predictions=predictions, references=labels, k=5)
# Set Hyperparameter
# Note: We stay mostly with default values to keep example short
# Though more hyperparameter should be set and tuned in praxis
train_args = tr.TrainingArguments(
output_dir="./cache",
per_device_train_batch_size=16,
num_train_epochs=20
)
trainer = tr.Trainer(
model=model,
args=train_args,
train_dataset=dataset["train"],
compute_metrics=compute_metric,
)
trainer.train() # This will take a while
print(trainer.evaluate(dataset["test"]))
# >>> {'eval_loss': 0.0018887673504650593, 'eval_nDCG@5': 0.8072531683578489, 'eval_runtime': 663.8582, 'eval_samples_per_second': 32.373, 'eval_steps_per_second': 4.048, 'epoch': 20.0}
```
### 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 dataset is divided into multiple split by _Sector x Language x Format_
Sector refers to the kind of Document it belongs to:
- **0:** Consolidated acts
- **1:** Treaties
- **2:** International agreements
- **3:** Legislation
- **4:** Complementary legislation
- **5:** Preparatory acts and working documents
- **6:** Case-law
- **7:** National transposition measures
- **8:** References to national case-law concerning EU law
- **9:** Parliamentary questions
- **C:** Other documents published in the Official Journal C series
- **E:** EFTA documents
Language refers to each of the 24 official European Languages that were included at the date of the dataset creation:
- BG ~ Bulgarian
- CS ~ Czech
- DA ~ Danish
- DE ~ German
- EL ~ Greek
- EN ~ English
- ES ~ Spanish
- ET ~ Estonian
- FI ~ Finnish
- FR ~ French
- GA ~ Irish
- HR ~ Croatian
- HU ~ Hungarian
- IT ~ Italian
- LT ~ Lithuanian
- LV ~ Latvian
- MT ~ Maltese
- NL ~ Dutch
- PL ~ Polish
- PT ~ Portuguese
- RO ~ Romanian
- SK ~ Slovak
- SL ~ Slovenian
- SV ~ Swedish
Format refers to plain Text (default) or HTML format (.html)
> Note: Plain Text contains generally more documents because not all documents were available in HTML format but those that were are included in both formats
Those Splits are named the following way:
`{sector}.{lang}[.html]`
For Example:
- `3.EN` would be English legislative documents in plain text format
- `3.EN.html` would be the same in HTML Format
Each _Sector_ has its own set of meta data:
<details><summary>Sector 0 (Consolidated acts)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
</p>
</details>
<details><summary>Sector 1 (Treaties)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
- _current_consolidated_version_ ~ date when this version of the document was consolidated `Format DD/MM/YYYY`
- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
</p>
</details>
<details><summary>Sector 2 (International agreements)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
- _latest_consolidated_version_ ~ `Format DD/MM/YYYY`
- _current_consolidated_version_ ~ `Format DD/MM/YYYY`
</p>
</details>
<details><summary>Sector 3 (Legislation)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
- _latest_consolidated_version_ ~ `Format DD/MM/YYYY`
- _current_consolidated_version_ ~ `Format DD/MM/YYYY`
</p>
</details>
<details><summary>Sector 4 (Complementary legislation)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
- _latest_consolidated_version_ ~ `Format DD/MM/YYYY`
- _current_consolidated_version_ ~ `Format DD/MM/YYYY`
</p>
</details>
<details><summary>Sector 5 (Preparatory acts and working documents)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
- _latest_consolidated_version_ ~ `Format DD/MM/YYYY`
</p>
</details>
<details><summary>Sector 6 (Case-law)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
- _case-law_directory_code_before_lisbon_ ~ Classification system used for case law before Treaty of Lisbon came into effect (2009), each code reflects a particular area of EU law
</p>
</details>
<details><summary>Sector 7 (National transposition measures)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _transposed_legal_acts_ ~ national laws that exist in EU member states as a direct result of the need to comply with EU directives
</p>
</details>
<details><summary>Sector 8 (References to national case-law concerning EU law)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _case-law_directory_code_before_lisbon_ ~ Classification system used for case law before Treaty of Lisbon came into effect (2009), each code reflects a particular area of EU law
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
</p>
</details>
<details><summary>Sector 9 (Parliamentary questions)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
</p>
</details>
<details><summary>Sector C (Other documents published in the Official Journal C series)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
</p>
</details>
<details><summary>Sector E (EFTA documents)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
</p>
</details>
## Dataset Creation
### Curation Rationale
This dataset was created for the creation and/or evaluation of pretrained Legal Language Models.
### Source Data
#### Data Collection and Processing
We used the [EURLEX-Web-Scrapper Repo](https://github.com/JP-SystemsX/Eurlex-Web-Scrapper) for the data collection process.
#### Who are the source data producers?
The Source data stems from the [EURLEX-Website](https://eur-lex.europa.eu/) and was therefore produced by various entities within the European Union
#### Personal and Sensitive Information
No Personal or Sensitive Information is included to the best of our knowledge.
## Bias, Risks, and Limitations
- We removed HTML documents from which we couldn't extract plain text under the assumption that those are **corrupted files**.
However, we can't guarantee that we removed all.
- The Extraction of plain text from legal HTML documents can lead to **formatting issues**
e.g. the extraction of text from tables might mix up the order such that it becomes nearly incomprehensible.
- This dataset might contain many **missing values** in the meta-data columns as not every document was annotated in the same way
[More Information Needed]
### Recommendations
- Consider Removing rows with missing values for the task before training a model on it
## 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] | ddrg/super_eurlex | [
"task_categories:text-classification",
"task_categories:fill-mask",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:ga",
"language:hr",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"license:mit",
"legal documents",
"corpus",
"eurlex",
"html",
"region:us"
]
| 2023-02-23T08:14:37+00:00 | {"annotations_creators": ["found"], "language_creators": ["found"], "language": ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv"], "license": ["mit"], "multilinguality": ["multilingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-classification", "fill-mask"], "task_ids": ["multi-class-classification", "multi-label-classification"], "pretty_name": "SuperEURLEX: A Corpus of Plain Text and HTML from EURLEX, Annotated for multiple Legal Domain Text Classification Tasks.", "tags": ["legal documents", "corpus", "eurlex", "html"]} | 2023-11-14T06:18:46+00:00 | []
| [
"bg",
"cs",
"da",
"de",
"el",
"en",
"es",
"et",
"fi",
"fr",
"ga",
"hr",
"hu",
"it",
"lt",
"lv",
"mt",
"nl",
"pl",
"pt",
"ro",
"sk",
"sl",
"sv"
]
| TAGS
#task_categories-text-classification #task_categories-fill-mask #task_ids-multi-class-classification #task_ids-multi-label-classification #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-original #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Croatian #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Slovak #language-Slovenian #language-Swedish #license-mit #legal documents #corpus #eurlex #html #region-us
| Dataset Card for SuperEURLEX
============================
This dataset contains over 4.6M Legal Documents from EURLEX with Annotations.
Over 3.7M of this 4.6M documents are also available in HTML format.
This dataset can be used for pretraining language models as well as for testing them on legal text classification tasks.
Use this dataset as follows:
Dataset Details
---------------
### Dataset Description
This Dataset was scrapped from EURLEX.
It contains more than 4.6M Legal Documents in Plain Text and over 3.7M In HTML Format.
Those Documents are separated by their language (This Dataset includes a total of 24 official European Languages)
and by their Sector.
#### The Table below shows the number of documents per language:
* Curated by:
* Funded by [optional]:
* Shared by [optional]:
* Language(s) (NLP):
* License:
### Dataset Sources [optional]
* Repository: URL
* Paper [optional]:
* Demo [optional]:
Uses
----
### As Corpus for:
* Pretraining of Language Models with self supervised tasks like Masked Language Modeling and Next Sentence Prediction
* Legal Text Analysis
### As Dataset for evaluation on the following task:
* *eurovoc*-Concepts Prediction i.e. which tags apply? (Muli-Label Classification (large Scale))
+ Example for this task is given[below
* *subject-matter* Prediction i.e. which other tags apply (Multi-Label Classification)
* *form* Classification i.e. What Kind of Document is it? (Multi-Class)
* And more
### Example for Use Of EUROVOC-Concepts
### Out-of-Scope Use
Dataset Structure
-----------------
This dataset is divided into multiple split by *Sector x Language x Format*
Sector refers to the kind of Document it belongs to:
* 0: Consolidated acts
* 1: Treaties
* 2: International agreements
* 3: Legislation
* 4: Complementary legislation
* 5: Preparatory acts and working documents
* 6: Case-law
* 7: National transposition measures
* 8: References to national case-law concerning EU law
* 9: Parliamentary questions
* C: Other documents published in the Official Journal C series
* E: EFTA documents
Language refers to each of the 24 official European Languages that were included at the date of the dataset creation:
* BG ~ Bulgarian
* CS ~ Czech
* DA ~ Danish
* DE ~ German
* EL ~ Greek
* EN ~ English
* ES ~ Spanish
* ET ~ Estonian
* FI ~ Finnish
* FR ~ French
* GA ~ Irish
* HR ~ Croatian
* HU ~ Hungarian
* IT ~ Italian
* LT ~ Lithuanian
* LV ~ Latvian
* MT ~ Maltese
* NL ~ Dutch
* PL ~ Polish
* PT ~ Portuguese
* RO ~ Romanian
* SK ~ Slovak
* SL ~ Slovenian
* SV ~ Swedish
Format refers to plain Text (default) or HTML format (.html)
>
> Note: Plain Text contains generally more documents because not all documents were available in HTML format but those that were are included in both formats
>
>
>
Those Splits are named the following way:
'{sector}.{lang}[.html]'
For Example:
* '3.EN' would be English legislative documents in plain text format
* '3.URL' would be the same in HTML Format
Each *Sector* has its own set of meta data:
Sector 0 (Consolidated acts)
* *celex\_id* ~ Unique Identifier for each document
* *text\_cleaned* (Plain Text) or *text\_html\_raw* (HTML Format)
* *form* ~ Kind of Document e.g. Consolidated text, or Treaty
Sector 1 (Treaties)
* *celex\_id* ~ Unique Identifier for each document
* *text\_cleaned* (Plain Text) or *text\_html\_raw* (HTML Format)
* *form* ~ Kind of Document e.g. Consolidated text, or Treaty
* *subject\_matter* ~ Keywords that provide general overview of content in a document see here for more information
* *current\_consolidated\_version* ~ date when this version of the document was consolidated 'Format DD/MM/YYYY'
* *directory\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''
* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information
Sector 2 (International agreements)
* *celex\_id* ~ Unique Identifier for each document
* *text\_cleaned* (Plain Text) or *text\_html\_raw* (HTML Format)
* *form* ~ Kind of Document e.g. Consolidated text, or Treaty
* *directory\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''
* *subject\_matter* ~ Keywords that provide general overview of content in a document see here for more information
* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information
* *latest\_consolidated\_version* ~ 'Format DD/MM/YYYY'
* *current\_consolidated\_version* ~ 'Format DD/MM/YYYY'
Sector 3 (Legislation)
* *celex\_id* ~ Unique Identifier for each document
* *text\_cleaned* (Plain Text) or *text\_html\_raw* (HTML Format)
* *form* ~ Kind of Document e.g. Consolidated text, or Treaty
* *directory\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''
* *subject\_matter* ~ Keywords that provide general overview of content in a document see here for more information
* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information
* *latest\_consolidated\_version* ~ 'Format DD/MM/YYYY'
* *current\_consolidated\_version* ~ 'Format DD/MM/YYYY'
Sector 4 (Complementary legislation)
* *celex\_id* ~ Unique Identifier for each document
* *text\_cleaned* (Plain Text) or *text\_html\_raw* (HTML Format)
* *form* ~ Kind of Document e.g. Consolidated text, or Treaty
* *directory\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''
* *subject\_matter* ~ Keywords that provide general overview of content in a document see here for more information
* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information
* *latest\_consolidated\_version* ~ 'Format DD/MM/YYYY'
* *current\_consolidated\_version* ~ 'Format DD/MM/YYYY'
Sector 5 (Preparatory acts and working documents)
* *celex\_id* ~ Unique Identifier for each document
* *text\_cleaned* (Plain Text) or *text\_html\_raw* (HTML Format)
* *form* ~ Kind of Document e.g. Consolidated text, or Treaty
* *directory\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''
* *subject\_matter* ~ Keywords that provide general overview of content in a document see here for more information
* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information
* *latest\_consolidated\_version* ~ 'Format DD/MM/YYYY'
Sector 6 (Case-law)
* *celex\_id* ~ Unique Identifier for each document
* *text\_cleaned* (Plain Text) or *text\_html\_raw* (HTML Format)
* *form* ~ Kind of Document e.g. Consolidated text, or Treaty
* *directory\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''
* *subject\_matter* ~ Keywords that provide general overview of content in a document see here for more information
* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information
* *case-law\_directory\_code\_before\_lisbon* ~ Classification system used for case law before Treaty of Lisbon came into effect (2009), each code reflects a particular area of EU law
Sector 7 (National transposition measures)
* *celex\_id* ~ Unique Identifier for each document
* *text\_cleaned* (Plain Text) or *text\_html\_raw* (HTML Format)
* *form* ~ Kind of Document e.g. Consolidated text, or Treaty
* *transposed\_legal\_acts* ~ national laws that exist in EU member states as a direct result of the need to comply with EU directives
Sector 8 (References to national case-law concerning EU law)
* *celex\_id* ~ Unique Identifier for each document
* *text\_cleaned* (Plain Text) or *text\_html\_raw* (HTML Format)
* *form* ~ Kind of Document e.g. Consolidated text, or Treaty
* *case-law\_directory\_code\_before\_lisbon* ~ Classification system used for case law before Treaty of Lisbon came into effect (2009), each code reflects a particular area of EU law
* *subject\_matter* ~ Keywords that provide general overview of content in a document see here for more information
Sector 9 (Parliamentary questions)
* *celex\_id* ~ Unique Identifier for each document
* *text\_cleaned* (Plain Text) or *text\_html\_raw* (HTML Format)
* *form* ~ Kind of Document e.g. Consolidated text, or Treaty
* *directory\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''
* *subject\_matter* ~ Keywords that provide general overview of content in a document see here for more information
* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information
Sector C (Other documents published in the Official Journal C series)
* *celex\_id* ~ Unique Identifier for each document
* *text\_cleaned* (Plain Text) or *text\_html\_raw* (HTML Format)
* *form* ~ Kind of Document e.g. Consolidated text, or Treaty
* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information
Sector E (EFTA documents)
* *celex\_id* ~ Unique Identifier for each document
* *text\_cleaned* (Plain Text) or *text\_html\_raw* (HTML Format)
* *form* ~ Kind of Document e.g. Consolidated text, or Treaty
* *directory\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''
* *subject\_matter* ~ Keywords that provide general overview of content in a document see here for more information
* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information
Dataset Creation
----------------
### Curation Rationale
This dataset was created for the creation and/or evaluation of pretrained Legal Language Models.
### Source Data
#### Data Collection and Processing
We used the EURLEX-Web-Scrapper Repo for the data collection process.
#### Who are the source data producers?
The Source data stems from the EURLEX-Website and was therefore produced by various entities within the European Union
#### Personal and Sensitive Information
No Personal or Sensitive Information is included to the best of our knowledge.
Bias, Risks, and Limitations
----------------------------
* We removed HTML documents from which we couldn't extract plain text under the assumption that those are corrupted files.
However, we can't guarantee that we removed all.
* The Extraction of plain text from legal HTML documents can lead to formatting issues
e.g. the extraction of text from tables might mix up the order such that it becomes nearly incomprehensible.
* This dataset might contain many missing values in the meta-data columns as not every document was annotated in the same way
### Recommendations
* Consider Removing rows with missing values for the task before training a model on it
[optional]
BibTeX:
APA:
Glossary [optional]
-------------------
More Information [optional]
---------------------------
Dataset Card Authors [optional]
-------------------------------
Dataset Card Contact
--------------------
| [
"### Dataset Description\n\n\nThis Dataset was scrapped from EURLEX.\nIt contains more than 4.6M Legal Documents in Plain Text and over 3.7M In HTML Format.\nThose Documents are separated by their language (This Dataset includes a total of 24 official European Languages)\nand by their Sector.",
"#### The Table below shows the number of documents per language:\n\n\n\n* Curated by:\n* Funded by [optional]:\n* Shared by [optional]:\n* Language(s) (NLP):\n* License:",
"### Dataset Sources [optional]\n\n\n* Repository: URL\n* Paper [optional]:\n* Demo [optional]:\n\n\nUses\n----",
"### As Corpus for:\n\n\n* Pretraining of Language Models with self supervised tasks like Masked Language Modeling and Next Sentence Prediction\n* Legal Text Analysis",
"### As Dataset for evaluation on the following task:\n\n\n* *eurovoc*-Concepts Prediction i.e. which tags apply? (Muli-Label Classification (large Scale))\n\t+ Example for this task is given[below\n* *subject-matter* Prediction i.e. which other tags apply (Multi-Label Classification)\n* *form* Classification i.e. What Kind of Document is it? (Multi-Class)\n* And more",
"### Example for Use Of EUROVOC-Concepts",
"### Out-of-Scope Use\n\n\nDataset Structure\n-----------------\n\n\nThis dataset is divided into multiple split by *Sector x Language x Format*\n\n\nSector refers to the kind of Document it belongs to:\n\n\n* 0: Consolidated acts\n* 1: Treaties\n* 2: International agreements\n* 3: Legislation\n* 4: Complementary legislation\n* 5: Preparatory acts and working documents\n* 6: Case-law\n* 7: National transposition measures\n* 8: References to national case-law concerning EU law\n* 9: Parliamentary questions\n* C: Other documents published in the Official Journal C series\n* E: EFTA documents\n\n\nLanguage refers to each of the 24 official European Languages that were included at the date of the dataset creation:\n\n\n* BG ~ Bulgarian\n* CS ~ Czech\n* DA ~ Danish\n* DE ~ German\n* EL ~ Greek\n* EN ~ English\n* ES ~ Spanish\n* ET ~ Estonian\n* FI ~ Finnish\n* FR ~ French\n* GA ~ Irish\n* HR ~ Croatian\n* HU ~ Hungarian\n* IT ~ Italian\n* LT ~ Lithuanian\n* LV ~ Latvian\n* MT ~ Maltese\n* NL ~ Dutch\n* PL ~ Polish\n* PT ~ Portuguese\n* RO ~ Romanian\n* SK ~ Slovak\n* SL ~ Slovenian\n* SV ~ Swedish\n\n\nFormat refers to plain Text (default) or HTML format (.html)\n\n\n\n> \n> Note: Plain Text contains generally more documents because not all documents were available in HTML format but those that were are included in both formats\n> \n> \n> \n\n\nThose Splits are named the following way:\n'{sector}.{lang}[.html]'\n\n\nFor Example:\n\n\n* '3.EN' would be English legislative documents in plain text format\n* '3.URL' would be the same in HTML Format\n\n\nEach *Sector* has its own set of meta data:\n\n\nSector 0 (Consolidated acts)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n\n\n\n\n\n\nSector 1 (Treaties)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n* *current\\_consolidated\\_version* ~ date when this version of the document was consolidated 'Format DD/MM/YYYY'\n* *directory\\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n\n\n\n\n\n\nSector 2 (International agreements)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *directory\\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n* *latest\\_consolidated\\_version* ~ 'Format DD/MM/YYYY'\n* *current\\_consolidated\\_version* ~ 'Format DD/MM/YYYY'\n\n\n\n\n\n\nSector 3 (Legislation)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *directory\\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n* *latest\\_consolidated\\_version* ~ 'Format DD/MM/YYYY'\n* *current\\_consolidated\\_version* ~ 'Format DD/MM/YYYY'\n\n\n\n\n\n\nSector 4 (Complementary legislation)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *directory\\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n* *latest\\_consolidated\\_version* ~ 'Format DD/MM/YYYY'\n* *current\\_consolidated\\_version* ~ 'Format DD/MM/YYYY'\n\n\n\n\n\n\nSector 5 (Preparatory acts and working documents)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *directory\\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n* *latest\\_consolidated\\_version* ~ 'Format DD/MM/YYYY'\n\n\n\n\n\n\nSector 6 (Case-law)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *directory\\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n* *case-law\\_directory\\_code\\_before\\_lisbon* ~ Classification system used for case law before Treaty of Lisbon came into effect (2009), each code reflects a particular area of EU law\n\n\n\n\n\n\nSector 7 (National transposition measures)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *transposed\\_legal\\_acts* ~ national laws that exist in EU member states as a direct result of the need to comply with EU directives\n\n\n\n\n\n\nSector 8 (References to national case-law concerning EU law)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *case-law\\_directory\\_code\\_before\\_lisbon* ~ Classification system used for case law before Treaty of Lisbon came into effect (2009), each code reflects a particular area of EU law\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n\n\n\n\n\n\nSector 9 (Parliamentary questions)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *directory\\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n\n\n\n\n\n\nSector C (Other documents published in the Official Journal C series)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n\n\n\n\n\n\nSector E (EFTA documents)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *directory\\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n\n\n\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nThis dataset was created for the creation and/or evaluation of pretrained Legal Language Models.",
"### Source Data",
"#### Data Collection and Processing\n\n\nWe used the EURLEX-Web-Scrapper Repo for the data collection process.",
"#### Who are the source data producers?\n\n\nThe Source data stems from the EURLEX-Website and was therefore produced by various entities within the European Union",
"#### Personal and Sensitive Information\n\n\nNo Personal or Sensitive Information is included to the best of our knowledge.\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\n* We removed HTML documents from which we couldn't extract plain text under the assumption that those are corrupted files.\nHowever, we can't guarantee that we removed all.\n* The Extraction of plain text from legal HTML documents can lead to formatting issues\ne.g. the extraction of text from tables might mix up the order such that it becomes nearly incomprehensible.\n* This dataset might contain many missing values in the meta-data columns as not every document was annotated in the same way",
"### Recommendations\n\n\n* Consider Removing rows with missing values for the task before training a model on it\n\n\n[optional]\n\n\nBibTeX:\n\n\nAPA:\n\n\nGlossary [optional]\n-------------------\n\n\nMore Information [optional]\n---------------------------\n\n\nDataset Card Authors [optional]\n-------------------------------\n\n\nDataset Card Contact\n--------------------"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-fill-mask #task_ids-multi-class-classification #task_ids-multi-label-classification #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-original #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Croatian #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Slovak #language-Slovenian #language-Swedish #license-mit #legal documents #corpus #eurlex #html #region-us \n",
"### Dataset Description\n\n\nThis Dataset was scrapped from EURLEX.\nIt contains more than 4.6M Legal Documents in Plain Text and over 3.7M In HTML Format.\nThose Documents are separated by their language (This Dataset includes a total of 24 official European Languages)\nand by their Sector.",
"#### The Table below shows the number of documents per language:\n\n\n\n* Curated by:\n* Funded by [optional]:\n* Shared by [optional]:\n* Language(s) (NLP):\n* License:",
"### Dataset Sources [optional]\n\n\n* Repository: URL\n* Paper [optional]:\n* Demo [optional]:\n\n\nUses\n----",
"### As Corpus for:\n\n\n* Pretraining of Language Models with self supervised tasks like Masked Language Modeling and Next Sentence Prediction\n* Legal Text Analysis",
"### As Dataset for evaluation on the following task:\n\n\n* *eurovoc*-Concepts Prediction i.e. which tags apply? (Muli-Label Classification (large Scale))\n\t+ Example for this task is given[below\n* *subject-matter* Prediction i.e. which other tags apply (Multi-Label Classification)\n* *form* Classification i.e. What Kind of Document is it? (Multi-Class)\n* And more",
"### Example for Use Of EUROVOC-Concepts",
"### Out-of-Scope Use\n\n\nDataset Structure\n-----------------\n\n\nThis dataset is divided into multiple split by *Sector x Language x Format*\n\n\nSector refers to the kind of Document it belongs to:\n\n\n* 0: Consolidated acts\n* 1: Treaties\n* 2: International agreements\n* 3: Legislation\n* 4: Complementary legislation\n* 5: Preparatory acts and working documents\n* 6: Case-law\n* 7: National transposition measures\n* 8: References to national case-law concerning EU law\n* 9: Parliamentary questions\n* C: Other documents published in the Official Journal C series\n* E: EFTA documents\n\n\nLanguage refers to each of the 24 official European Languages that were included at the date of the dataset creation:\n\n\n* BG ~ Bulgarian\n* CS ~ Czech\n* DA ~ Danish\n* DE ~ German\n* EL ~ Greek\n* EN ~ English\n* ES ~ Spanish\n* ET ~ Estonian\n* FI ~ Finnish\n* FR ~ French\n* GA ~ Irish\n* HR ~ Croatian\n* HU ~ Hungarian\n* IT ~ Italian\n* LT ~ Lithuanian\n* LV ~ Latvian\n* MT ~ Maltese\n* NL ~ Dutch\n* PL ~ Polish\n* PT ~ Portuguese\n* RO ~ Romanian\n* SK ~ Slovak\n* SL ~ Slovenian\n* SV ~ Swedish\n\n\nFormat refers to plain Text (default) or HTML format (.html)\n\n\n\n> \n> Note: Plain Text contains generally more documents because not all documents were available in HTML format but those that were are included in both formats\n> \n> \n> \n\n\nThose Splits are named the following way:\n'{sector}.{lang}[.html]'\n\n\nFor Example:\n\n\n* '3.EN' would be English legislative documents in plain text format\n* '3.URL' would be the same in HTML Format\n\n\nEach *Sector* has its own set of meta data:\n\n\nSector 0 (Consolidated acts)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n\n\n\n\n\n\nSector 1 (Treaties)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n* *current\\_consolidated\\_version* ~ date when this version of the document was consolidated 'Format DD/MM/YYYY'\n* *directory\\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n\n\n\n\n\n\nSector 2 (International agreements)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *directory\\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n* *latest\\_consolidated\\_version* ~ 'Format DD/MM/YYYY'\n* *current\\_consolidated\\_version* ~ 'Format DD/MM/YYYY'\n\n\n\n\n\n\nSector 3 (Legislation)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *directory\\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n* *latest\\_consolidated\\_version* ~ 'Format DD/MM/YYYY'\n* *current\\_consolidated\\_version* ~ 'Format DD/MM/YYYY'\n\n\n\n\n\n\nSector 4 (Complementary legislation)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *directory\\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n* *latest\\_consolidated\\_version* ~ 'Format DD/MM/YYYY'\n* *current\\_consolidated\\_version* ~ 'Format DD/MM/YYYY'\n\n\n\n\n\n\nSector 5 (Preparatory acts and working documents)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *directory\\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n* *latest\\_consolidated\\_version* ~ 'Format DD/MM/YYYY'\n\n\n\n\n\n\nSector 6 (Case-law)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *directory\\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n* *case-law\\_directory\\_code\\_before\\_lisbon* ~ Classification system used for case law before Treaty of Lisbon came into effect (2009), each code reflects a particular area of EU law\n\n\n\n\n\n\nSector 7 (National transposition measures)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *transposed\\_legal\\_acts* ~ national laws that exist in EU member states as a direct result of the need to comply with EU directives\n\n\n\n\n\n\nSector 8 (References to national case-law concerning EU law)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *case-law\\_directory\\_code\\_before\\_lisbon* ~ Classification system used for case law before Treaty of Lisbon came into effect (2009), each code reflects a particular area of EU law\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n\n\n\n\n\n\nSector 9 (Parliamentary questions)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *directory\\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n\n\n\n\n\n\nSector C (Other documents published in the Official Journal C series)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n\n\n\n\n\n\nSector E (EFTA documents)\n* *celex\\_id* ~ Unique Identifier for each document\n* *text\\_cleaned* (Plain Text) or *text\\_html\\_raw* (HTML Format)\n* *form* ~ Kind of Document e.g. Consolidated text, or Treaty\n* *directory\\_code* ~ Information to structure documents in some kind of directory structure by topic e.g. ''03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics''\n* *subject\\_matter* ~ Keywords that provide general overview of content in a document see here for more information\n* *eurovoc* ~ Keywords that describe document content based on the European Vocabulary see here for more information\n\n\n\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nThis dataset was created for the creation and/or evaluation of pretrained Legal Language Models.",
"### Source Data",
"#### Data Collection and Processing\n\n\nWe used the EURLEX-Web-Scrapper Repo for the data collection process.",
"#### Who are the source data producers?\n\n\nThe Source data stems from the EURLEX-Website and was therefore produced by various entities within the European Union",
"#### Personal and Sensitive Information\n\n\nNo Personal or Sensitive Information is included to the best of our knowledge.\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\n* We removed HTML documents from which we couldn't extract plain text under the assumption that those are corrupted files.\nHowever, we can't guarantee that we removed all.\n* The Extraction of plain text from legal HTML documents can lead to formatting issues\ne.g. the extraction of text from tables might mix up the order such that it becomes nearly incomprehensible.\n* This dataset might contain many missing values in the meta-data columns as not every document was annotated in the same way",
"### Recommendations\n\n\n* Consider Removing rows with missing values for the task before training a model on it\n\n\n[optional]\n\n\nBibTeX:\n\n\nAPA:\n\n\nGlossary [optional]\n-------------------\n\n\nMore Information [optional]\n---------------------------\n\n\nDataset Card Authors [optional]\n-------------------------------\n\n\nDataset Card Contact\n--------------------"
]
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251,
67,
48,
33,
38,
112,
14,
2503,
28,
4,
26,
34,
147,
74
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-fill-mask #task_ids-multi-class-classification #task_ids-multi-label-classification #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-original #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Croatian #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Slovak #language-Slovenian #language-Swedish #license-mit #legal documents #corpus #eurlex #html #region-us \n### Dataset Description\n\n\nThis Dataset was scrapped from EURLEX.\nIt contains more than 4.6M Legal Documents in Plain Text and over 3.7M In HTML Format.\nThose Documents are separated by their language (This Dataset includes a total of 24 official European Languages)\nand by their Sector.#### The Table below shows the number of documents per language:\n\n\n\n* Curated by:\n* Funded by [optional]:\n* Shared by [optional]:\n* Language(s) (NLP):\n* License:### Dataset Sources [optional]\n\n\n* Repository: URL\n* Paper [optional]:\n* Demo [optional]:\n\n\nUses\n----### As Corpus for:\n\n\n* Pretraining of Language Models with self supervised tasks like Masked Language Modeling and Next Sentence Prediction\n* Legal Text Analysis",
"passage: ### As Dataset for evaluation on the following task:\n\n\n* *eurovoc*-Concepts Prediction i.e. which tags apply? (Muli-Label Classification (large Scale))\n\t+ Example for this task is given[below\n* *subject-matter* Prediction i.e. which other tags apply (Multi-Label Classification)\n* *form* Classification i.e. What Kind of Document is it? (Multi-Class)\n* And more### Example for Use Of EUROVOC-Concepts"
]
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|
4f565f669f91253a860abedea60fccf7a04986d6 | ---
license: creativeml-openrail-m
task_categories:
- text-classification
language:
- en
tags:
- entity
- autotrain
- web
pretty_name: autotrain
size_categories:
- 10K<n<100K
---
https://clickhere.medium.com/ | mariakatosvich/autotrain-data-extract-entities | [
"doi:10.57967/hf/0828",
"region:us"
]
| 2023-02-23T08:23:20+00:00 | {} | 2023-06-30T07:07:52+00:00 | []
| []
| TAGS
#doi-10.57967/hf/0828 #region-us
| ---
license: creativeml-openrail-m
task_categories:
- text-classification
language:
- en
tags:
- entity
- autotrain
- web
pretty_name: autotrain
size_categories:
- 10K<n<100K
---
URL | []
| [
"TAGS\n#doi-10.57967/hf/0828 #region-us \n"
]
| [
18
]
| [
"passage: TAGS\n#doi-10.57967/hf/0828 #region-us \n"
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|
2a08f5b67e07e3e212ef2fc6164dcb1c78c59f2a | # Dataset Card for "input-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | piamo/auto-retrain-input-dataset | [
"region:us"
]
| 2023-02-23T08:52:01+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "ADONIS", "1": "AFRICAN GIANT SWALLOWTAIL", "2": "AMERICAN SNOOT"}}}}], "splits": [{"name": "train", "num_bytes": 8825732.0, "num_examples": 338}], "download_size": 8823395, "dataset_size": 8825732.0}} | 2023-02-23T11:21:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "input-dataset"
More Information needed | [
"# Dataset Card for \"input-dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"input-dataset\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"input-dataset\"\n\nMore Information needed"
]
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784ac1ec7dc8a6412fa0c4046e1c61986d79a38f | # Dataset Card for "patched_test_p_40_f_membrane_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_40_f_membrane_m1_predictions | [
"region:us"
]
| 2023-02-23T09:19:18+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 1959469283, "num_examples": 3134581}], "download_size": 165870843, "dataset_size": 1959469283}} | 2023-02-23T09:19:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_40_f_membrane_m1_predictions"
More Information needed | [
"# Dataset Card for \"patched_test_p_40_f_membrane_m1_predictions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_40_f_membrane_m1_predictions\"\n\nMore Information needed"
]
| [
6,
31
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_40_f_membrane_m1_predictions\"\n\nMore Information needed"
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|
554cce743cc462d8f68f2fafe36787f35f6506d1 |
On this repository, I just downloaded and processed the `translate_train.tar.gz` file here: `https://github.com/ir-nlp-csui/indonli/tree/main/data`
How to use? As simple as this:
```python
!wget https://huggingface.co/datasets/muhammadravi251001/translated-indo-nli/raw/main/dev.jsonl
!wget https://huggingface.co/datasets/muhammadravi251001/translated-indo-nli/resolve/main/train.jsonl
import pandas as pd
data_train_translated_indonli = pd.read_json(path_or_buf='train.jsonl', lines=True)
data_dev_translated_indonli = pd.read_json(path_or_buf='dev.jsonl', lines=True)
```
Voila~!
## Reference
The dataset I used is by IndoNLI.
```
@inproceedings{indonli,
title = "IndoNLI: A Natural Language Inference Dataset for Indonesian",
author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
publisher = "Association for Computational Linguistics",
}
``` | muhammadravi251001/translated-indo-nli | [
"license:bigscience-openrail-m",
"translated-indonli",
"region:us"
]
| 2023-02-23T09:43:58+00:00 | {"license": "bigscience-openrail-m", "tags": ["translated-indonli"], "datasets": ["indonli"]} | 2023-02-23T15:19:38+00:00 | []
| []
| TAGS
#license-bigscience-openrail-m #translated-indonli #region-us
|
On this repository, I just downloaded and processed the 'translate_train.URL' file here: 'URL
How to use? As simple as this:
Voila~!
## Reference
The dataset I used is by IndoNLI.
| [
"## Reference\n\nThe dataset I used is by IndoNLI."
]
| [
"TAGS\n#license-bigscience-openrail-m #translated-indonli #region-us \n",
"## Reference\n\nThe dataset I used is by IndoNLI."
]
| [
25,
13
]
| [
"passage: TAGS\n#license-bigscience-openrail-m #translated-indonli #region-us \n## Reference\n\nThe dataset I used is by IndoNLI."
]
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|
c7c8affcb6afebffa3b71c4a5f44ec9555cee48c |
# App Flow
This dataset consists of hourly maximum traffic flow for 128 systems deployed on 16 logic data centers, resulting in 1083 different time series in total.
The length of each series is more than 4 months. Each time series is divided into two segments for training and testing with a ratio of 32:1.
This dataset was collected at Ant Group and does not contain any Personal Identifiable Information and is desensitized and encrypted. | kashif/App_Flow | [
"task_categories:time-series-forecasting",
"license:apache-2.0",
"region:us"
]
| 2023-02-23T10:41:46+00:00 | {"license": "apache-2.0", "task_categories": ["time-series-forecasting"]} | 2023-02-23T12:07:29+00:00 | []
| []
| TAGS
#task_categories-time-series-forecasting #license-apache-2.0 #region-us
|
# App Flow
This dataset consists of hourly maximum traffic flow for 128 systems deployed on 16 logic data centers, resulting in 1083 different time series in total.
The length of each series is more than 4 months. Each time series is divided into two segments for training and testing with a ratio of 32:1.
This dataset was collected at Ant Group and does not contain any Personal Identifiable Information and is desensitized and encrypted. | [
"# App Flow\n\nThis dataset consists of hourly maximum traffic flow for 128 systems deployed on 16 logic data centers, resulting in 1083 different time series in total.\nThe length of each series is more than 4 months. Each time series is divided into two segments for training and testing with a ratio of 32:1.\nThis dataset was collected at Ant Group and does not contain any Personal Identifiable Information and is desensitized and encrypted."
]
| [
"TAGS\n#task_categories-time-series-forecasting #license-apache-2.0 #region-us \n",
"# App Flow\n\nThis dataset consists of hourly maximum traffic flow for 128 systems deployed on 16 logic data centers, resulting in 1083 different time series in total.\nThe length of each series is more than 4 months. Each time series is divided into two segments for training and testing with a ratio of 32:1.\nThis dataset was collected at Ant Group and does not contain any Personal Identifiable Information and is desensitized and encrypted."
]
| [
29,
100
]
| [
"passage: TAGS\n#task_categories-time-series-forecasting #license-apache-2.0 #region-us \n# App Flow\n\nThis dataset consists of hourly maximum traffic flow for 128 systems deployed on 16 logic data centers, resulting in 1083 different time series in total.\nThe length of each series is more than 4 months. Each time series is divided into two segments for training and testing with a ratio of 32:1.\nThis dataset was collected at Ant Group and does not contain any Personal Identifiable Information and is desensitized and encrypted."
]
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|
b6b40f2ce5527af8ff1be927ab56d1fe610aa779 | # AutoTrain Dataset for project: stratefied-processing
## Dataset Description
This dataset has been automatically processed by AutoTrain for project stratefied-processing.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "dateline resources ltd",
"target": 0
},
{
"text": "dateline resources",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['BBG001S0S611', 'BBG001S112S8', 'BBG001S112Y1', 'BBG001S11324', 'BBG001S11351', 'BBG001S168Y3', 'BBG001S16908', 'BBG001S16971', 'BBG001S169N3', 'BBG001S169P1', 'BBG001S16LK9', 'BBG001S26WK3', 'BBG001S2CDJ0', 'BBG001S2CDW5', 'BBG001S2CK04', 'BBG001S2TZ30', 'BBG001S2WTG6', 'BBG001S2XSC1', 'BBG001S3F8M2', 'BBG001S3FBF3', 'BBG001S3RD02', 'BBG001S3RD11', 'BBG001S4BJY7', 'BBG001S4WVT3', 'BBG001S50HF1', 'BBG001S561K4', 'BBG001S561Q8', 'BBG001S561V2', 'BBG001S561Z8', 'BBG001S56277', 'BBG001S562F8', 'BBG001S562P7', 'BBG001S562W9', 'BBG001S56320', 'BBG001S56D46', 'BBG001S56G21', 'BBG001S56JK5', 'BBG001S56L11', 'BBG001S56P85', 'BBG001S56QM7', 'BBG001S56QV7', 'BBG001S56R18', 'BBG001S58BG5', 'BBG001S58L91', 'BBG001S5N8T1', 'BBG001S5N8V8', 'BBG001S5N964', 'BBG001S5N991', 'BBG001S5N9B8', 'BBG001S5N9D6', 'BBG001S5N9M6', 'BBG001S5N9P3', 'BBG001S5N9S0', 'BBG001S5NB14', 'BBG001S5NBJ5', 'BBG001S5NBN0', 'BBG001S5NBP8', 'BBG001S5NBQ7', 'BBG001S5NBT4', 'BBG001S5NBV1', 'BBG001S5NCC0', 'BBG001S5NCJ3', 'BBG001S5NCK1', 'BBG001S5NCQ5', 'BBG001S5NCZ5', 'BBG001S5ND38', 'BBG001S5ND65', 'BBG001S5ND74', 'BBG001S5ND92', 'BBG001S5NDG4', 'BBG001S5NDV7', 'BBG001S5NDW6', 'BBG001S5NF24', 'BBG001S5NF60', 'BBG001S5NFD2', 'BBG001S5NG22', 'BBG001S5NGJ4', 'BBG001S5NGN9', 'BBG001S5NGY7', 'BBG001S5NHB0', 'BBG001S5NHP5', 'BBG001S5NHS2', 'BBG001S5NJG1', 'BBG001S5NJV4', 'BBG001S5NJX2', 'BBG001S5NJZ0', 'BBG001S5NK14', 'BBG001S5NKC2', 'BBG001S5NKR6', 'BBG001S5NKV1', 'BBG001S5NKW0', 'BBG001S5NL21', 'BBG001S5NLG6', 'BBG001S5NLS3', 'BBG001S5NM29', 'BBG001S5NM65', 'BBG001S5NM92', 'BBG001S5NMM7', 'BBG001S5NMN6', 'BBG001S5NMX5', 'BBG001S5NN36', 'BBG001S5NN45', 'BBG001S5NN54', 'BBG001S5NN72', 'BBG001S5NNB7', 'BBG001S5NNL6', 'BBG001S5NNS9', 'BBG001S5NP04', 'BBG001S5NP86', 'BBG001S5NPF8', 'BBG001S5NPN9', 'BBG001S5NPQ6', 'BBG001S5NPR5', 'BBG001S5NQ11', 'BBG001S5NQ57', 'BBG001S5NQB0', 'BBG001S5NQH4', 'BBG001S5NR37', 'BBG001S5NRH2', 'BBG001S5NRY3', 'BBG001S5NS08', 'BBG001S5NS35', 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'BBG001SBRR21', 'BBG001SBRRM9', 'BBG001SBRV22', 'BBG001SBRW11', 'BBG001SBRX64', 'BBG001SBRYW3', 'BBG001SBRZ32', 'BBG001SBRZB3', 'BBG001SBRZT4', 'BBG001SBS2H9', 'BBG001SBS2N2', 'BBG001SBS2R8', 'BBG001SBS4N8', 'BBG001SBS5H2', 'BBG001SBS644', 'BBG001SBS6S8', 'BBG001SBS706', 'BBG001SBS7C3', 'BBG001SBS7D2', 'BBG001SBS8V0', 'BBG001SBSCN0', 'BBG001SBSCZ7', 'BBG001SBSDZ5', 'BBG001SBSFM4', 'BBG001SBSKG0', 'BBG001SBSMH5', 'BBG001SBSNV7', 'BBG001SBSRN7', 'BBG001SBSTJ8', 'BBG001SBSTK6', 'BBG001SBSTN3', 'BBG001SBSTQ0', 'BBG001SBSV76', 'BBG001SBSWY4', 'BBG001SBSY34', 'BBG001SBSZ95', 'BBG001SBT1Y1', 'BBG001SBT2W1', 'BBG001SBT2X0', 'BBG001SBT652', 'BBG001SBT670', 'BBG001SBT7R6', 'BBG001SBT7X9', 'BBG001SBT830', 'BBG001SBT849', 'BBG001SBT867', 'BBG001SBT983', 'BBG001SBT9X5', 'BBG001SBTBG9', 'BBG001SBTBJ6', 'BBG001SBTC31', 'BBG001SBTHK1', 'BBG001SBTHX7', 'BBG001SBTK51', 'BBG001SBTNW5', 'BBG001SBTQ67', 'BBG001SBTR10', 'BBG001SBTRJ1', 'BBG001SBTRW6', 'BBG001SBTSF3', 'BBG001SBTTB5', 'BBG001SBTW91', 'BBG001SBTYG9', 'BBG001SBTZY6', 'BBG001SBV025', 'BBG001SBV1Z7', 'BBG001SBV5B4', 'BBG001SBV5W1', 'BBG001SBV8V6', 'BBG001SBV8X4', 'BBG001SBV9R9', 'BBG001SBVB21', 'BBG001SBVBM9', 'BBG001SBVC74', 'BBG001SBVCL8', 'BBG001SBVD90', 'BBG001SBVF13', 'BBG001SBVH37', 'BBG001SBVMW4', 'BBG001SBVMY2', 'BBG001SBVP84', 'BBG001SBVQF4', 'BBG001SBVQH2', 'BBG001SBVTD0', 'BBG001SBVWN2', 'BBG001SBVXF9', 'BBG001SBVY30', 'BBG001SBVY94', 'BBG001SBVYY6', 'BBG001SBVZ73', 'BBG001SBW569', 'BBG001SBW578', 'BBG001SBW5G8', 'BBG001SBW6S3', 'BBG001SBW7S1', 'BBG001SBW881', 'BBG001SBW8S9', 'BBG001SBWB48', 'BBG001SBWC19', 'BBG001SBWDB6', 'BBG001SBWDX2', 'BBG001SBWFH5', 'BBG001SBWFZ5', 'BBG001SBWG10', 'BBG001SBWGB9', 'BBG001SBWGV7', 'BBG001SBWJ96', 'BBG001SBWJZ7', 'BBG001SBWKK0', 'BBG001SBWN15', 'BBG001SBWR52', 'BBG001SBWRN2', 'BBG001SBWVD4', 'BBG001SBWVT7', 'BBG001SBWXC1', 'BBG001SBWXG7', 'BBG001SBWXS4', 'BBG001SBWY84', 'BBG001SBWZL6', 'BBG001SBX1Z5', 'BBG001SBX229', 'BBG001SBX238', 'BBG001SBX2Q3', 'BBG001SBX336', 'BBG001SBX345', 'BBG001SBX470', 'BBG001SBX4B5', 'BBG001SBX540', 'BBG001SBX728', 'BBG001SBX7C7', 'BBG001SBX7P3', 'BBG001SBX933', 'BBG001SBXB29', 'BBG001SBXCF3', 'BBG001SBXCM5', 'BBG001SBXCZ1', 'BBG001SBXDV3', 'BBG001SBXGW5', 'BBG001SBXK74', 'BBG001SBXKK9', 'BBG001SBXMD3', 'BBG001SBXNS5', 'BBG001SBXR88', 'BBG001SBXWD1', 'BBG001SBXY01', 'BBG001SBY086', 'BBG001SBY2M6', 'BBG001SBY2S0', 'BBG001SBY380', 'BBG001SBY647', 'BBG001SBY8P0', 'BBG001SBY8V3', 'BBG001SBYC53', 'BBG001SBYCQ0', 'BBG001SBYCR9', 'BBG001SBYCZ0', 'BBG001SBYDJ6', 'BBG001SBYF92', 'BBG001SBYFK9', 'BBG001SBYHB5', 'BBG001SBYJJ3', 'BBG001SBYK19', 'BBG001SBYK46', 'BBG001SBYK73', 'BBG001SBYKD6', 'BBG001SBYL17', 'BBG001SBYM42', 'BBG001SBYMW1', 'BBG001SBYQ16', 'BBG001SBYQ89', 'BBG001SBYQ98', 'BBG001SBYR23', 'BBG001SBYTL8', 'BBG001SBYVK4', 'BBG001SBYVV2', 'BBG001SBYVY9', 'BBG001SBYXM8', 'BBG001SBYZG0', 'BBG001SBYZM3', 'BBG001SBYZN2', 'BBG001SBZ0D9', 'BBG001SBZ2Z1', 'BBG001SBZ343', 'BBG001SBZ370', 'BBG001SBZ441', 'BBG001SBZ4W0', 'BBG001SBZ7Q0', 'BBG001SBZ8N1', 'BBG001SBZ8W1', 'BBG001SBZ8X0', 'BBG001SBZ9G7', 'BBG001SBZ9T3', 'BBG001SBZ9X8', 'BBG001SBZBQ1', 'BBG001SBZBZ1', 'BBG001SBZCT6', 'BBG001SBZDP8', 'BBG001SBZDW0', 'BBG001SBZF64', 'BBG001SBZFQ2', 'BBG001SBZG08', 'BBG001SBZGC5', 'BBG001SBZGP1', 'BBG001SBZH79', 'BBG001SBZH88', 'BBG001SBZHZ8', 'BBG001SBZJM8', 'BBG001SBZJT1', 'BBG001SBZK36', 'BBG001SBZKV5', 'BBG001SBZNN8', 'BBG001SBZNW8', 'BBG001SBZP17', 'BBG001SBZQV2', 'BBG001SBZVV1', 'BBG001SBZVZ7', 'BBG001SBZWH5', 'BBG001SBZWY6', 'BBG001SBZXY4', 'BBG001SBZZX0', 'BBG001SC0054', 'BBG001SC00J9', 'BBG001SC00P2', 'BBG001SC02D1', 'BBG001SC02L2', 'BBG001SC02Q7', 'BBG001SC0312', 'BBG001SC0358', 'BBG001SC0376', 'BBG001SC03W8', 'BBG001SC0447', 'BBG001SC04J1', 'BBG001SC04V7', 'BBG001SC05G1', 'BBG001SC05Q0', 'BBG001SC07V0', 'BBG001SC07Z6', 'BBG001SC08P5', 'BBG001SC0BC2', 'BBG001SC0BS5', 'BBG001SC0FW1', 'BBG001SC0G68', 'BBG001SC0G86', 'BBG001SC0HR3', 'BBG001SC0HS2', 'BBG001SC0K41', 'BBG001SC0MG4', 'BBG001SC0NL6', 'BBG001SC0WW4', 'BBG001SC0X34', 'BBG001SC0XF1', 'BBG001SC0XH9', 'BBG001SC0YF9', 'BBG001SC0ZM8', 'BBG001SC0ZS2', 'BBG001SC10W3', 'BBG001SC1160', 'BBG001SC1188', 'BBG001SC11K4', 'BBG001SC11L3', 'BBG001SC13J2', 'BBG001SC1678', 'BBG001SC1712', 'BBG001SC17F7', 'BBG001SC1DM6', 'BBG001SC1NV4', 'BBG001SC1PJ3', 'BBG001SC1ZS1', 'BBG001SC21T4', 'BBG001SC23N6', 'BBG001SC28R1', 'BBG001SC2D27', 'BBG001SC2MJ9', 'BBG001SC3M71', 'BBG001SC3S41', 'BBG001SC3Y75', 'BBG001SC40Y8', 'BBG001SC4719', 'BBG001SC4746', 'BBG001SC4755', 'BBG001SC47X4', 'BBG001SC4BT0', 'BBG001SC4CM5', 'BBG001SC4D52', 'BBG001SC4DT6', 'BBG001SC4F84', 'BBG001SC4JB2', 'BBG001SC4L63', 'BBG001SC4MN2', 'BBG001SC4MR8', 'BBG001SC4NZ7', 'BBG001SC4Q62', 'BBG001SC4TM8', 'BBG001SC4TR3', 'BBG001SC4W23', 'BBG001SC4WP8', 'BBG001SC4X67', 'BBG001SC4XZ5', 'BBG001SC4Y65', 'BBG001SC4YT0', 'BBG001SC5255', 'BBG001SC52R1', 'BBG001SC54H8', 'BBG001SC5601', 'BBG001SC5638', 'BBG001SC5647', 'BBG001SC56D7', 'BBG001SC59F9', 'BBG001SC5DP9', 'BBG001SC5F47', 'BBG001SC5F65', 'BBG001SC5GP2', 'BBG001SC5H89', 'BBG001SC5H98', 'BBG001SC5HJ7', 'BBG001SC5KN5', 'BBG001SC5KR1', 'BBG001SC5MN1', 'BBG001SC5P36', 'BBG001SC5P54', 'BBG001SC5P90', 'BBG001SC5PB7', 'BBG001SC5RM1', 'BBG001SC5SP6', 'BBG001SC5TT0', 'BBG001SC5X02', 'BBG001SC5XD8', 'BBG001SC5XW7', 'BBG001SC5YX4', 'BBG001SC6003', 'BBG001SC6405', 'BBG001SC64Y8', 'BBG001SC6548', 'BBG001SC68G9', 'BBG001SC6904', 'BBG001SC6CZ9', 'BBG001SC6F91', 'BBG001SC6FV6', 'BBG001SC6GK6', 'BBG001SC6GL5', 'BBG001SC6HQ8', 'BBG001SC6HT5', 'BBG001SC6JH4', 'BBG001SC6LZ9', 'BBG001SC6N58', 'BBG001SC6NK1', 'BBG001SC6NV9', 'BBG001SC6P08', 'BBG001SC6QX0', 'BBG001SC6TH2', 'BBG001SC6VG8', 'BBG001SC6VM1', 'BBG001SC6VN0', 'BBG001SC6W03', 'BBG001SC6W94', 'BBG001SC6XP4', 'BBG001SC6Y90', 'BBG001SC6YL6', 'BBG001SC75L8', 'BBG001SC76G2', 'BBG001SC76K7', 'BBG001SC7C15', 'BBG001SC7CN1', 'BBG001SC7DG7', 'BBG001SC7JZ3', 'BBG001SC7PX1', 'BBG001SC7Q14', 'BBG001SC7QW0', 'BBG001SC7R30', 'BBG001SC7RP6', 'BBG001SC7SZ3', 'BBG001SC7TN4', 'BBG001SC7TV5', 'BBG001SC7VZ6', 'BBG001SC7WK0', 'BBG001SC7ZJ5', 'BBG001SC80T0', 'BBG001SC82J7', 'BBG001SC8564', 'BBG001SC85H2', 'BBG001SC8724', 'BBG001SC8868', 'BBG001SC88P7', 'BBG001SC89R3', 'BBG001SC8JB8', 'BBG001SC8NG4', 'BBG001SC8YQ9', 'BBG001SC8YX1', 'BBG001SC90W5', 'BBG001SC92M2', 'BBG001SC93S4', 'BBG001SC9741', 'BBG001SC9B06', 'BBG001SC9C40', 'BBG001SC9C86', 'BBG001SC9FJ7', 'BBG001SC9GB3', 'BBG001SC9JW4', 'BBG001SC9KM2', 'BBG001SC9LN9', 'BBG001SC9MM8', 'BBG001SC9MR3', 'BBG001SC9QL0', 'BBG001SC9R65', 'BBG001SC9R83', 'BBG001SC9SB7', 'BBG001SC9T98', 'BBG001SCB089', 'BBG001SCB374', 'BBG001SCB3K9', 'BBG001SCB463', 'BBG001SCB551', 'BBG001SCB604', 'BBG001SCB980', 'BBG001SCB9W3', 'BBG001SCBBM9', 'BBG001SCBDN4', 'BBG001SCBDP2', 'BBG001SCBDR0', 'BBG001SCBF31', 'BBG001SCBGC9', 'BBG001SCBHD6', 'BBG001SCBHG3', 'BBG001SCBJG9', 'BBG001SCBJP9', 'BBG001SCBLC8', 'BBG001SCBLD7', 'BBG001SCBN16', 'BBG001SCBNK5', 'BBG001SCBSB4', 'BBG001SCBSL3', 'BBG001SCBVL6', 'BBG001SCBW07', 'BBG001SCBX23', 'BBG001SCBYZ5', 'BBG001SCC006', 'BBG001SCC1L1', 'BBG001SCC1T3', 'BBG001SCC2J2', 'BBG001SCC3T9', 'BBG001SCC649', 'BBG001SCC6N8', 'BBG001SCC774', 'BBG001SCC7R2', 'BBG001SCCBK0', 'BBG001SCCF30', 'BBG001SCCJ23', 'BBG001SCCJ50', 'BBG001SCCJ96', 'BBG001SCCJM1', 'BBG001SCCKH4', 'BBG001SCCMJ8', 'BBG001SCCNB4', 'BBG001SCCPJ1', 'BBG001SCCQ81', 'BBG001SCCQC6', 'BBG001SCCSS5', 'BBG001SCCV80', 'BBG001SCCVC5', 'BBG001SCCWT5', 'BBG001SCCYK0', 'BBG001SCCZ54', 'BBG001SCD3Z1', 'BBG001SCDB38', 'BBG001SCDDY0', 'BBG001SCDF75', 'BBG001SCDFC9', 'BBG001SCDFW7', 'BBG001SCDG73', 'BBG001SCDM52', 'BBG001SCDQP1', 'BBG001SCDRJ6', 'BBG001SCDSX8', 'BBG001SCDW32', 'BBG001SCDXM9', 'BBG001SCDXR4', 'BBG001SCDY38', 'BBG001SCDZ80', 'BBG001SCDZL5', 'BBG001SCF1B9', 'BBG001SCF2F3', 'BBG001SCF2M5', 'BBG001SCF2T8', 'BBG001SCF5V8', 'BBG001SCF7F2', 'BBG001SCFBQ1', 'BBG001SCFQV2', 'BBG001SCFRQ6', 'BBG001SCFRV0', 'BBG001SCFV14', 'BBG001SCFWF7', 'BBG001SCG0R3', 'BBG001SCG119', 'BBG001SCG164', 'BBG001SCG1F4', 'BBG001SCG1P3', 'BBG001SCG397', 'BBG001SCG510', 'BBG001SCG5V7', 'BBG001SCG8J5', 'BBG001SCG9N8', 'BBG001SCGCB4', 'BBG001SCGD40', 'BBG001SCGDF8', 'BBG001SCGJG4', 'BBG001SCGK08', 'BBG001SCGLJ6', 'BBG001SCGLY9', 'BBG001SCGR67', 'BBG001SCGRY6', 'BBG001SCGVP7', 'BBG001SCGX00', 'BBG001SCGX73', 'BBG001SCH0Q3', 'BBG001SCH270', 'BBG001SCH2H9', 'BBG001SCH369', 'BBG001SCH5J0', 'BBG001SCH7V2', 'BBG001SCHDC0', 'BBG001SCHDY6', 'BBG001SCHF99', 'BBG001SCHFK6', 'BBG001SCHFV4', 'BBG001SCHHD0', 'BBG001SCHKP0', 'BBG001SCHLC2', 'BBG001SCHTR9', 'BBG001SCHV30', 'BBG001SCHV85', 'BBG001SCHWG4', 'BBG001SCHX09', 'BBG001SCHYJ7', 'BBG001SCJ3Q5', 'BBG001SCJ3T2', 'BBG001SCJ937', 'BBG001SCJ946', 'BBG001SCJ9M6', 'BBG001SCJF24', 'BBG001SCJF60', 'BBG001SCJGQ6', 'BBG001SCJJ08', 'BBG001SCJJ53', 'BBG001SCJLG6', 'BBG001SCJM38', 'BBG001SCJNN4', 'BBG001SCJQ20', 'BBG001SCJTQ8', 'BBG001SCJVR2', 'BBG001SCJXR8', 'BBG001SCK0M3', 'BBG001SCK3P4', 'BBG001SCK846', 'BBG001SCK8H2', 'BBG001SCKBM9', 'BBG001SCKC47', 'BBG001SCKD81', 'BBG001SCKD90', 'BBG001SCKDG2', 'BBG001SCKGY5', 'BBG001SCKHG3', 'BBG001SCKJT5', 'BBG001SCKKF7', 'BBG001SCKMZ1', 'BBG001SCKPS2', 'BBG001SCKXX9', 'BBG001SCKYC0', 'BBG001SCL1T3', 'BBG001SCL2J2', 'BBG001SCL5C2', 'BBG001SCL5Z7', 'BBG001SCL658', 'BBG001SCL8F3', 'BBG001SCLBK0', 'BBG001SCLC46', 'BBG001SCLF03', 'BBG001SCLGS1', 'BBG001SCLJ96', 'BBG001SCLJD1', 'BBG001SCLJN0', 'BBG001SCLKQ4', 'BBG001SCLKR3', 'BBG001SCLKS2', 'BBG001SCLKT1', 'BBG001SCLLV6', 'BBG001SCLMK6', 'BBG001SCLP01', 'BBG001SCLSL2', 'BBG001SCLV26', 'BBG001SCLV80', 'BBG001SCLVD4', 'BBG001SCLXR5', 'BBG001SCLY66', 'BBG001SCLYN7', 'BBG001SCLZ36', 'BBG001SCLZJ9', 'BBG001SCLZN4', 'BBG001SCLZT8', 'BBG001SCM069', 'BBG001SCM3Z1', 'BBG001SCM4W2', 'BBG001SCM568', 'BBG001SCM9Y9', 'BBG001SCMCN4', 'BBG001SCMGW5', 'BBG001SCMKQ3', 'BBG001SCMM52', 'BBG001SCMM61', 'BBG001SCMPD6', 'BBG001SCMPL7', 'BBG001SCN111', 'BBG001SCN7J9', 'BBG001SCN8L4', 'BBG001SCN8R8', 'BBG001SCNDP9', 'BBG001SCNDY9', 'BBG001SCNF65', 'BBG001SCNHG0', 'BBG001SCNJW8', 'BBG001SCNKJ0', 'BBG001SCNKK8', 'BBG001SCNMF0', 'BBG001SCNQN2', 'BBG001SCNRQ7', 'BBG001SCNSC0', 'BBG001SCNST2', 'BBG001SCNVC3', 'BBG001SCNVY9', 'BBG001SCNW31', 'BBG001SCNXL9', 'BBG001SCNXT1', 'BBG001SCNXX6', 'BBG001SCNXY5', 'BBG001SCNY82', 'BBG001SCP208', 'BBG001SCP351', 'BBG001SCP486', 'BBG001SCP5M7', 'BBG001SCP5R2', 'BBG001SCP5X5', 'BBG001SCP6Y2', 'BBG001SCP6Z1', 'BBG001SCP8D1', 'BBG001SCP9P6', 'BBG001SCPB62', 'BBG001SCPBC5', 'BBG001SCPBP1', 'BBG001SCPBV4', 'BBG001SCPCG9', 'BBG001SCPDF8', 'BBG001SCPDW9', 'BBG001SCPGK5', 'BBG001SCPH78', 'BBG001SCPJ10', 'BBG001SCPKJ8', 'BBG001SCPKS8', 'BBG001SCPMJ4', 'BBG001SCPMM0', 'BBG001SCPMQ6', 'BBG001SCPN11', 'BBG001SCPP25', 'BBG001SCPQY8', 'BBG001SCPRX7', 'BBG001SCPT27', 'BBG001SCPT90', 'BBG001SCPV31', 'BBG001SCPX28', 'BBG001SCPXR1', 'BBG001SCPZ69', 'BBG001SCPZJ5', 'BBG001SCPZM1', 'BBG001SCPZR6', 'BBG001SCQ065', 'BBG001SCQ0Y4', 'BBG001SCQ1Z1', 'BBG001SCQ2H9', 'BBG001SCQ2X1', 'BBG001SCQ314', 'BBG001SCQ332', 'BBG001SCQ3J5', 'BBG001SCQ4G6', 'BBG001SCQ4S3', 'BBG001SCQ4T2', 'BBG001SCQ5L7', 'BBG001SCQ840', 'BBG001SCQB07', 'BBG001SCQCD1', 'BBG001SCQDY6', 'BBG001SCQF62', 'BBG001SCQG15', 'BBG001SCQH95', 'BBG001SCQHY7', 'BBG001SCQJ64', 'BBG001SCQMT2', 'BBG001SCQN56', 'BBG001SCQPX0', 'BBG001SCQRF6', 'BBG001SCQV30', 'BBG001SCQWF5', 'BBG001SCQWW6', 'BBG001SCQZY7', 'BBG001SCR2H8', 'BBG001SCR2T5', 'BBG001SCR4R3', 'BBG001SCR581', 'BBG001SCR6X1', 'BBG001SCR7R6', 'BBG001SCR885', 'BBG001SCR894', 'BBG001SCR9Q3', 'BBG001SCRDM8', 'BBG001SCRFD3', 'BBG001SCRFG0', 'BBG001SCRFK5', 'BBG001SCRJB7', 'BBG001SCRJK7', 'BBG001SCRLD0', 'BBG001SCRP78', 'BBG001SCRPV1', 'BBG001SCRSG2', 'BBG001SCRSZ1', 'BBG001SCRV39', 'BBG001SCRXC5', 'BBG001SCRZR4', 'BBG001SCS189', 'BBG001SCS205', 'BBG001SCS2R6', 'BBG001SCS303', 'BBG001SCS483', 'BBG001SCS795', 'BBG001SCS9H2', 'BBG001SCS9W5', 'BBG001SCSCZ5', 'BBG001SCSH57', 'BBG001SCSHS2', 'BBG001SCSN27', 'BBG001SCSN81', 'BBG001SCSPQ6', 'BBG001SCSQN7', 'BBG001SCSSJ8', 'BBG001SCST33', 'BBG001SCSVC8', 'BBG001SCT231', 'BBG001SCT3M8', 'BBG001SCT721', 'BBG001SCT7H5', 'BBG001SCT8G4', 'BBG001SCT8N6', 'BBG001SCT927', 'BBG001SCTCR3', 'BBG001SCTDQ2', 'BBG001SCTFM1', 'BBG001SCTFP8', 'BBG001SCTFW0', 'BBG001SCTG12', 'BBG001SCTGC0', 'BBG001SCTGZ5', 'BBG001SCTL48', 'BBG001SCTLL9', 'BBG001SCTPK1', 'BBG001SCTQB9', 'BBG001SCTQY4', 'BBG001SCTS25', 'BBG001SCTS61', 'BBG001SCTSN2', 'BBG001SCTSQ9', 'BBG001SCTT05', 'BBG001SCTT87', 'BBG001SCTW53', 'BBG001SCV1F7', 'BBG001SCV1W8', 'BBG001SCV2B9', 'BBG001SCV2N6', 'BBG001SCV513', 'BBG001SCV5Q6', 'BBG001SCV6N7', 'BBG001SCV7W5', 'BBG001SCVBM7', 'BBG001SCVJQ6', 'BBG001SCVMF1', 'BBG001SCVQ26', 'BBG001SCVQ99', 'BBG001SCVQG1', 'BBG001SCVQN3', 'BBG001SCVRZ8', 'BBG001SCVS22', 'BBG001SCVS59', 'BBG001SCVSS4', 'BBG001SCVV52', 'BBG001SCVWD1', 'BBG001SCVWW0', 'BBG001SCVXS3', 'BBG001SCVYG4', 'BBG001SCVYP4', 'BBG001SCVZ26', 'BBG001SCW228', 'BBG001SCW2F4', 'BBG001SCW488', 'BBG001SCW6R2', 'BBG001SCW8R8', 'BBG001SCW8V3', 'BBG001SCW9F9', 'BBG001SCWB00', 'BBG001SCWG18', 'BBG001SCWKB8', 'BBG001SCWN40', 'BBG001SCWN95', 'BBG001SCWNB2', 'BBG001SCWNN9', 'BBG001SCWP45', 'BBG001SCWP54', 'BBG001SCWVF0', 'BBG001SCX147', 'BBG001SCX192', 'BBG001SCX584', 'BBG001SCX7G1', 'BBG001SCX806', 'BBG001SCX8L3', 'BBG001SCXB36', 'BBG001SCXBG2', 'BBG001SCXBR0', 'BBG001SCXBS9', 'BBG001SCXBZ1', 'BBG001SCXC89', 'BBG001SCXDF9', 'BBG001SCXK90', 'BBG001SCXLD3', 'BBG001SCXLH9', 'BBG001SCXLV3', 'BBG001SCXRG7', 'BBG001SCXW21', 'BBG001SCXW30', 'BBG001SCXWP6', 'BBG001SCXY18', 'BBG001SCXZV2', 'BBG001SCY1L7', 'BBG001SCY1M6', 'BBG001SCY2P1', 'BBG001SCY716', 'BBG001SCY921', 'BBG001SCYBD4', 'BBG001SCYFJ9', 'BBG001SCYJR2', 'BBG001SCYLW1', 'BBG001SCYMJ4', 'BBG001SCYN75', 'BBG001SCYNN7', 'BBG001SCYR12', 'BBG001SCYW02', 'BBG001SCYWS2', 'BBG001SCYZ32', 'BBG001SCYZW0', 'BBG001SCZ0F5', 'BBG001SCZ0M7', 'BBG001SCZ118', 'BBG001SCZ181', 'BBG001SCZ2V3', 'BBG001SCZ341', 'BBG001SCZ3M1', 'BBG001SCZ555', 'BBG001SCZ7K4', 'BBG001SCZB89', 'BBG001SCZBR8', 'BBG001SCZCS5', 'BBG001SCZCV1', 'BBG001SCZDT2', 'BBG001SCZH13', 'BBG001SCZHX8', 'BBG001SCZKL4', 'BBG001SCZLH7', 'BBG001SCZLL2', 'BBG001SCZM76', 'BBG001SCZMK1', 'BBG001SCZQY7', 'BBG001SCZSG3', 'BBG001SCZSK8', 'BBG001SCZVC0', 'BBG001SCZVQ5', 'BBG001SCZZS4', 'BBG001SD01K3', 'BBG001SD0285', 'BBG001SD0659', 'BBG001SD06N9', 'BBG001SD0766', 'BBG001SD0800', 'BBG001SD08D6', 'BBG001SD08G3', 'BBG001SD0CF5', 'BBG001SD0CM7', 'BBG001SD0DF3', 'BBG001SD0DX3', 'BBG001SD0H46', 'BBG001SD0HL7', 'BBG001SD0PG5', 'BBG001SD0PL9', 'BBG001SD0QX4', 'BBG001SD0ST5', 'BBG001SD0ZQ2', 'BBG001SD1097', 'BBG001SD12S2', 'BBG001SD1408', 'BBG001SD14F2', 'BBG001SD14L5', 'BBG001SD1D44', 'BBG001SD1F76', 'BBG001SD1G38', 'BBG001SD1KZ4', 'BBG001SD1L64', 'BBG001SD1N79', 'BBG001SD1TR4', 'BBG001SD1X04', 'BBG001SD1XD0', 'BBG001SD1XQ6', 'BBG001SD1YR3', 'BBG001SD2041', 'BBG001SD2112', 'BBG001SD21P6', 'BBG001SD2318', 'BBG001SD2461', 'BBG001SD2666', 'BBG001SD2B10', 'BBG001SD2B74', 'BBG001SD2BB9', 'BBG001SD2DR8', 'BBG001SD2GJ0', 'BBG001SD2J40', 'BBG001SD2J68', 'BBG001SD2QH0', 'BBG001SD2QM4', 'BBG001SD2R79', 'BBG001SD2RB4', 'BBG001SD2RD2', 'BBG001SD2SB2', 'BBG001SD2SD0', 'BBG001SD2T57', 'BBG001SD2TR3', 'BBG001SD2TY5', 'BBG001SD2W32', 'BBG001SD2W96', 'BBG001SD2X12', 'BBG001SD2YP4', 'BBG001SD2Z08', 'BBG001SD2ZG1', 'BBG001SD3004', 'BBG001SD3013', 'BBG001SD3068', 'BBG001SD30D0', 'BBG001SD3148', 'BBG001SD31B0', 'BBG001SD31G5', 'BBG001SD31K0', 'BBG001SD31M8', 'BBG001SD31N7', 'BBG001SD31Q4', 'BBG001SD32N5', 'BBG001SD32Z2', 'BBG001SD33H0', 'BBG001SD33Z0', 'BBG001SD3530', 'BBG001SD3576', 'BBG001SD3692', 'BBG001SD36N6', 'BBG001SD36Q3', 'BBG001SD36W6', 'BBG001SD3709', 'BBG001SD3736', 'BBG001SD39D1', 'BBG001SD39J5', 'BBG001SD39Z7', 'BBG001SD3BG3', 'BBG001SD3BH2', 'BBG001SD3BP3', 'BBG001SD3BV6', 'BBG001SD3BW5', 'BBG001SD3CY1', 'BBG001SD3DY9', 'BBG001SD3DZ8', 'BBG001SD3F38', 'BBG001SD3F56', 'BBG001SD3F74', 'BBG001SD3FG4', 'BBG001SD3JR4', 'BBG001SD3L08', 'BBG001SD3M06', 'BBG001SD3M15', 'BBG001SD3N95', 'BBG001SD3PG2', 'BBG001SD3PL6', 'BBG001SD3PN4', 'BBG001SD3SG6', 'BBG001SD3SN8', 'BBG001SD3TS1', 'BBG001SD3XR3', 'BBG001SD3XZ4', 'BBG001SD3Y46', 'BBG001SD3YJ0', 'BBG001SD3ZX1', 'BBG001SD4085', 'BBG001SD4192', 'BBG001SD41P4', 'BBG001SD4245', 'BBG001SD4254', 'BBG001SD4290', 'BBG001SD42R0', 'BBG001SD42S9', 'BBG001SD42W4', 'BBG001SD43R8', 'BBG001SD43V3', 'BBG001SD43X1', 'BBG001SD45H4', 'BBG001SD46L7', 'BBG001SD46Q2', 'BBG001SD47H0', 'BBG001SD49H6', 'BBG001SD4BJ9', 'BBG001SD4BL6', 'BBG001SD4C43', 'BBG001SD4C89', 'BBG001SD4DZ7', 'BBG001SD4FQ2', 'BBG001SD4FZ2', 'BBG001SD4G08', 'BBG001SD4G53', 'BBG001SD4JP5', 'BBG001SD4JX6', 'BBG001SD4KQ1', 'BBG001SD4L34', 'BBG001SD4N12', 'BBG001SD4NM9', 'BBG001SD4NT2', 'BBG001SD4Q24', 'BBG001SD4Q88', 'BBG001SD4S66', 'BBG001SD4TH2', 'BBG001SD4TY3', 'BBG001SD4VV1', 'BBG001SD4W03', 'BBG001SD4W30', 'BBG001SD4W94', 'BBG001SD4WL0', 'BBG001SD4XG4', 'BBG001SD4Y36', 'BBG001SD4YL6', 'BBG001SD4YY2', 'BBG001SD4Z42', 'BBG001SD4Z97', 'BBG001SD50Y5', 'BBG001SD5137', 'BBG001SD5262', 'BBG001SD52B6', 'BBG001SD52Y1', 'BBG001SD54L1', 'BBG001SD5592', 'BBG001SD55D7', 'BBG001SD56D5', 'BBG001SD5CL3', 'BBG001SD5H41', 'BBG001SD5JX5', 'BBG001SD5K08', 'BBG001SD5MF8', 'BBG001SD5R03', 'BBG001SD5SK9', 'BBG001SD5TS9', 'BBG001SD5VL1', 'BBG001SD5WG5', 'BBG001SD61N4', 'BBG001SD64G6', 'BBG001SD68G7', 'BBG001SD69B0', 'BBG001SD69D8', 'BBG001SD69L9', 'BBG001SD6D76', 'BBG001SD6DW8', 'BBG001SD6GY9', 'BBG001SD6NF5', 'BBG001SD6S91', 'BBG001SD6SM6', 'BBG001SD6VB1', 'BBG001SD6Z04', 'BBG001SD6Z13', 'BBG001SD6ZW9', 'BBG001SD7135', 'BBG001SD73X8', 'BBG001SD7509', 'BBG001SD7536', 'BBG001SD75G2', 'BBG001SD77H7', 'BBG001SD77Z7', 'BBG001SD7901', 'BBG001SD79J1', 'BBG001SD79V7', 'BBG001SD7BP9', 'BBG001SD7D84', 'BBG001SD7FL4', 'BBG001SD7FZ9', 'BBG001SD7G05', 'BBG001SD7GP8', 'BBG001SD7H76', 'BBG001SD7H94', 'BBG001SD7HQ5', 'BBG001SD7J81', 'BBG001SD7MQ4', 'BBG001SD7PS5', 'BBG001SD7QK1', 'BBG001SD7RB9', 'BBG001SD7XS8', 'BBG001SD80P2', 'BBG001SD8214', 'BBG001SD82B3', 'BBG001SD8330', 'BBG001SD83T2', 'BBG001SD8517', 'BBG001SD86X0', 'BBG001SD8DD7', 'BBG001SD8DK9', 'BBG001SD8F15', 'BBG001SD8JJ8', 'BBG001SD8R91', 'BBG001SD8RV6', 'BBG001SD8VW6', 'BBG001SD8W72', 'BBG001SD8XK5', 'BBG001SD8Z84', 'BBG001SD9008', 'BBG001SD9213', 'BBG001SD94P3', 'BBG001SD9525', 'BBG001SD9561', 'BBG001SD96X9', 'BBG001SD9712', 'BBG001SD97Y6', 'BBG001SD9C75', 'BBG001SD9CQ4', 'BBG001SD9DW5', 'BBG001SD9H29', 'BBG001SD9N71', 'BBG001SD9P67', 'BBG001SD9RJ9', 'BBG001SD9RM5', 'BBG001SD9T14', 'BBG001SD9TG8', 'BBG001SD9TS5', 'BBG001SD9V55', 'BBG001SD9V73', 'BBG001SD9VH2', 'BBG001SD9WB6', 'BBG001SD9X60', 'BBG001SDB050', 'BBG001SDB087', 'BBG001SDB0L2', 'BBG001SDB176', 'BBG001SDB327', 'BBG001SDB363', 'BBG001SDB559', 'BBG001SDB5Z6', 'BBG001SDB791', 'BBG001SDB7B8', 'BBG001SDB7C7', 'BBG001SDBB01', 'BBG001SDBD34', 'BBG001SDBF48', 'BBG001SDBF84', 'BBG001SDBFQ4', 'BBG001SDBJR5', 'BBG001SDBJT3', 'BBG001SDBJZ6', 'BBG001SDBLX3', 'BBG001SDBN96', 'BBG001SDBP37', 'BBG001SDBP55', 'BBG001SDBRV2', 'BBG001SDBRW1', 'BBG001SDBSD0', 'BBG001SDBSR5', 'BBG001SDBZ80', 'BBG001SDC1W7', 'BBG001SDC273', 'BBG001SDC291', 'BBG001SDC380', 'BBG001SDC4D2', 'BBG001SDC4J6', 'BBG001SDC870', 'BBG001SDC8B5', 'BBG001SDC8L4', 'BBG001SDCC08', 'BBG001SDCF10', 'BBG001SDCFT0', 'BBG001SDCJ21', 'BBG001SDCK91', 'BBG001SDCL35', 'BBG001SDCM15', 'BBG001SDCPS9', 'BBG001SDCRT4', 'BBG001SDCST2', 'BBG001SDCV97', 'BBG001SDCWG7', 'BBG001SDCX75', 'BBG001SDCY73', 'BBG001SDCZ70', 'BBG001SDCZZ9', 'BBG001SDD1S1', 'BBG001SDD209', 'BBG001SDD3P0', 'BBG001SDD4T4', 'BBG001SDD6L7', 'BBG001SDD7S8', 'BBG001SDD7Z0', 'BBG001SDD824', 'BBG001SDD8F0', 'BBG001SDDBQ1', 'BBG001SDDC52', 'BBG001SDDD05', 'BBG001SDDDP8', 'BBG001SDDF82', 'BBG001SDDFM6', 'BBG001SDDFY3', 'BBG001SDDHJ6', 'BBG001SDDJH4', 'BBG001SDDMX9', 'BBG001SDDVQ7', 'BBG001SDDWL0', 'BBG001SDDWZ5', 'BBG001SDF181', 'BBG001SDF2N2', 'BBG001SDF582', 'BBG001SDF760', 'BBG001SDFF62', 'BBG001SDFFC5', 'BBG001SDFFK6', 'BBG001SDFG15', 'BBG001SDFHL1', 'BBG001SDFKP0', 'BBG001SDFP06', 'BBG001SDFPD2', 'BBG001SDFPL3', 'BBG001SDFR75', 'BBG001SDFRF6', 'BBG001SDFSM6', 'BBG001SDFV49', 'BBG001SDFX63', 'BBG001SDFY52', 'BBG001SDFYJ7', 'BBG001SDFYK5', 'BBG001SDFZS4', 'BBG001SDFZX8', 'BBG001SDG0H2', 'BBG001SDG572', 'BBG001SDG6C4', 'BBG001SDG7X9', 'BBG001SDG849', 'BBG001SDG885', 'BBG001SDG8T2', 'BBG001SDG8Y6', 'BBG001SDG965', 'BBG001SDG9D7', 'BBG001SDG9X5', 'BBG001SDGD75', 'BBG001SDGFR8', 'BBG001SDGFT6', 'BBG001SDGFV3', 'BBG001SDGHK1', 'BBG001SDGKP9', 'BBG001SDGNN5', 'BBG001SDGQB1', 'BBG001SDGR01', 'BBG001SDGSF3', 'BBG001SDGSH1', 'BBG001SDGTK5', 'BBG001SDGVT1', 'BBG001SDGWS0', 'BBG001SDGWV6', 'BBG001SDGXF2', 'BBG001SDGZB1', 'BBG001SDGZF7', 'BBG001SDH152', 'BBG001SDH1H9', 'BBG001SDH1V3', 'BBG001SDH250', 'BBG001SDH3R4', 'BBG001SDH6G9', 'BBG001SDH6W1', 'BBG001SDH7B2', 'BBG001SDH884', 'BBG001SDH8F6', 'BBG001SDH9Y3', 'BBG001SDHBB3', 'BBG001SDHBQ7', 'BBG001SDHC58', 'BBG001SDHD83', 'BBG001SDHF60', 'BBG001SDHGY7', 'BBG001SDHJS8', 'BBG001SDHJW3', 'BBG001SDHK32', 'BBG001SDHKH7', 'BBG001SDHNW4', 'BBG001SDHP95', 'BBG001SDHPH6', 'BBG001SDHSQ0', 'BBG001SDHSY1', 'BBG001SDHTQ8', 'BBG001SDHWD5', 'BBG001SDHWR0', 'BBG001SDJ285', 'BBG001SDJ347', 'BBG001SDJ365', 'BBG001SDJ3S1', 'BBG001SDJ4R0', 'BBG001SDJ6N9', 'BBG001SDJ7K0', 'BBG001SDJ882', 'BBG001SDJ9B6', 'BBG001SDJBK1', 'BBG001SDJBN8', 'BBG001SDJC38', 'BBG001SDJHP3', 'BBG001SDJJX0', 'BBG001SDJKQ5', 'BBG001SDJP11', 'BBG001SDJQ37', 'BBG001SDJSF0', 'BBG001SDJVG2', 'BBG001SDJX50', 'BBG001SDK0P8', 'BBG001SDK2P4', 'BBG001SDK336', 'BBG001SDK3J9', 'BBG001SDK3S9', 'BBG001SDK3V5', 'BBG001SDK6B0', 'BBG001SDK871', 'BBG001SDK915', 'BBG001SDKDB5', 'BBG001SDKJZ6', 'BBG001SDKMJ7', 'BBG001SDKNS5', 'BBG001SDKNY8', 'BBG001SDKPD6', 'BBG001SDKPT9', 'BBG001SDKS86', 'BBG001SDKTM8', 'BBG001SDKVB5', 'BBG001SDKXN8', 'BBG001SDKXT2', 'BBG001SDL1R3', 'BBG001SDL4X0', 'BBG001SDL5X7', 'BBG001SDL709', 'BBG001SDL7L6', 'BBG001SDL807', 'BBG001SDL9S5', 'BBG001SDLBT9', 'BBG001SDLC80', 'BBG001SDLD33', 'BBG001SDLD51', 'BBG001SDLDD2', 'BBG001SDLN68', 'BBG001SDLNC1', 'BBG001SDLP09', 'BBG001SDLQ34', 'BBG001SDLQH9', 'BBG001SDLQZ9', 'BBG001SDLSS3', 'BBG001SDLX20', 'BBG001SDLYG3', 'BBG001SDLYP3', 'BBG001SDLYX4', 'BBG001SDM0V9', 'BBG001SDM2J9', 'BBG001SDM307', 'BBG001SDM3D3', 'BBG001SDM3M3', 'BBG001SDM3W2', 'BBG001SDM4Q7', 'BBG001SDM673', 'BBG001SDMF46', 'BBG001SDMG62', 'BBG001SDMPG1', 'BBG001SDMRD0', 'BBG001SDMVC2', 'BBG001SDMX92', 'BBG001SDMXH3', 'BBG001SDN217', 'BBG001SDN912', 'BBG001SDN9T2', 'BBG001SDN9V9', 'BBG001SDN9W8', 'BBG001SDNB44', 'BBG001SDNBP1', 'BBG001SDNC79', 'BBG001SDNFQ1', 'BBG001SDNKB6', 'BBG001SDNNW7', 'BBG001SDNPJ7', 'BBG001SDNPY0', 'BBG001SDNSB9', 'BBG001SDNT81', 'BBG001SDNVN9', 'BBG001SDNYJ8', 'BBG001SDNYQ0', 'BBG001SDNYS8', 'BBG001SDP3B2', 'BBG001SDP3N9', 'BBG001SDP7H7', 'BBG001SDP7J5', 'BBG001SDP8P6', 'BBG001SDP9J1', 'BBG001SDPCV0', 'BBG001SDPDJ2', 'BBG001SDPDP5', 'BBG001SDPDR3', 'BBG001SDPFH9', 'BBG001SDPFY0', 'BBG001SDPG41', 'BBG001SDPGD1', 'BBG001SDPGZ7', 'BBG001SDPHZ5', 'BBG001SDPJC6', 'BBG001SDPLN9', 'BBG001SDPLQ6', 'BBG001SDPM66', 'BBG001SDPNY3', 'BBG001SDPPH7', 'BBG001SDPTN2', 'BBG001SDPZ76', 'BBG001SDQ2Q7', 'BBG001SDQ697', 'BBG001SDQ7F8', 'BBG001SDQ7N9', 'BBG001SDQ8V8', 'BBG001SDQ8W7', 'BBG001SDQB14', 'BBG001SDQC76', 'BBG001SDQF15', 'BBG001SDQJ17', 'BBG001SDQJG1', 'BBG001SDQNQ1', 'BBG001SDQNX3', 'BBG001SDQP59', 'BBG001SDQQD8', 'BBG001SDQR19', 'BBG001SDQRX4', 'BBG001SDQSB6', 'BBG001SDQSC5', 'BBG001SDQSW3', 'BBG001SDQV83', 'BBG001SDQWW4', 'BBG001SDR062', 'BBG001SDR7W8', 'BBG001SDR8R2', 'BBG001SDR8W6', 'BBG001SDRB22', 'BBG001SDRDX4', 'BBG001SDRG21', 'BBG001SDRGP6', 'BBG001SDRH74', 'BBG001SDRJ16', 'BBG001SDRJD3', 'BBG001SDRJX1', 'BBG001SDRL75', 'BBG001SDRL93', 'BBG001SDRLJ2', 'BBG001SDRLL9', 'BBG001SDRQM7', 'BBG001SDRQW6', 'BBG001SDRTR6', 'BBG001SDRVL7', 'BBG001SDRXY9', 'BBG001SDRZ29', 'BBG001SDRZL8', 'BBG001SDS178', 'BBG001SDS4K7', 'BBG001SDS5G9', 'BBG001SDS891', 'BBG001SDSDH1', 'BBG001SDSN07', 'BBG001SDST22', 'BBG001SDSV81', 'BBG001SDSVH1', 'BBG001SDSW61', 'BBG001SDSWX1', 'BBG001SDSWY0', 'BBG001SDSX14', 'BBG001SDSX32', 'BBG001SDSZP3', 'BBG001SDT523', 'BBG001SDT8M5', 'BBG001SDT961', 'BBG001SDTBP5', 'BBG001SDTCH2', 'BBG001SDTJY8', 'BBG001SDTLC7', 'BBG001SDTLV6', 'BBG001SDTN33', 'BBG001SDTNQ8', 'BBG001SDTNY9', 'BBG001SDTRN2', 'BBG001SDTSJ5', 'BBG001SDTVQ0', 'BBG001SDTZ45', 'BBG001SDV2Y2', 'BBG001SDV370', 'BBG001SDV3N2', 'BBG001SDV414', 'BBG001SDV5Z4', 'BBG001SDV6G3', 'BBG001SDV6R1', 'BBG001SDV780', 'BBG001SDV7X2', 'BBG001SDV8R7', 'BBG001SDVC34', 'BBG001SDVCH9', 'BBG001SDVFV6', 'BBG001SDVGN3', 'BBG001SDVKL6', 'BBG001SDVL07', 'BBG001SDVPN3', 'BBG001SDVS48', 'BBG001SDVSM8', 'BBG001SDVWG6', 'BBG001SDVXS1', 'BBG001SDVZP9', 'BBG001SDVZV2', 'BBG001SDW0C9', 'BBG001SDW128', 'BBG001SDW1S0', 'BBG001SDW4G7', 'BBG001SDW6Y2', 'BBG001SDW7L4', 'BBG001SDW7Q9', 'BBG001SDWBN3', 'BBG001SDWCT5', 'BBG001SDWCZ8', 'BBG001SDWD59', 'BBG001SDWJQ3', 'BBG001SDWMW9', 'BBG001SDWPS7', 'BBG001SDWRP6', 'BBG001SDWTD5', 'BBG001SDWVG7', 'BBG001SDWX19', 'BBG001SDX029', 'BBG001SDX207', 'BBG001SDX225', 'BBG001SDX252', 'BBG001SDX305', 'BBG001SDX4S3', 'BBG001SDX5H2', 'BBG001SDX6N3', 'BBG001SDX760', 'BBG001SDX840', 'BBG001SDX8R5', 'BBG001SDX8T3', 'BBG001SDX9V8', 'BBG001SDXBJ7', 'BBG001SDXC50', 'BBG001SDXFP1', 'BBG001SDXFT7', 'BBG001SDXGH8', 'BBG001SDXH04', 'BBG001SDXHV0', 'BBG001SDXJR1', 'BBG001SDXQD0', 'BBG001SDXT71', 'BBG001SDXTP1', 'BBG001SDY0X4', 'BBG001SDY2N1', 'BBG001SDY4R3', 'BBG001SDYBH8', 'BBG001SDYGQ7', 'BBG001SDYGS5', 'BBG001SDYMP5', 'BBG001SDYQL0', 'BBG001SDYZG6', 'BBG001SDYZM9', 'BBG001SDZ161', 'BBG001SDZ278', 'BBG001SDZ6G9', 'BBG001SDZ7B2', 'BBG001SDZ839', 'BBG001SDZ9K8', 'BBG001SDZBM1', 'BBG001SDZCF7', 'BBG001SDZCG6', 'BBG001SDZDD7', 'BBG001SDZGD0', 'BBG001SDZHG5', 'BBG001SDZHM8', 'BBG001SDZHP5', 'BBG001SDZHV8', 'BBG001SDZM29', 'BBG001SDZNG2', 'BBG001SDZPN9', 'BBG001SDZPR5', 'BBG001SDZPT3', 'BBG001SDZRB8', 'BBG001SDZSV4', 'BBG001SDZV65', 'BBG001SDZYX9', 'BBG001SF0100', 'BBG001SF01F4', 'BBG001SF01G3', 'BBG001SF02H0', 'BBG001SF06Q1', 'BBG001SF0CS6', 'BBG001SF0F27', 'BBG001SF0K17', 'BBG001SF0R30', 'BBG001SF0RN8', 'BBG001SF0T63', 'BBG001SF0VF8', 'BBG001SF0XN5', 'BBG001SF0Z23', 'BBG001SF11H1', 'BBG001SF1332', 'BBG001SF13S5', 'BBG001SF1449', 'BBG001SF1582', 'BBG001SF1617', 'BBG001SF1626', 'BBG001SF17M2', 'BBG001SF1BX1', 'BBG001SF1HK2', 'BBG001SF1NW6', 'BBG001SF1PH8', 'BBG001SF1R75', 'BBG001SF1SL7', 'BBG001SF1WN6', 'BBG001SF1XH1', 'BBG001SF1XY2', 'BBG001SF21Q0', 'BBG001SF2288', 'BBG001SF22H8', 'BBG001SF23V0', 'BBG001SF28J3', 'BBG001SF28W8', 'BBG001SF2DC9', 'BBG001SF2DY5', 'BBG001SF2GW0', 'BBG001SF2HF7', 'BBG001SF2JX3', 'BBG001SF2K06', 'BBG001SF2K42', 'BBG001SF2L95', 'BBG001SF2PW0', 'BBG001SF2VM8', 'BBG001SF2YK4', 'BBG001SF34K9', 'BBG001SF34Q3', 'BBG001SF3660', 'BBG001SF3946', 'BBG001SF3C76', 'BBG001SF3F60', 'BBG001SF3FX0', 'BBG001SF3J26', 'BBG001SF3JC5', 'BBG001SF3JF2', 'BBG001SF3JZ0', 'BBG001SF3K41', 'BBG001SF3K87', 'BBG001SF3KB3', 'BBG001SF3L85', 'BBG001SF3NM5', 'BBG001SF3P04', 'BBG001SF3P77', 'BBG001SF3Q66', 'BBG001SF3Q84', 'BBG001SF3TR7', 'BBG001SF3WY2', 'BBG001SF3ZQ4', 'BBG001SF4605', 'BBG001SF4B68', 'BBG001SF4KP7', 'BBG001SF4L48', 'BBG001SF4LP5', 'BBG001SF4NM4', 'BBG001SF4NY1', 'BBG001SF4RY2', 'BBG001SF4TP8', 'BBG001SF4TX9', 'BBG001SF4VS0', 'BBG001SF4XM2', 'BBG001SF5141', 'BBG001SF51N0', 'BBG001SF56T3', 'BBG001SF5953', 'BBG001SF5980', 'BBG001SF59H0', 'BBG001SF5DJ9', 'BBG001SF5FJ4', 'BBG001SF5H91', 'BBG001SF5HB8', 'BBG001SF5LT0', 'BBG001SF5LW6', 'BBG001SF5M09', 'BBG001SF5MB7', 'BBG001SF5MH1', 'BBG001SF5MS9', 'BBG001SF5MW4', 'BBG001SF5N07', 'BBG001SF5RK6', 'BBG001SF5RY1', 'BBG001SF5SC3', 'BBG001SF5SG9', 'BBG001SF5VZ1', 'BBG001SF60M2', 'BBG001SF62F6', 'BBG001SF62P5', 'BBG001SF6373', 'BBG001SF6596', 'BBG001SF65L2', 'BBG001SF65P8', 'BBG001SF6747', 'BBG001SF6961', 'BBG001SF69P0', 'BBG001SF6B93', 'BBG001SF6CM6', 'BBG001SF6DY1', 'BBG001SF6FR4', 'BBG001SF6G56', 'BBG001SF6HK7', 'BBG001SF6L46', 'BBG001SF6PZ3', 'BBG001SF6R07', 'BBG001SF6R25', 'BBG001SF6S05', 'BBG001SF6S96', 'BBG001SF6V71', 'BBG001SF6W88', 'BBG001SF6Y39', 'BBG001SF6YK0', 'BBG001SF6YV8', 'BBG001SF6Z45', 'BBG001SF6Z72', 'BBG001SF6ZZ1', 'BBG001SF7005', 'BBG001SF70S5', 'BBG001SF73F3', 'BBG001SF7416', 'BBG001SF74W2', 'BBG001SF74Y0', 'BBG001SF7522', 'BBG001SF7595', 'BBG001SF75B2', 'BBG001SF7657', 'BBG001SF76G5', 'BBG001SF7906', 'BBG001SF7BF5', 'BBG001SF7BW6', 'BBG001SF7C18', 'BBG001SF7C54', 'BBG001SF7C72', 'BBG001SF7CB7', 'BBG001SF7CH1', 'BBG001SF7CM5', 'BBG001SF7D61', 'BBG001SF7DQ9', 'BBG001SF7DX1', 'BBG001SF7FF6', 'BBG001SF7FT1', 'BBG001SF7G00', 'BBG001SF7H08', 'BBG001SF7HB6', 'BBG001SF7M43', 'BBG001SF7QG1', 'BBG001SF7R33', 'BBG001SF7RS6', 'BBG001SF7T02', 'BBG001SF7TV8', 'BBG001SF7V07', 'BBG001SF7V89', 'BBG001SF8255', 'BBG001SF82R1', 'BBG001SF83D4', 'BBG001SF83K6', 'BBG001SF8406', 'BBG001SF8460', 'BBG001SF84L3', 'BBG001SF85Y6', 'BBG001SF86D7', 'BBG001SF87V5', 'BBG001SF8807', 'BBG001SF8861', 'BBG001SF88Y0', 'BBG001SF8932', 'BBG001SF89W0', 'BBG001SF8DJ6', 'BBG001SF8F10', 'BBG001SF8FK9', 'BBG001SF8G27', 'BBG001SF8H07', 'BBG001SF8HM3', 'BBG001SF8L44', 'BBG001SF8LP1', 'BBG001SF8M33', 'BBG001SF8P45', 'BBG001SF8Q52', 'BBG001SF8W86', 'BBG001SF8WV0', 'BBG001SF8X02', 'BBG001SF8XY5', 'BBG001SF8XZ4', 'BBG001SF8YB8', 'BBG001SF8YW5', 'BBG001SF91V7', 'BBG001SF9272', 'BBG001SF93X1', 'BBG001SF93Z9', 'BBG001SF94V1', 'BBG001SF95L9', 'BBG001SF9744', 'BBG001SF97Y1', 'BBG001SF99G7', 'BBG001SF9B81', 'BBG001SF9DJ5', 'BBG001SF9F19', 'BBG001SF9FC7', 'BBG001SF9GV4', 'BBG001SF9H06', 'BBG001SF9H97', 'BBG001SF9HJ6', 'BBG001SF9J02', 'BBG001SF9J93', 'BBG001SF9JC9', 'BBG001SF9JJ2', 'BBG001SF9KP2', 'BBG001SF9KX3', 'BBG001SF9M14', 'BBG001SF9MK3', 'BBG001SF9ML2', 'BBG001SF9NK1', 'BBG001SF9NQ5', 'BBG001SF9QM2', 'BBG001SF9R68', 'BBG001SF9RT3', 'BBG001SF9RV0', 'BBG001SF9S57', 'BBG001SF9S75', 'BBG001SF9SX6', 'BBG001SF9W21', 'BBG001SF9W94', 'BBG001SF9WJ3', 'BBG001SF9WZ5', 'BBG001SF9X92', 'BBG001SF9XZ3', 'BBG001SF9YL6', 'BBG001SF9YT8', 'BBG001SF9YY2', 'BBG001SFB2S6', 'BBG001SFB6X1', 'BBG001SFB7R6', 'BBG001SFB7Z7', 'BBG001SFBBM2', 'BBG001SFBBS6', 'BBG001SFBD48', 'BBG001SFBDR3', 'BBG001SFBF25', 'BBG001SFBGZ7', 'BBG001SFBHY6', 'BBG001SFBJ27', 'BBG001SFBK60', 'BBG001SFBLL1', 'BBG001SFBM20', 'BBG001SFBNM6', 'BBG001SFBNQ2', 'BBG001SFBNY3', 'BBG001SFBP87', 'BBG001SFBPB3', 'BBG001SFBQ03', 'BBG001SFBQD9', 'BBG001SFBR01', 'BBG001SFBS27', 'BBG001SFBZN8', 'BBG001SFC349', 'BBG001SFC4M7', 'BBG001SFC4P4', 'BBG001SFC5F2', 'BBG001SFC7V0', 'BBG001SFC7W9', 'BBG001SFC7Y7', 'BBG001SFC7Z6', 'BBG001SFC802', 'BBG001SFC820', 'BBG001SFC9P3', 'BBG001SFC9S0', 'BBG001SFCBS5', 'BBG001SFCCH5', 'BBG001SFCH93', 'BBG001SFCJ35', 'BBG001SFCJK6', 'BBG001SFCJN3', 'BBG001SFCK78', 'BBG001SFCMH3', 'BBG001SFCMX5', 'BBG001SFCPS4', 'BBG001SFCR82', 'BBG001SFCS99', 'BBG001SFCSK6', 'BBG001SFCTL3', 'BBG001SFCTN1', 'BBG001SFCVJ1', 'BBG001SFCVW6', 'BBG001SFCW36', 'BBG001SFCXS7', 'BBG001SFCY78', 'BBG001SFCYN0', 'BBG001SFD0K6', 'BBG001SFD1Z8', 'BBG001SFD6Z7', 'BBG001SFD8J1', 'BBG001SFDBC1', 'BBG001SFDBF8', 'BBG001SFDF41', 'BBG001SFDFB3', 'BBG001SFDFV1', 'BBG001SFDHB9', 'BBG001SFDLD8', 'BBG001SFDM82', 'BBG001SFDT96', 'BBG001SFDTK3', 'BBG001SFDWV4', 'BBG001SFDYZ6', 'BBG001SFF0N1', 'BBG001SFF2W7', 'BBG001SFF4H0', 'BBG001SFF4S8', 'BBG001SFFB84', 'BBG001SFFDM4', 'BBG001SFFHL6', 'BBG001SFFL55', 'BBG001SFFLX4', 'BBG001SFFLY3', 'BBG001SFFYJ2', 'BBG001SFFZ63', 'BBG001SFFZF3', 'BBG001SFG452', 'BBG001SFG6Z4', 'BBG001SFG7G3', 'BBG001SFG8S8', 'BBG001SFGC09', 'BBG001SFGF84', 'BBG001SFGF93', 'BBG001SFGFG5', 'BBG001SFGH35', 'BBG001SFGJ31', 'BBG001SFGL81', 'BBG001SFGWG8', 'BBG001SFGWZ7', 'BBG001SFGX21', 'BBG001SFGXD9', 'BBG001SFGXG6', 'BBG001SFGXJ3', 'BBG001SFGXQ5', 'BBG001SFGXR4', 'BBG001SFGXS3', 'BBG001SFGXW8', 'BBG001SFGXZ5', 'BBG001SFGY10', 'BBG001SFGZL5', 'BBG001SFH0K2', 'BBG001SFH120', 'BBG001SFH1P5', 'BBG001SFH3T7', 'BBG001SFH5C0', 'BBG001SFH7D5', 'BBG001SFH7S9', 'BBG001SFH8P0', 'BBG001SFH950', 'BBG001SFH9C2', 'BBG001SFHGY2', 'BBG001SFHH43', 'BBG001SFHJL0', 'BBG001SFHKJ0', 'BBG001SFHKT9', 'BBG001SFHKW5', 'BBG001SFHP72', 'BBG001SFHPG2', 'BBG001SFHS85', 'BBG001SFHSV9', 'BBG001SFHVP9', 'BBG001SFHVZ8', 'BBG001SFHXR3', 'BBG001SFJ0F6', 'BBG001SFJ734', 'BBG001SFJ743', 'BBG001SFJF18', 'BBG001SFJFD5', 'BBG001SFJKF2', 'BBG001SFJL33', 'BBG001SFJMM0', 'BBG001SFJNH4', 'BBG001SFJQ69', 'BBG001SFJQS5', 'BBG001SFJQV1', 'BBG001SFJRV9', 'BBG001SFJXW5', 'BBG001SFJZ41', 'BBG001SFK0V6', 'BBG001SFK313', 'BBG001SFK4L9', 'BBG001SFK6C4', 'BBG001SFK9K9', 'BBG001SFK9N6', 'BBG001SFKCZ6', 'BBG001SFKF98', 'BBG001SFKFF1', 'BBG001SFKH21', 'BBG001SFKH49', 'BBG001SFKKH8', 'BBG001SFKN73', 'BBG001SFKP87', 'BBG001SFKPS5', 'BBG001SFKPX9', 'BBG001SFKRX5', 'BBG001SFKS45', 'BBG001SFKSH1', 'BBG001SFKTF1', 'BBG001SFKW19', 'BBG001SFKYQ8', 'BBG001SFKZ03', 'BBG001SFL125', 'BBG001SFL1C4', 'BBG001SFL1Q9', 'BBG001SFL1R8', 'BBG001SFL2F9', 'BBG001SFL3W8', 'BBG001SFL438', 'BBG001SFL642', 'BBG001SFL9K8', 'BBG001SFL9Y3', 'BBG001SFLB87', 'BBG001SFLC94', 'BBG001SFLCV9', 'BBG001SFLF15', 'BBG001SFLGF8', 'BBG001SFLGL1', 'BBG001SFLKN0', 'BBG001SFLL12', 'BBG001SFLN09', 'BBG001SFLP31', 'BBG001SFLPW9', 'BBG001SFLQM8', 'BBG001SFLS17', 'BBG001SFLS53', 'BBG001SFLSF2', 'BBG001SFLVP4', 'BBG001SFLYP8', 'BBG001SFM071', 'BBG001SFM2W9', 'BBG001SFM3D8', 'BBG001SFM3Q4', 'BBG001SFM6N0', 'BBG001SFMCT1', 'BBG001SFMFS5', 'BBG001SFMKS4', 'BBG001SFMSZ9', 'BBG001SFMT05', 'BBG001SFMVV6', 'BBG001SFMWH0', 'BBG001SFMX15', 'BBG001SFMXT5', 'BBG001SFMXZ8', 'BBG001SFN1L2', 'BBG001SFN347', 'BBG001SFN3T0', 'BBG001SFN5N1', 'BBG001SFN6V0', 'BBG001SFN819', 'BBG001SFN828', 'BBG001SFN837', 'BBG001SFNB21', 'BBG001SFNBB1', 'BBG001SFNBR4', 'BBG001SFNHB8', 'BBG001SFNK58', 'BBG001SFNL56', 'BBG001SFNTK2', 'BBG001SFNVQ1', 'BBG001SFNY12', 'BBG001SFP586', 'BBG001SFP5W9', 'BBG001SFP737', 'BBG001SFP8J8', 'BBG001SFPB56', 'BBG001SFPFL9', 'BBG001SFPFM8', 'BBG001SFPHB6', 'BBG001SFPML4', 'BBG001SFPMM3', 'BBG001SFPNM1', 'BBG001SFPRR7', 'BBG001SFPV25', 'BBG001SFPXM9', 'BBG001SFPXR4', 'BBG001SFPZ35', 'BBG001SFPZC5', 'BBG001SFPZZ0', 'BBG001SFQ0P7', 'BBG001SFQ2T9', 'BBG001SFQ3Y1', 'BBG001SFQ610', 'BBG001SFQ6S1', 'BBG001SFQ7D5', 'BBG001SFQCC5', 'BBG001SFQDT5', 'BBG001SFQHQ9', 'BBG001SFQJ76', 'BBG001SFQJF7', 'BBG001SFQJM9', 'BBG001SFQJX7', 'BBG001SFQL08', 'BBG001SFQL26', 'BBG001SFQL35', 'BBG001SFQL44', 'BBG001SFQL80', 'BBG001SFQL99', 'BBG001SFQM51', 'BBG001SFQM79', 'BBG001SFQMB4', 'BBG001SFQNN9', 'BBG001SFQPM5', 'BBG001SFQQ89', 'BBG001SFQQ98', 'BBG001SFQSF7', 'BBG001SFQWB2', 'BBG001SFQXG5', 'BBG001SFQXK0', 'BBG001SFQXM8', 'BBG001SFQXN7', 'BBG001SFR0N8', 'BBG001SFR4X9', 'BBG001SFR5G5', 'BBG001SFR6R1', 'BBG001SFR879', 'BBG001SFR888', 'BBG001SFRD87', 'BBG001SFRDG8', 'BBG001SFRF82', 'BBG001SFRHK4', 'BBG001SFRK81', 'BBG001SFRL43', 'BBG001SFRLH9', 'BBG001SFRPF2', 'BBG001SFRQ79', 'BBG001SFRR04', 'BBG001SFRR22', 'BBG001SFRT28', 'BBG001SFRVY8', 'BBG001SFRWG6', 'BBG001SFRWP6', 'BBG001SFRZW1', 'BBG001SFS1Q2', 'BBG001SFS3R7', 'BBG001SFS4P7', 'BBG001SFS5M7', 'BBG001SFS6T8', 'BBG001SFS7Q9', 'BBG001SFS805', 'BBG001SFS869', 'BBG001SFS896', 'BBG001SFSBH0', 'BBG001SFSC97', 'BBG001SFSHM1', 'BBG001SFSK62', 'BBG001SFSKW3', 'BBG001SFSMY7', 'BBG001SFSNV8', 'BBG001SFSQW0', 'BBG001SFSQX9', 'BBG001SFSR85', 'BBG001SFSSG4', 'BBG001SFSXD6', 'BBG001SFSXJ0', 'BBG001SFSY71', 'BBG001SFSZT4', 'BBG001SFT1Z1', 'BBG001SFT225', 'BBG001SFT2R8', 'BBG001SFT412', 'BBG001SFTBB5', 'BBG001SFTCR6', 'BBG001SFTCX9', 'BBG001SFTD76', 'BBG001SFTG51', 'BBG001SFTK61', 'BBG001SFTQW9', 'BBG001SFTQZ6', 'BBG001SFTRS2', 'BBG001SFTTS8', 'BBG001SFTVM9', 'BBG001SFTY52', 'BBG001SFV1C3', 'BBG001SFV1J6', 'BBG001SFV3R3', 'BBG001SFV6Q7', 'BBG001SFV721', 'BBG001SFV7B1', 'BBG001SFV7S3', 'BBG001SFV7W8', 'BBG001SFVCJ2', 'BBG001SFVD28', 'BBG001SFVF50', 'BBG001SFVF87', 'BBG001SFVJ52', 'BBG001SFVK86', 'BBG001SFVKJ4', 'BBG001SFVM00', 'BBG001SFVMP3', 'BBG001SFVN99', 'BBG001SFVNG1', 'BBG001SFVPN8', 'BBG001SFVPP6', 'BBG001SFVQ29', 'BBG001SFVQ83', 'BBG001SFVS07', 'BBG001SFVS98', 'BBG001SFVSP0', 'BBG001SFVT96', 'BBG001SFVTK3', 'BBG001SFVVN5', 'BBG001SFVVW5', 'BBG001SFVWR9', 'BBG001SFVYV0', 'BBG001SFW1V1', 'BBG001SFWCV7', 'BBG001SFWFL1', 'BBG001SFWHY3', 'BBG001SFWKK1', 'BBG001SFWLL8', 'BBG001SFWR17', 'BBG001SFWS51', 'BBG001SFWTN9', 'BBG001SFWTV0', 'BBG001SFWVX3', 'BBG001SFWWY0', 'BBG001SFWXN0', 'BBG001SFWYX7', 'BBG001SFWYY6', 'BBG001SFWZ82', 'BBG001SFWZR1', 'BBG001SFX104', 'BBG001SFX2D8', 'BBG001SFX3M6', 'BBG001SFX3P3', 'BBG001SFX5G8', 'BBG001SFX836', 'BBG001SFXCR1', 'BBG001SFXDX2', 'BBG001SFXG65', 'BBG001SFXJ96', 'BBG001SFXJR6', 'BBG001SFXK75', 'BBG001SFXLJ0', 'BBG001SFXMP1', 'BBG001SFXTV9', 'BBG001SFXVG1', 'BBG001SFXY66', 'BBG001SFXYR3', 'BBG001SFXZ36', 'BBG001SFY3K7', 'BBG001SFY5P7', 'BBG001SFYCM5', 'BBG001SFYHV4', 'BBG001SFYWL2', 'BBG001SFYZT7', 'BBG001SFZ086', 'BBG001SFZ2P3', 'BBG001SFZ3S8', 'BBG001SFZ7N4', 'BBG001SFZ9N0', 'BBG001SFZ9Y8', 'BBG001SFZBL7', 'BBG001SFZD60', 'BBG001SFZDJ6', 'BBG001SFZKN5', 'BBG001SFZL99', 'BBG001SFZLN3', 'BBG001SFZNJ4', 'BBG001SFZQJ7', 'BBG001SFZQL4', 'BBG001SFZRR6', 'BBG001SG00G3', 'BBG001SG03X8', 'BBG001SG0723', 'BBG001SG09V7', 'BBG001SG0B51', 'BBG001SG0F07', 'BBG001SG0GN0', 'BBG001SG0H12', 'BBG001SG0JW4', 'BBG001SG0K79', 'BBG001SG0M20', 'BBG001SG0M48', 'BBG001SG0TR8', 'BBG001SG0V75', 'BBG001SG0VF6', 'BBG001SG0X80', 'BBG001SG1571', 'BBG001SG15B6', 'BBG001SG17S4', 'BBG001SG17T3', 'BBG001SG1K14', 'BBG001SG1LH5', 'BBG001SG1LP6', 'BBG001SG1LY6', 'BBG001SG1ML8', 'BBG001SG1MZ3', 'BBG001SG1NR0', 'BBG001SG1WQ1', 'BBG001SG1XF1', 'BBG001SG1ZV8', 'BBG001SG23W7', 'BBG001SG24P3', 'BBG001SG2954', 'BBG001SG29F3', 'BBG001SG2BG7', 'BBG001SG2DQ2', 'BBG001SG2JJ7', 'BBG001SG2M46', 'BBG001SG2NJ8', 'BBG001SG2VR1', 'BBG001SG2X24', 'BBG001SG37B0', 'BBG001SG37M8', 'BBG001SG3CC8', 'BBG001SG3GG5', 'BBG001SG3HZ2', 'BBG001SG3JV2', 'BBG001SG3K30', 'BBG001SG3KP6', 'BBG001SG3L56', 'BBG001SG3M27', 'BBG001SG3MY2', 'BBG001SG3MZ1', 'BBG001SG3TN9', 'BBG001SG3WS7', 'BBG001SG3Y58', 'BBG001SG40N1', 'BBG001SG4195', 'BBG001SG4257', 'BBG001SG4275', 'BBG001SG4284', 'BBG001SG42D8', 'BBG001SG4435', 'BBG001SG46V9', 'BBG001SG47G4', 'BBG001SG4970', 'BBG001SG4B84', 'BBG001SG4DJ8', 'BBG001SG4DQ0', 'BBG001SG4DX2', 'BBG001SG4DY1', 'BBG001SG4F76', 'BBG001SG4F94', 'BBG001SG4FG6', 'BBG001SG4FM9', 'BBG001SG4FR4', 'BBG001SG4FT2', 'BBG001SG4FW8', 'BBG001SG4FX7', 'BBG001SG4G01', 'BBG001SG4G29', 'BBG001SG4G47', 'BBG001SG4G74', 'BBG001SG4G92', 'BBG001SG4GD7', 'BBG001SG4GF5', 'BBG001SG4GH3', 'BBG001SG4GJ1', 'BBG001SG4GR2', 'BBG001SG4GW6', 'BBG001SG4H18', 'BBG001SG4H63', 'BBG001SG4NL3', 'BBG001SG4NN1', 'BBG001SG4PF5', 'BBG001SG4PQ3', 'BBG001SG4WZ8', 'BBG001SG4XK2', 'BBG001SG4Z09', 'BBG001SG4Z27', 'BBG001SG53D5', 'BBG001SG5559', 'BBG001SG56C9', 'BBG001SG5DQ9', 'BBG001SG5DW2', 'BBG001SG5G91', 'BBG001SG5KF5', 'BBG001SG5P46', 'BBG001SG5PL7', 'BBG001SG5PR1', 'BBG001SG5RL3', 'BBG001SG5VG0', 'BBG001SG6175', 'BBG001SG6380', 'BBG001SG6567', 'BBG001SG65Q5', 'BBG001SG65Z5', 'BBG001SG6843', 'BBG001SG6DZ8', 'BBG001SG6K73', 'BBG001SG6LT7', 'BBG001SG6RL2', 'BBG001SG6ZX1', 'BBG001SG7986', 'BBG001SG7C98', 'BBG001SG7F64', 'BBG001SG7G71', 'BBG001SG7J11', 'BBG001SG7ML2', 'BBG001SG7SC9', 'BBG001SG80W7', 'BBG001SG82Y1', 'BBG001SG83T5', 'BBG001SG84J4', 'BBG001SG87L4', 'BBG001SG87Y0', 'BBG001SG88H7', 'BBG001SG8C24', 'BBG001SG8C60', 'BBG001SG8GH9', 'BBG001SG8GM3', 'BBG001SG8HX9', 'BBG001SG8KX2', 'BBG001SG8MN9', 'BBG001SG8MR5', 'BBG001SG8W48', 'BBG001SG8WT1', 'BBG001SG8Z87', 'BBG001SG95B8', 'BBG001SG98G7', 'BBG001SG98H6', 'BBG001SG9CL2', 'BBG001SG9D58', 'BBG001SG9FS8', 'BBG001SG9G79', 'BBG001SG9H22', 'BBG001SG9HF8', 'BBG001SG9HM0', 'BBG001SG9KH9', 'BBG001SG9PM2', 'BBG001SG9SZ2', 'BBG001SG9V85', 'BBG001SG9WK9', 'BBG001SG9YB5', 'BBG001SGB026', 'BBG001SGB1W1', 'BBG001SGB2G7', 'BBG001SGB2J4', 'BBG001SGB4T9', 'BBG001SGB561', 'BBG001SGB5Y0', 'BBG001SGB641', 'BBG001SGBBG7', 'BBG001SGBC02', 'BBG001SGBC48', 'BBG001SGBGW8', 'BBG001SGBJP0', 'BBG001SGBJW2', 'BBG001SGBLP5', 'BBG001SGBML7', 'BBG001SGBNY1', 'BBG001SGBNZ0', 'BBG001SGBPV9', 'BBG001SGBQ74', 'BBG001SGBQD7', 'BBG001SGBWR9', 'BBG001SGBXM2', 'BBG001SGBXP9', 'BBG001SGBYW9', 'BBG001SGC3C8', 'BBG001SGC5L3', 'BBG001SGC828', 'BBG001SGC8M6', 'BBG001SGC953', 'BBG001SGC9Z0', 'BBG001SGCFT3', 'BBG001SGCJY9', 'BBG001SGCKY6', 'BBG001SGCL92', 'BBG001SGCLB9', 'BBG001SGCRG1', 'BBG001SGCTD0', 'BBG001SGCVF3', 'BBG001SGCYR4', 'BBG001SGD499', 'BBG001SGD6C0', 'BBG001SGD7G4', 'BBG001SGD8Z1', 'BBG001SGDB57', 'BBG001SGDC64', 'BBG001SGDF49', 'BBG001SGDHN4', 'BBG001SGDJ23', 'BBG001SGDN51', 'BBG001SGDQF3', 'BBG001SGDR16', 'BBG001SGDV17', 'BBG001SGDX59', 'BBG001SGDXC1', 'BBG001SGF3N3', 'BBG001SGF5P6', 'BBG001SGF8F1', 'BBG001SGFG09', 'BBG001SGFMC3', 'BBG001SGFMK4', 'BBG001SGFR32', 'BBG001SGFTT0', 'BBG001SGFXV8', 'BBG001SGG5R3', 'BBG001SGG5W7', 'BBG001SGGDL2', 'BBG001SGGFG3', 'BBG001SGGK90', 'BBG001SGGKG2', 'BBG001SGGKT8', 'BBG001SGGP80', 'BBG001SGGQX0', 'BBG001SGGR22', 'BBG001SGH2P1', 'BBG001SGH2S8', 'BBG001SGH3R7', 'BBG001SGH8Q7', 'BBG001SGH967', 'BBG001SGH9N8', 'BBG001SGHBP1', 'BBG001SGHH14', 'BBG001SGHHZ7', 'BBG001SGHKN3', 'BBG001SGHPN2', 'BBG001SGHSS1', 'BBG001SGHSV7', 'BBG001SGHVD0', 'BBG001SGHYV4', 'BBG001SGJ1L5', 'BBG001SGJ4L9', 'BBG001SGJ5F3', 'BBG001SGJ714', 'BBG001SGJ8Q5', 'BBG001SGJ929', 'BBG001SGJD57', 'BBG001SGJG96', 'BBG001SGJGZ7', 'BBG001SGJH12', 'BBG001SGJHW8', 'BBG001SGJJB7', 'BBG001SGJJN4', 'BBG001SGJLB2', 'BBG001SGJPH7', 'BBG001SGJRC8', 'BBG001SGJTK5', 'BBG001SGJVC9', 'BBG001SGJYW1', 'BBG001SGK1B4', 'BBG001SGK758', 'BBG001SGKBW9', 'BBG001SGKBY7', 'BBG001SGKBZ6', 'BBG001SGKC20', 'BBG001SGKR63', 'BBG001SGKST6', 'BBG001SGKTJ5', 'BBG001SGKV64', 'BBG001SGL070', 'BBG001SGL0H9', 'BBG001SGL123', 'BBG001SGL1J5', 'BBG001SGL3Y4', 'BBG001SGL604', 'BBG001SGL6R5', 'BBG001SGL748', 'BBG001SGL8G3', 'BBG001SGL9Y1', 'BBG001SGLD63', 'BBG001SGLDS9', 'BBG001SGLF40', 'BBG001SGLH64', 'BBG001SGLHS0', 'BBG001SGLJ88', 'BBG001SGLMW4', 'BBG001SGLP11', 'BBG001SGLPS2', 'BBG001SGLQH2', 'BBG001SGLTC1', 'BBG001SGLW52', 'BBG001SGLXW0', 'BBG001SGLYM9', 'BBG001SGLZ37', 'BBG001SGM3F4', 'BBG001SGM5P8', 'BBG001SGM6M9', 'BBG001SGMBX6', 'BBG001SGMFJ3', 'BBG001SGMH90', 'BBG001SGMQ27', 'BBG001SGMVF2', 'BBG001SGMYJ2', 'BBG001SGN0C2', 'BBG001SGN0V1', 'BBG001SGN1L0', 'BBG001SGNC72', 'BBG001SGNL18', 'BBG001SGNMC4', 'BBG001SGNMK5', 'BBG001SGNNQ7', 'BBG001SGNPK8', 'BBG001SGNQG1', 'BBG001SGNX85', 'BBG001SGNYC8', 'BBG001SGNYQ3', 'BBG001SGP2V5', 'BBG001SGP2W4', 'BBG001SGP423', 'BBG001SGP7W3', 'BBG001SGP7Z0', 'BBG001SGPCF1', 'BBG001SGPF64', 'BBG001SGPKJ9', 'BBG001SGPQB4', 'BBG001SGPR59', 'BBG001SGPWZ5', 'BBG001SGPXJ1', 'BBG001SGPY09', 'BBG001SGQ002', 'BBG001SGQ2M4', 'BBG001SGQ4W9', 'BBG001SGQ6K7', 'BBG001SGQ9T2', 'BBG001SGQBL5', 'BBG001SGQD22', 'BBG001SGQFR0', 'BBG001SGQL06', 'BBG001SGQMB2', 'BBG001SGQND8', 'BBG001SGQQR6', 'BBG001SGQRM9', 'BBG001SGQWF6', 'BBG001SGQY62', 'BBG001SGQYB6', 'BBG001SGQYM4', 'BBG001SGR494', 'BBG001SGR5Q2', 'BBG001SGR6Q0', 'BBG001SGR859', 'BBG001SGRBK5', 'BBG001SGRMS3', 'BBG001SGRT26', 'BBG001SGRVF7', 'BBG001SGRW38', 'BBG001SGRY43', 'BBG001SGRZ86', 'BBG001SGS2Z8', 'BBG001SGSC04', 'BBG001SGSMW7', 'BBG001SGSP96', 'BBG001SGSRC8', 'BBG001SGSSF3', 'BBG001SGSZK1', 'BBG001SGT296', 'BBG001SGT4X5', 'BBG001SGT731', 'BBG001SGT8G5', 'BBG001SGTCH5', 'BBG001SGTCW8', 'BBG001SGTD56', 'BBG001SGTD74', 'BBG001SGTGK2', 'BBG001SGTPK2', 'BBG001SGTQB0', 'BBG001SGTVC8', 'BBG001SGTVM7', 'BBG001SGTWW4', 'BBG001SGTWZ1', 'BBG001SGV202', 'BBG001SGV417', 'BBG001SGV444', 'BBG001SGV4D4', 'BBG001SGV4P1', 'BBG001SGV569', 'BBG001SGVBF6', 'BBG001SGVD71', 'BBG001SGVH18', 'BBG001SGVHD5', 'BBG001SGVK75', 'BBG001SGVK84', 'BBG001SGVNJ6', 'BBG001SGVNZ8', 'BBG001SGVS78', 'BBG001SGVY93', 'BBG001SGW2S1', 'BBG001SGW3M5', 'BBG001SGW5F8', 'BBG001SGW6B0', 'BBG001SGW6F6', 'BBG001SGW6H4', 'BBG001SGWBH3', 'BBG001SGWCD5', 'BBG001SGWD25', 'BBG001SGWDS7', 'BBG001SGWK56', 'BBG001SGWWN0', 'BBG001SGWXQ5', 'BBG001SGX399', 'BBG001SGX3X2', 'BBG001SGX585', 'BBG001SGX6D7', 'BBG001SGXB64', 'BBG001SGXCQ0', 'BBG001SGXCY1', 'BBG001SGXKR1', 'BBG001SGXLB6', 'BBG001SGXNC1', 'BBG001SGXPS9', 'BBG001SGXPT8', 'BBG001SGXPX3', 'BBG001SGXPY2', 'BBG001SGXQ07', 'BBG001SGXVB4', 'BBG001SGXVD2', 'BBG001SGXZ16', 'BBG001SGY0F7', 'BBG001SGY138', 'BBG001SGY2X3', 'BBG001SGY325', 'BBG001SGY4N0', 'BBG001SGY6K8', 'BBG001SGY7M4', 'BBG001SGYD96', 'BBG001SGYHK4', 'BBG001SGYN03', 'BBG001SGYN21', 'BBG001SGYST1', 'BBG001SGYSW7', 'BBG001SGYWQ5', 'BBG001SGYXJ1', 'BBG001SGYYG2', 'BBG001SGYYT8', 'BBG001SGZ4V0', 'BBG001SGZBN3', 'BBG001SGZDS4', 'BBG001SGZH23', 'BBG001SGZJP4', 'BBG001SH39G9', 'BBG001SH39H8', 'BBG001SH3DY0', 'BBG001SH3WJ5', 'BBG001SH3XT2', 'BBG001SH44F0', 'BBG001SH44Z8', 'BBG001SH4BQ2', 'BBG001SH4BR1', 'BBG001SH4JJ3', 'BBG001SH4JL0', 'BBG001SH4LQ0', 'BBG001SH4R23', 'BBG001SH4TR2', 'BBG001SH5BX3', 'BBG001SH5H24', 'BBG001SH5MD1', 'BBG001SH5X74', 'BBG001SH5Y72', 'BBG001SH6C79', 'BBG001SH6D77', 'BBG001SH6D86', 'BBG001SH6P16', 'BBG001SH6PD3', 'BBG001SH6Q32', 'BBG001SH6Q78', 'BBG001SH6V77', 'BBG001SH6V95', 'BBG001SH6X00', 'BBG001SH73L2', 'BBG001SH7449', 'BBG001SH7500', 'BBG001SH7760', 'BBG001SH7C50', 'BBG001SH7JJ0', 'BBG001SH7K07', 'BBG001SH7K34', 'BBG001SH7MN8', 'BBG001SH7Q77', 'BBG001SH7RD8', 'BBG001SH7S00', 'BBG001SH80B8', 'BBG001SH80F4', 'BBG001SH81D4', 'BBG001SH81N3', 'BBG001SH8359', 'BBG001SH8GP8', 'BBG001SH8J63', 'BBG001SH8NW5', 'BBG001SH8Q58', 'BBG001SH8RG4', 'BBG001SH8RT0', 'BBG001SH8V39', 'BBG001SH8V66', 'BBG001SH8XS8', 'BBG001SH8YL3', 'BBG001SH92P8', 'BBG001SH92R6', 'BBG001SH9CG6', 'BBG001SH9DM7', 'BBG001SH9HH4', 'BBG001SH9K05', 'BBG001SH9LZ5', 'BBG001SH9PV0', 'BBG001SH9ST7', 'BBG001SH9SV4', 'BBG001SH9YZ7', 'BBG001SH9ZW7', 'BBG001SHB1G7', 'BBG001SHB1X8', 'BBG001SHB5Z7', 'BBG001SHBFC0', 'BBG001SHBG47', 'BBG001SHBK57', 'BBG001SHBKS2', 'BBG001SHBR07', 'BBG001SHBRR8', 'BBG001SHBT12', 'BBG001SHBV08', 'BBG001SHC0N0', 'BBG001SHC3X3', 'BBG001SHC611', 'BBG001SHC7K8', 'BBG001SHC9Z8', 'BBG001SHCCJ9', 'BBG001SHCMV3', 'BBG001SHCT84', 'BBG001SHCXC0', 'BBG001SHD4R7', 'BBG001SHD5V9', 'BBG001SHD692', 'BBG001SHD6Y4', 'BBG001SHD8F1', 'BBG001SHD9F9', 'BBG001SHDDC3', 'BBG001SHDWV0', 'BBG001SHDYS0', 'BBG001SHF1H2', 'BBG001SHF1J0', 'BBG001SHF501', 'BBG001SHFG34', 'BBG001SHFG98', 'BBG001SHFGB5', 'BBG001SHFKY1', 'BBG001SHFMK2', 'BBG001SHG2Q9', 'BBG001SHG9Q4', 'BBG001SHGV76', 'BBG001SHHBR7', 'BBG001SHHD02', 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'BBG001SKHT19', 'BBG001SKHV14', 'BBG001SKHWM9', 'BBG001SKHZW1', 'BBG001SKJ3H7', 'BBG001SKJ4B1', 'BBG001SKJ4K1', 'BBG001SKJ5G3', 'BBG001SKJ797', 'BBG001SKJB25', 'BBG001SKJB43', 'BBG001SKJB98', 'BBG001SKJBX1', 'BBG001SKJBZ9', 'BBG001SKJG51', 'BBG001SKJGF0', 'BBG001SKJJT9', 'BBG001SKJMD9', 'BBG001SKJV12', 'BBG001SKJVR4', 'BBG001SKJYG0', 'BBG001SKJZH6', 'BBG001SKK063', 'BBG001SKK0H1', 'BBG001SKK1Z9', 'BBG001SKK287', 'BBG001SKK3J3', 'BBG001SKK3W8', 'BBG001SKK7B2', 'BBG001SKK875', 'BBG001SKK946', 'BBG001SKKCS3', 'BBG001SKKGS4', 'BBG001SKKJC5', 'BBG001SKKK23', 'BBG001SKKTY9', 'BBG001SKKV10', 'BBG001SKKV29', 'BBG001SKL0F2', 'BBG001SKL5K5', 'BBG001SKL5N2', 'BBG001SKL9Y2', 'BBG001SKLF23', 'BBG001SKLFF9', 'BBG001SKLLN7', 'BBG001SKLT23', 'BBG001SKLW53', 'BBG001SKLXS6', 'BBG001SKLXW1', 'BBG001SKM1Q7', 'BBG001SKM1S5', 'BBG001SKM3S1', 'BBG001SKM445', 'BBG001SKM4V5', 'BBG001SKMCQ3', 'BBG001SKMDL6', 'BBG001SKMKG6', 'BBG001SKML10', 'BBG001SKMM63', 'BBG001SKMNW2', 'BBG001SKMNX1', 'BBG001SKMRS8', 'BBG001SKMS06', 'BBG001SKMVM5', 'BBG001SKMW43', 'BBG001SKMWH9', 'BBG001SKMWN2', 'BBG001SKMWS7', 'BBG001SKMYG6', 'BBG001SKN3Q2', 'BBG001SKN5R6', 'BBG001SKN6N8', 'BBG001SKN8K7', 'BBG001SKNBV8', 'BBG001SKND62', 'BBG001SKNHF3', 'BBG001SKNM26', 'BBG001SKNN24', 'BBG001SKNNS6', 'BBG001SKNQX3', 'BBG001SKNSX9', 'BBG001SKP1H4', 'BBG001SKP2Y3', 'BBG001SKP8S7', 'BBG001SKP8T6', 'BBG001SKP8X1', 'BBG001SKP8Y0', 'BBG001SKPB00', 'BBG001SKPB28', 'BBG001SKPCY1', 'BBG001SKPGT8', 'BBG001SKPH70', 'BBG001SKPLD4', 'BBG001SKPT56', 'BBG001SKPXB0', 'BBG001SKQ1B9', 'BBG001SKQ2Y2', 'BBG001SKQ2Z1', 'BBG001SKQ8Q8', 'BBG001SKQ8S6', 'BBG001SKQ968', 'BBG001SKQLN2', 'BBG001SKQLW2', 'BBG001SKQPK6', 'BBG001SKQWD9', 'BBG001SKQWJ3', 'BBG001SKQX38', 'BBG001SKQYR0', 'BBG001SKR1T9', 'BBG001SKR574', 'BBG001SKR7K5', 'BBG001SKR9Y6', 'BBG001SKRBD4', 'BBG001SKRCS6', 'BBG001SKRCV2', 'BBG001SKRCW1', 'BBG001SKRDN9', 'BBG001SKRDR5', 'BBG001SKRF36', 'BBG001SKRFF3', 'BBG001SKRFP2', 'BBG001SKRFR0', 'BBG001SKRHH7', 'BBG001SKRJJ1', 'BBG001SKRJP4', 'BBG001SKRL88', 'BBG001SKRQ05', 'BBG001SKRSM7', 'BBG001SKRSS1', 'BBG001SKRSW6', 'BBG001SKRV04', 'BBG001SKRYS8', 'BBG001SKS500', 'BBG001SKS519', 'BBG001SKS528', 'BBG001SKS653', 'BBG001SKSDQ5', 'BBG001SKSF62', 'BBG001SKSFQ0', 'BBG001SKSKG0', 'BBG001SKSKH9', 'BBG001SKSKR8', 'BBG001SKSMC0', 'BBG001SKSMX7', 'BBG001SKSWT0', 'BBG001SKT117', 'BBG001SKT153', 'BBG001SKT1Z0', 'BBG001SKT4P5', 'BBG001SKT8Q5', 'BBG001SKTBJ6', 'BBG001SKTF43', 'BBG001SKTF61', 'BBG001SKTSW4', 'BBG001SKTTF1', 'BBG001SKTV57', 'BBG001SKTZ12', 'BBG001SKV276', 'BBG001SKV3R2', 'BBG001SKV3V7', 'BBG001SKV695', 'BBG001SKV6G7', 'BBG001SKV828', 'BBG001SKVC47', 'BBG001SKVFK2', 'BBG001SKVHX4', 'BBG001SKVJY9', 'BBG001SKW104', 'BBG001SKW373', 'BBG001SKW426', 'BBG001SKW569', 'BBG001SKW6M9', 'BBG001SKWCS0', 'BBG001SKWDQ0', 'BBG001SKWHD5', 'BBG001SKWHM5', 'BBG001SKWHR0', 'BBG001SKWK57', 'BBG001SKWPZ3', 'BBG001SKWSL2', 'BBG001SKWX22', 'BBG001SKWX40', 'BBG001SKWXZ6', 'BBG001SKX0D1', 'BBG001SKX1F7', 'BBG001SKX1G6', 'BBG001SKX238', 'BBG001SKX3C6', 'BBG001SKX3N4', 'BBG001SKX4P0', 'BBG001SKX4R8', 'BBG001SKX9R7', 'BBG001SKX9S6', 'BBG001SKXC54', 'BBG001SKXLQ1', 'BBG001SKXM70', 'BBG001SKXPR1', 'BBG001SKXTM8', 'BBG001SKXV70', 'BBG001SKXV98', 'BBG001SKXVB5', 'BBG001SKXW87', 'BBG001SKY6D7', 'BBG001SKY7X3', 'BBG001SKY923', 'BBG001SKYGZ1', 'BBG001SKYK55', 'BBG001SKYP45', 'BBG001SKYP54', 'BBG001SKYP63', 'BBG001SKYQH9', 'BBG001SKYTG4', 'BBG001SKZ209', 'BBG001SKZ9J4', 'BBG001SKZ9N9', 'BBG001SKZ9P7', 'BBG001SKZ9Q6', 'BBG001SKZB54', 'BBG001SKZF64', 'BBG001SKZFD6', 'BBG001SKZG99', 'BBG001SKZLC4', 'BBG001SKZM78', 'BBG001SKZNF7', 'BBG001SKZQ15', 'BBG001SKZSF6', 'BBG001SKZX56', 'BBG001SL03C0', 'BBG001SL04M7', 'BBG001SL08F6', 'BBG001SL0CR4', 'BBG001SL0DP4', 'BBG001SL0FX0', 'BBG001SL0KP8', 'BBG001SL0LJ3', 'BBG001SL0Q11', 'BBG001SL0Q48', 'BBG001SL1053', 'BBG001SL1BD0', 'BBG001SL1C93', 'BBG001SL1DQ2', 'BBG001SL1DS0', 'BBG001SL1J52', 'BBG001SL1LW7', 'BBG001SL1PP6', 'BBG001SL1ZT0', 'BBG001SL20P0', 'BBG001SL26C1', 'BBG001SL2739', 'BBG001SL27N7', 'BBG001SL27R3', 'BBG001SL28N5', 'BBG001SL2C10', 'BBG001SL2CQ3', 'BBG001SL2D63', 'BBG001SL2FT3', 'BBG001SL2K30', 'BBG001SL2KB1', 'BBG001SL2L47', 'BBG001SL2Q82', 'BBG001SL30S6', 'BBG001SL3DN3', 'BBG001SL3GQ3', 'BBG001SL3HM5', 'BBG001SL3LN5', 'BBG001SL3LQ2', 'BBG001SL3TS3', 'BBG001SL3WS6', 'BBG001SL3ZH1', 'BBG001SL3ZY2', 'BBG001SL42X5', 'BBG001SL46L9', 'BBG001SL46X6', 'BBG001SL4LS9', 'BBG001SL4QM4', 'BBG001SL4TS2', 'BBG001SL4YJ1', 'BBG001SL4YT0', 'BBG001SL51D8', 'BBG001SL52N5', 'BBG001SL52Q2', 'BBG001SL56K9', 'BBG001SL57V5', 'BBG001SL5898', 'BBG001SL5DV2', 'BBG001SL5GB7', 'BBG001SL5GC6', 'BBG001SL5GH1', 'BBG001SL5GW4', 'BBG001SL5N13', 'BBG001SL5TM7', 'BBG001SL5Y46', 'BBG001SL64C2', 'BBG001SL64H7', 'BBG001SL6GN3', 'BBG001SL6GW3', 'BBG001SL6L70', 'BBG001SL6PR9', 'BBG001SL6PZ0', 'BBG001SL6QR7', 'BBG001SL6R95', 'BBG001SL6SX6', 'BBG001SL6T73', 'BBG001SL6WF7', 'BBG001SL6YH1', 'BBG001SL6Z15', 'BBG001SL72C5', 'BBG001SL72W3', 'BBG001SL7HR6', 'BBG001SL7HY8', 'BBG001SL7J47', 'BBG001SL7JD7', 'BBG001SL7KY1', 'BBG001SL7V04', 'BBG001SL7YQ0', 'BBG001SL7YT7', 'BBG001SL7Z05', 'BBG001SL7ZR6', 'BBG001SL81Y2', 'BBG001SL84P6', 'BBG001SL8DD9', 'BBG001SL8HX8', 'BBG001SL8J00', 'BBG001SL8J46', 'BBG001SL8J64', 'BBG001SL8JB8', 'BBG001SL8JM6', 'BBG001SL8SB8', 'BBG001SL8SD6', 'BBG001SL8TJ8', 'BBG001SL8TR9', 'BBG001SL8XW4', 'BBG001SL90J0', 'BBG001SL92H8', 'BBG001SL9368', 'BBG001SL9402', 'BBG001SL9457', 'BBG001SL94R3', 'BBG001SL95H1', 'BBG001SL96Z9', 'BBG001SL97H7', 'BBG001SL9C95', 'BBG001SL9G14', 'BBG001SL9GM1', 'BBG001SL9HH5', 'BBG001SL9MQ4', 'BBG001SL9Q30', 'BBG001SL9R65', 'BBG001SL9RV7', 'BBG001SL9SH1', 'BBG001SL9SX3', 'BBG001SL9VM8', 'BBG001SLB4K7', 'BBG001SLB9Q0', 'BBG001SLBBB1', 'BBG001SLBD27', 'BBG001SLBF22', 'BBG001SLBK12', 'BBG001SLBKJ3', 'BBG001SLBMH1', 'BBG001SLBPN7', 'BBG001SLBVW4', 'BBG001SLBY94', 'BBG001SLC4P1', 'BBG001SLCHJ9', 'BBG001SLCK57', 'BBG001SLCQ18', 'BBG001SLD3K7', 'BBG001SLD4X1', 'BBG001SLD6T1', 'BBG001SLD871', 'BBG001SLD979', 'BBG001SLD9C3', 'BBG001SLD9D2', 'BBG001SLDC63', 'BBG001SLDJ59', 'BBG001SLDPD6', 'BBG001SLDQ80', 'BBG001SLF370', 'BBG001SLF4N0', 'BBG001SLF4P8', 'BBG001SLFB27', 'BBG001SLFCT6', 'BBG001SLFDF9', 'BBG001SLFDK3', 'BBG001SLFJB0', 'BBG001SLFKM5', 'BBG001SLFQ79', 'BBG001SLFVR6', 'BBG001SLFYY2', 'BBG001SLG5M7', 'BBG001SLG672', 'BBG001SLGGJ7', 'BBG001SLGHK3', 'BBG001SLGJ92', 'BBG001SLGM77', 'BBG001SLGR12', 'BBG001SLGV22', 'BBG001SLGXT9', 'BBG001SLH608', 'BBG001SLH7Q8', 'BBG001SLHHB2', 'BBG001SLHL96', 'BBG001SLHMT2', 'BBG001SLHNX5', 'BBG001SLHRS2', 'BBG001SLHSL7', 'BBG001SLHT35', 'BBG001SLHV21', 'BBG001SLHVV9', 'BBG001SLHZV0', 'BBG001SLJ198', 'BBG001SLJ624', 'BBG001SLJR19', 'BBG001SLJV56', 'BBG001SLJVN6', 'BBG001SLK0B5', 'BBG001SLK1R6', 'BBG001SLK3B9', 'BBG001SLK5M2', 'BBG001SLK8C7', 'BBG001SLKBJ3', 'BBG001SLKBL0', 'BBG001SLKBM9', 'BBG001SLKBR4', 'BBG001SLKD81', 'BBG001SLKDX3', 'BBG001SLKFL1', 'BBG001SLKGB0', 'BBG001SLKJK4', 'BBG001SLKK49', 'BBG001SLKL38', 'BBG001SLKNW2', 'BBG001SLKPP5', 'BBG001SLKS33', 'BBG001SLKS51', 'BBG001SLKYH5', 'BBG001SLKYJ3', 'BBG001SLL7N6', 'BBG001SLL934', 'BBG001SLLJ78', 'BBG001SLLLY3', 'BBG001SLLQV5', 'BBG001SLLS23', 'BBG001SLLWL3', 'BBG001SLLZM5', 'BBG001SLM3W4', 'BBG001SLM3Z1', 'BBG001SLM443', 'BBG001SLM522', 'BBG001SLMFB0', 'BBG001SLMK47', 'BBG001SLMK92', 'BBG001SLMKX5', 'BBG001SLMLM5', 'BBG001SLMNQ7', 'BBG001SLMP91', 'BBG001SLN031', 'BBG001SLN1H4', 'BBG001SLN3H0', 'BBG001SLN5S3', 'BBG001SLNBM6', 'BBG001SLNKV6', 'BBG001SLNQ61', 'BBG001SLNQD3', 'BBG001SLNSP6', 'BBG001SLNWJ4', 'BBG001SLNWP7', 'BBG001SLNXS2', 'BBG001SLP413', 'BBG001SLP431', 'BBG001SLP4P7', 'BBG001SLP5D7', 'BBG001SLP6Q1', 'BBG001SLPGH9', 'BBG001SLPHZ7', 'BBG001SLPMY7', 'BBG001SLPP25', 'BBG001SLPX37', 'BBG001SLQ109', 'BBG001SLQ6G1', 'BBG001SLQ6K6', 'BBG001SLQ6N3', 'BBG001SLQ8F8', 'BBG001SLQG97', 'BBG001SLQNX5', 'BBG001SLQPB4', 'BBG001SLQV49', 'BBG001SLQWW6', 'BBG001SLR359', 'BBG001SLR3C1', 'BBG001SLR625', 'BBG001SLR8B1', 'BBG001SLR8S3', 'BBG001SLRCD0', 'BBG001SLRCW9', 'BBG001SLRDN7', 'BBG001SLRDP5', 'BBG001SLRNY3', 'BBG001SLRWP3', 'BBG001SLS3T2', 'BBG001SLS4Q3', 'BBG001SLS606', 'BBG001SLS697', 'BBG001SLS7B2', 'BBG001SLS973', 'BBG001SLSBV1', 'BBG001SLSDV7', 'BBG001SLSF60', 'BBG001SLSHC9', 'BBG001SLSHZ4', 'BBG001SLSKN0', 'BBG001SLSN72', 'BBG001SLSQM8', 'BBG001SLSWH1', 'BBG001SLSXG0', 'BBG001SLSXK5', 'BBG001SLSXM3', 'BBG001SLT0M4', 'BBG001SLT758', 'BBG001SLTC75', 'BBG001SLTFJ5', 'BBG001SLTGB1', 'BBG001SLTPP6', 'BBG001SLTQD7', 'BBG001SLTSR8', 'BBG001SLTZG4', 'BBG001SLV078', 'BBG001SLV0G8', 'BBG001SLV0Q7', 'BBG001SLV4J7', 'BBG001SLVFY5', 'BBG001SLVJ95', 'BBG001SLVJZ6', 'BBG001SLVT11', 'BBG001SLVTL9', 'BBG001SLVXW8', 'BBG001SLW0V0', 'BBG001SLW3L5', 'BBG001SLW4C3', 'BBG001SLW549', 'BBG001SLW8L4', 'BBG001SLWD88', 'BBG001SLWGL6', 'BBG001SLWM79', 'BBG001SLWNS4', 'BBG001SLWQF1', 'BBG001SLWR05', 'BBG001SLWWP7', 'BBG001SLWWR5', 'BBG001SLWY37', 'BBG001SLX1D7', 'BBG001SLX1V7', 'BBG001SLX290', 'BBG001SLX2D5', 'BBG001SLX655', 'BBG001SLX7F2', 'BBG001SLX7G1', 'BBG001SLX9R5', 'BBG001SLXGZ0', 'BBG001SLXKF3', 'BBG001SLXRL1', 'BBG001SLXTB8', 'BBG001SLXTP3', 'BBG001SLXYK7', 'BBG001SLXYL6', 'BBG001SLY0X6', 'BBG001SLY155', 'BBG001SLY235', 'BBG001SLY244', 'BBG001SLY2K6', 'BBG001SLY2R9', 'BBG001SLY556', 'BBG001SLY7R8', 'BBG001SLY9X7', 'BBG001SLYQX9', 'BBG001SLYWD8', 'BBG001SLYY17', 'BBG001SLZ5Y3', 'BBG001SLZ671', 'BBG001SLZ724', 'BBG001SLZ8D0', 'BBG001SLZ939', 'BBG001SLZ9R3', 'BBG001SLZBJ7', 'BBG001SLZC23', 'BBG001SLZF44', 'BBG001SLZFT7', 'BBG001SLZLJ5', 'BBG001SM02X7', 'BBG001SM06F8', 'BBG001SM0819', 'BBG001SM08B8', 'BBG001SM08F4', 'BBG001SM0GX6', 'BBG001SM0HS0', 'BBG001SM0K30', 'BBG001SM0K49', 'BBG001SM0TL1', 'BBG001SM0W43', 'BBG001SM13G3', 'BBG001SM16W8', 'BBG001SM1DK6', 'BBG001SM1G56', 'BBG001SM1J50', 'BBG001SM1QQ1', 'BBG001SM1QT8', 'BBG001SM1RN2', 'BBG001SM1SS5', 'BBG001SM1T49', 'BBG001SM1TX7', 'BBG001SM1Z18', 'BBG001SM2112', 'BBG001SM21J3', 'BBG001SM2210', 'BBG001SM2390', 'BBG001SM23F3', 'BBG001SM24M3', 'BBG001SM28D4', 'BBG001SM29R7', 'BBG001SM2F48', 'BBG001SM2G73', 'BBG001SM2GJ0', 'BBG001SM2GK8', 'BBG001SM2GR1', 'BBG001SM2LG2', 'BBG001SM2MF1', 'BBG001SM2MK5', 'BBG001SM2PW5', 'BBG001SM2Q26', 'BBG001SM2S77', 'BBG001SM30Y7', 'BBG001SM31X6', 'BBG001SM32G3', 'BBG001SM3736', 'BBG001SM3BF4', 'BBG001SM3BQ2', 'BBG001SM3CC5', 'BBG001SM3DP9', 'BBG001SM3GY2', 'BBG001SM3KX4', 'BBG001SM3Q16', 'BBG001SM3VF0', 'BBG001SM3VS6', 'BBG001SM44F9', 'BBG001SM4539', 'BBG001SM4691', 'BBG001SM46B8', 'BBG001SM48F0', 'BBG001SM48J6', 'BBG001SM4986', 'BBG001SM49Z6', 'BBG001SM4HD2', 'BBG001SM4KY2', 'BBG001SM4P71', 'BBG001SM4PC5', 'BBG001SM4WF7', 'BBG001SM4WZ5', 'BBG001SM4XN6', 'BBG001SM4XW6', 'BBG001SM4YD5', 'BBG001SM4YK7', 'BBG001SM4Z60', 'BBG001SM51M6', 'BBG001SM51P3', 'BBG001SM51S0', 'BBG001SM51V6', 'BBG001SM53B4', 'BBG001SM54J4', 'BBG001SM5949', 'BBG001SM5958', 'BBG001SM5F27', 'BBG001SM5FW4', 'BBG001SM5H05', 'BBG001SM5JR2', 'BBG001SM5KZ0', 'BBG001SM5LL3', 'BBG001SM5MM0', 'BBG001SM5QS5', 'BBG001SM5TJ9', 'BBG001SM5XQ2', 'BBG001SM62K5', 'BBG001SM68G7', 'BBG001SM69P5', 'BBG001SM6C41', 'BBG001SM6FD4', 'BBG001SM6HR5', 'BBG001SM6JZ2', 'BBG001SM6LG8', 'BBG001SM6MG6', 'BBG001SM6PD2', 'BBG001SM6V94', 'BBG001SM6W65', 'BBG001SM6Y34', 'BBG001SM6Z59', 'BBG001SM70S0', 'BBG001SM70Y3', 'BBG001SM71W3', 'BBG001SM73S4', 'BBG001SM7B15', 'BBG001SM7B33', 'BBG001SM7BG9', 'BBG001SM7BJ6', 'BBG001SM7BN1', 'BBG001SM7HC0', 'BBG001SM7N73', 'BBG001SM7Q67', 'BBG001SM7XM4', 'BBG001SM7Y33', 'BBG001SM7Y60', 'BBG001SM7ZD9', 'BBG001SM80T8', 'BBG001SM81V3', 'BBG001SM82S5', 'BBG001SM85R9', 'BBG001SM8660', 'BBG001SM8BV1', 'BBG001SM8CY6', 'BBG001SM8D74', 'BBG001SM8F97', 'BBG001SM8JP1', 'BBG001SM8L58', 'BBG001SM8L67', 'BBG001SM8M10', 'BBG001SM8QX6', 'BBG001SM8WV5', 'BBG001SM8XW2', 'BBG001SM96T4', 'BBG001SM9794', 'BBG001SM99S9', 'BBG001SM99T8', 'BBG001SM99X3', 'BBG001SM9D19', 'BBG001SM9FD1', 'BBG001SM9G12', 'BBG001SM9GN8', 'BBG001SM9GY6', 'BBG001SM9H01', 'BBG001SM9LS2', 'BBG001SM9MD6', 'BBG001SM9NZ0', 'BBG001SM9P03', 'BBG001SM9QV7', 'BBG001SM9R18', 'BBG001SM9RC6', 'BBG001SM9WT7', 'BBG001SMB2L8', 'BBG001SMB2N6', 'BBG001SMB871', 'BBG001SMBB47', 'BBG001SMBC18', 'BBG001SMBD43', 'BBG001SMBJG7', 'BBG001SMBKM7', 'BBG001SMBT39', 'BBG001SMBVP0', 'BBG001SMBXG6', 'BBG001SMBY29', 'BBG001SMBYX5', 'BBG001SMBZR9', 'BBG001SMC1M8', 'BBG001SMC2J0', 'BBG001SMC4F0', 'BBG001SMC5N8', 'BBG001SMC5T2', 'BBG001SMC629', 'BBG001SMC6B9', 'BBG001SMC6F5', 'BBG001SMC7H1', 'BBG001SMC7S9', 'BBG001SMC9T4', 'BBG001SMCBQ2', 'BBG001SMCFB9', 'BBG001SMCHF1', 'BBG001SMCKB8', 'BBG001SMCN40', 'BBG001SMCRV1', 'BBG001SMCS49', 'BBG001SMCTP4', 'BBG001SMCWX8', 'BBG001SMCZH9', 'BBG001SMD0N8', 'BBG001SMD2X3', 'BBG001SMD307', 'BBG001SMD3Q9', 'BBG001SMD3T6', 'BBG001SMD6B8', 'BBG001SMDCC4', 'BBG001SMDCR8', 'BBG001SMDG62', 'BBG001SMDGS8', 'BBG001SMDMK3', 'BBG001SMDNB1', 'BBG001SMDPC5', 'BBG001SMDR68', 'BBG001SMDR77', 'BBG001SMF298', 'BBG001SMF7C3', 'BBG001SMF920', 'BBG001SMFCR6', 'BBG001SMFKS7', 'BBG001SMG1Q0', 'BBG001SMG9M7', 'BBG001SMG9V7', 'BBG001SMGG41', 'BBG001SMGGP8', 'BBG001SMGVG5', 'BBG001SMGW19', 'BBG001SMGWC7', 'BBG001SMH152', 'BBG001SMH2H7', 'BBG001SMH2R6', 'BBG001SMH2T4', 'BBG001SMH2W0', 'BBG001SMH456', 'BBG001SMH465', 'BBG001SMH4B9', 'BBG001SMH6Y9', 'BBG001SMH768', 'BBG001SMH7G7', 'BBG001SMH7Z6', 'BBG001SMH8K0', 'BBG001SMHBM1', 'BBG001SMHG59', 'BBG001SMHJ53', 'BBG001SMHLQ5', 'BBG001SMHPW9', 'BBG001SMHR46', 'BBG001SMHRF4', 'BBG001SMHRH2', 'BBG001SMHTF0', 'BBG001SMHW27', 'BBG001SMHX52', 'BBG001SMHY41', 'BBG001SMHY69', 'BBG001SMHYB3', 'BBG001SMJ3J1', 'BBG001SMJ5C3', 'BBG001SMJ5Y9', 'BBG001SMJ7S2', 'BBG001SMJ882', 'BBG001SMJB76', 'BBG001SMJC56', 'BBG001SMJCK9', 'BBG001SMJGS2', 'BBG001SMJL38', 'BBG001SMJMY2', 'BBG001SMJQ46', 'BBG001SMK0R6', 'BBG001SMK5P7', 'BBG001SMK666', 'BBG001SMK8X2', 'BBG001SMK906', 'BBG001SMK988', 'BBG001SMKDB5', 'BBG001SMKHL5', 'BBG001SMKJT3', 'BBG001SMKND1', 'BBG001SMKRM2', 'BBG001SMKV61', 'BBG001SMKX67', 'BBG001SMKXT2', 'BBG001SMKXY6', 'BBG001SMKZC5', 'BBG001SMKZP1', 'BBG001SML1C9', 'BBG001SML8F1', 'BBG001SMLC62', 'BBG001SMLFP4', 'BBG001SMLGK7', 'BBG001SMLHL4', 'BBG001SMLMJ6', 'BBG001SMLN40', 'BBG001SMLN77', 'BBG001SMLP54', 'BBG001SMLP90', 'BBG001SMLPK7', 'BBG001SMLRT4', 'BBG001SMLTL8', 'BBG001SMLVH8', 'BBG001SMLVL3', 'BBG001SMLVY9', 'BBG001SMLW04', 'BBG001SMLW13', 'BBG001SMLW77', 'BBG001SMLZ16', 'BBG001SMLZ34', 'BBG001SMM101', 'BBG001SMM1V7', 'BBG001SMM2J9', 'BBG001SMM7Q0', 'BBG001SMM806', 'BBG001SMM9J4', 'BBG001SMMCW2', 'BBG001SMMG26', 'BBG001SMMNF7', 'BBG001SMMW12', 'BBG001SMN155', 'BBG001SMN2X2', 'BBG001SMN4R5', 'BBG001SMN5W6', 'BBG001SMNB80', 'BBG001SMNBC5', 'BBG001SMNHG8', 'BBG001SMNMK2', 'BBG001SMNR49', 'BBG001SMNT90', 'BBG001SMP1N3', 'BBG001SMP1Y1', 'BBG001SMP466', 'BBG001SMP5L6', 'BBG001SMP6C4', 'BBG001SMP6Y0', 'BBG001SMPCQ6', 'BBG001SMPD11', 'BBG001SMPFZ9', 'BBG001SMPG23', 'BBG001SMPRZ3', 'BBG001SMPTF1', 'BBG001SMPX26', 'BBG001SMPXD4', 'BBG001SMPYN1', 'BBG001SMQ474', 'BBG001SMQ866', 'BBG001SMQ928', 'BBG001SMQD47', 'BBG001SMQD74', 'BBG001SMQD83', 'BBG001SMQF15', 'BBG001SMQJ35', 'BBG001SMQJ99', 'BBG001SMQJK6', 'BBG001SMQKD1', 'BBG001SMQKM1', 'BBG001SMRQL8', 'BBG001SMRV00', 'BBG001SMS1X9', 'BBG001SMTJ14', 'BBG001SMV110', 'BBG001SMX993', 'BBG001SMXWD7', 'BBG001SMY0Y3', 'BBG001SMYFR8', 'BBG001SMYH12', 'BBG001SMYJN4', 'BBG001SMYJW4', 'BBG001SMYMM8', 'BBG001SMYMP5', 'BBG001SMYY42', 'BBG001SMYYY9', 'BBG001SMZ5B6', 'BBG001SMZ5M4', 'BBG001SMZ5T7', 'BBG001SMZ857', 'BBG001SMZ955', 'BBG001SMZ9S0', 'BBG001SMZKH7', 'BBG001SMZNG2', 'BBG001SMZNP2', 'BBG001SMZP95', 'BBG001SMZQC9', 'BBG001SMZQS2', 'BBG001SMZQW7', 'BBG001SMZRH2', 'BBG001SMZRV6', 'BBG001SMZRY3', 'BBG001SN0434', 'BBG001SN0B92', 'BBG001SN0P46', 'BBG001SN0PJ0', 'BBG001SN0PQ2', 'BBG001SN0R60', 'BBG001SN0V52', 'BBG001SN0VJ7', 'BBG001SN0VM3', 'BBG001SN0Y92', 'BBG001SN0YW6', 'BBG001SN10M0', 'BBG001SN10Q6', 'BBG001SN11Y5', 'BBG001SN12G3', 'BBG001SN15J3', 'BBG001SN16N6', 'BBG001SN1825', 'BBG001SN18L4', 'BBG001SN1R78', 'BBG001SN1W22', 'BBG001SN1XQ4', 'BBG001SN2227', 'BBG001SN25H4', 'BBG001SN2619', 'BBG001SN29W9', 'BBG001SN2B54', 'BBG001SN2DT4', 'BBG001SN2NH5', 'BBG001SN2VH7', 'BBG001SN2YN4', 'BBG001SN2Z33', 'BBG001SN3HS5', 'BBG001SN3M04', 'BBG001SN3PM3', 'BBG001SN3PQ9', 'BBG001SN3Q23', 'BBG001SN3QG8', 'BBG001SN3RQ5', 'BBG001SN3V68', 'BBG001SN3WM8', 'BBG001SN3Z50', 'BBG001SN3Z78', 'BBG001SN4127', 'BBG001SN47D2', 'BBG001SN4L78', 'BBG001SN4LD1', 'BBG001SN4QP7', 'BBG001SN4SJ0', 'BBG001SN4T80', 'BBG001SN4TH0', 'BBG001SN4VK1', 'BBG001SN4XL6', 'BBG001SN4ZG7', 'BBG001SN4ZP7', 'BBG001SN52J6', 'BBG001SN5484', 'BBG001SN5929', 'BBG001SN59W6', 'BBG001SN5F07', 'BBG001SN5FL4', 'BBG001SN5MK0', 'BBG001SN5PR6', 'BBG001SN5ST8', 'BBG001SN5VB0', 'BBG001SN5XW3', 'BBG001SN60V5', 'BBG001SN60Z1', 'BBG001SN63X7', 'BBG001SN65J8', 'BBG001SN66N1', 'BBG001SN68L9', 'BBG001SN6GM0', 'BBG001SN6GN9', 'BBG001SN6GP7', 'BBG001SN6GS4', 'BBG001SN6GV0', 'BBG001SN6L03', 'BBG001SN74B8', 'BBG001SN7614', 'BBG001SN7C84', 'BBG001SN7CQ4', 'BBG001SN7DS0', 'BBG001SN7G49', 'BBG001SN7HP4', 'BBG001SN7ZL8', 'BBG001SN82H5', 'BBG001SN83G4', 'BBG001SN86J4', 'BBG001SN8FN9', 'BBG001SN8JL3', 'BBG001SN8NT6', 'BBG001SN8SK4', 'BBG001SN8XT4', 'BBG001SN91H6', 'BBG001SN93X4', 'BBG001SN9426', 'BBG001SN96C0', 'BBG001SN96V9', 'BBG001SN9872', 'BBG001SN99J7', 'BBG001SN9B75', 'BBG001SN9B84', 'BBG001SN9B93', 'BBG001SN9FQ5', 'BBG001SN9HR0', 'BBG001SN9L37', 'BBG001SN9NG9', 'BBG001SN9P56', 'BBG001SN9RF1', 'BBG001SN9RH9', 'BBG001SN9TC0', 'BBG001SN9TR4', 'BBG001SN9X86', 'BBG001SN9XB2', 'BBG001SNBF28', 'BBG001SNBF37', 'BBG001SNBK54', 'BBG001SNBK90', 'BBG001SNBKD5', 'BBG001SNBMJ5', 'BBG001SNBQ51', 'BBG001SNBR77', 'BBG001SNBTR1', 'BBG001SNC510', 'BBG001SNC8F9', 'BBG001SNC9N8', 'BBG001SNC9P6', 'BBG001SNCH50', 'BBG001SNCMR5', 'BBG001SNCNX6', 'BBG001SNCTZ1', 'BBG001SNCWV8', 'BBG001SNCYR9', 'BBG001SNCZG8', 'BBG001SNDBS7', 'BBG001SNDBX1', 'BBG001SNDHV0', 'BBG001SNDN29', 'BBG001SNDNZ3', 'BBG001SNDSG3', 'BBG001SNF0N4', 'BBG001SNF1V3', 'BBG001SNF3S3', 'BBG001SNF4L8', 'BBG001SNF6C3', 'BBG001SNFD38', 'BBG001SNFDG4', 'BBG001SNFHX6', 'BBG001SNFNL6', 'BBG001SNFP59', 'BBG001SNFRZ2', 'BBG001SNFXZ9', 'BBG001SNG0Y1', 'BBG001SNG6W0', 'BBG001SNG8P4', 'BBG001SNG9H1', 'BBG001SNGJ34', 'BBG001SNGPG6', 'BBG001SNGXJ6', 'BBG001SNGXL3', 'BBG001SNGY95', 'BBG001SNH2V9', 'BBG001SNH2X7', 'BBG001SNH4H1', 'BBG001SNH4N4', 'BBG001SNH5S6', 'BBG001SNH622', 'BBG001SNH6H6', 'BBG001SNH8Q2', 'BBG001SNH9L5', 'BBG001SNHDP2', 'BBG001SNHQJ0', 'BBG001SNHQP3', 'BBG001SNHR44', 'BBG001SNHR62', 'BBG001SNHVQ1', 'BBG001SNHXM1', 'BBG001SNHYZ5', 'BBG001SNJ3B7', 'BBG001SNJGK8', 'BBG001SNJTT1', 'BBG001SNJV52', 'BBG001SNJYP4', 'BBG001SNK4Z7', 'BBG001SNK6N5', 'BBG001SNK879', 'BBG001SNKC25', 'BBG001SNKCM3', 'BBG001SNKGQ0', 'BBG001SNKPZ0', 'BBG001SNKQ42', 'BBG001SNKXY4', 'BBG001SNL235', 'BBG001SNL4S4', 'BBG001SNL805', 'BBG001SNLCD2', 'BBG001SNLGR8', 'BBG001SNLHY8', 'BBG001SNLHZ7', 'BBG001SNLYV4', 'BBG001SNLZ23', 'BBG001SNM0H3', 'BBG001SNM387', 'BBG001SNM403', 'BBG001SNM5F4', 'BBG001SNM7S6', 'BBG001SNM8Q6', 'BBG001SNMN10', 'BBG001SNN4P5', 'BBG001SNN4Q4', 'BBG001SNN8R4', 'BBG001SNNBX0', 'BBG001SNNC86', 'BBG001SNNJN4', 'BBG001SNNVK0', 'BBG001SNNW37', 'BBG001SNNWL7', 'BBG001SNNWM6', 'BBG001SNNY88', 'BBG001SNP2B2', 'BBG001SNP936', 'BBG001SNPL84', 'BBG001SNPMH2', 'BBG001SNPP85', 'BBG001SNPRZ1', 'BBG001SNPT69', 'BBG001SNPVH2', 'BBG001SNPWX2', 'BBG001SNPY22', 'BBG001SNPYP7', 'BBG001SNQ2X7', 'BBG001SNQ2Z5', 'BBG001SNQ668', 'BBG001SNQ748', 'BBG001SNQ962', 'BBG001SNQ971', 'BBG001SNQFB2', 'BBG001SNQLG4', 'BBG001SNQLH3', 'BBG001SNQLM7', 'BBG001SNQXB3', 'BBG001SNQZ91', 'BBG001SNR159', 'BBG001SNR2K0', 'BBG001SNR391', 'BBG001SNR658', 'BBG001SNRN60', 'BBG001SNRRM3', 'BBG001SNRTC0', 'BBG001SNRVS8', 'BBG001SNRWN1', 'BBG001SNS069', 'BBG001SNS2B9', 'BBG001SNS461', 'BBG001SNS4R8', 'BBG001SNSCX3', 'BBG001SNSDQ9', 'BBG001SNSDZ9', 'BBG001SNSHN3', 'BBG001SNSLF3', 'BBG001SNSM07', 'BBG001SNSM70', 'BBG001SNSN87', 'BBG001SNST39', 'BBG001SNSYR2', 'BBG001SNSYW6', 'BBG001SNT0C1', 'BBG001SNT1R3', 'BBG001SNT4Q8', 'BBG001SNT6T0', 'BBG001SNT745', 'BBG001SNT7H1', 'BBG001SNT8T6', 'BBG001SNTDT5', 'BBG001SNTFW6', 'BBG001SNTNM0', 'BBG001SNTP63', 'BBG001SNTT01', 'BBG001SNTVT5', 'BBG001SNTWR5', 'BBG001SNTXY5', 'BBG001SNV0J1', 'BBG001SNV216', 'BBG001SNV3J5', 'BBG001SNV706', 'BBG001SNVCQ7', 'BBG001SNVCS5', 'BBG001SNVJN5', 'BBG001SNVQQ6', 'BBG001SNVT35', 'BBG001SNVWC8', 'BBG001SNVWN6', 'BBG001SNVWR2', 'BBG001SNVXC6', 'BBG001SNVXD5', 'BBG001SNVXP2', 'BBG001SNVYD3', 'BBG001SNW4H4', 'BBG001SNW8G6', 'BBG001SNW8K1', 'BBG001SNW8R4', 'BBG001SNWCK2', 'BBG001SNWCT3', 'BBG001SNWHL0', 'BBG001SNWNL7', 'BBG001SNWPR6', 'BBG001SNWRF5', 'BBG001SNWTM3', 'BBG001SNWVJ2', 'BBG001SNWVK0', 'BBG001SNWVT1', 'BBG001SNX0W4', 'BBG001SNX1Q9', 'BBG001SNX2X9', 'BBG001SNX2Y8', 'BBG001SNX349', 'BBG001SNX4Y4', 'BBG001SNXGW9', 'BBG001SNXN36', 'BBG001SNXQB0', 'BBG001SNXSQ0', 'BBG001SNXTW1', 'BBG001SNY6B3', 'BBG001SNY6R6', 'BBG001SNY7W8', 'BBG001SNYBH6', 'BBG001SNYFR6', 'BBG001SNYFT4', 'BBG001SNYRH1', 'BBG001SNYRK7', 'BBG001SNYTV1', 'BBG001SNZ1Q7', 'BBG001SNZ604', 'BBG001SNZ6P7', 'BBG001SNZ6Q6', 'BBG001SNZ926', 'BBG001SNZ9F2', 'BBG001SNZLM7', 'BBG001SNZPH4', 'BBG001SNZPY5', 'BBG001SNZTD0', 'BBG001SNZTS4', 'BBG001SNZWN2', 'BBG001SNZXJ5', 'BBG001SNZZK8', 'BBG001SP0625', 'BBG001SP07T4', 'BBG001SP0MG5', 'BBG001SP0QC0', 'BBG001SP0RF5', 'BBG001SP0S90', 'BBG001SP0YC3', 'BBG001SP0YQ8', 'BBG001SP11V3', 'BBG001SP14B9', 'BBG001SP19R1', 'BBG001SP1B69', 'BBG001SP1D10', 'BBG001SP1G40', 'BBG001SP1KL2', 'BBG001SP1LB1', 'BBG001SP1N63', 'BBG001SP1V65', 'BBG001SP1W09', 'BBG001SP1Y87', 'BBG001SP2482', 'BBG001SP24T9', 'BBG001SP29R0', 'BBG001SP2C20', 'BBG001SP2FG8', 'BBG001SP2H10', 'BBG001SP2HP4', 'BBG001SP2LL9', 'BBG001SP2M37', 'BBG001SP3016', 'BBG001SP3383', 'BBG001SP33S1', 'BBG001SP3K94', 'BBG001SP3KZ5', 'BBG001SP3MC6', 'BBG001SP3MD5', 'BBG001SP3NJ7', 'BBG001SP3QC7', 'BBG001SP3XJ5', 'BBG001SP4872', 'BBG001SP49Z9', 'BBG001SP4CD6', 'BBG001SP5023', 'BBG001SP50B3', 'BBG001SP57F4', 'BBG001SP58W3', 'BBG001SP5988', 'BBG001SP59T5', 'BBG001SP5B56', 'BBG001SP5H99', 'BBG001SP5JM0', 'BBG001SP5LC6', 'BBG001SP5R79', 'BBG001SP5TS2', 'BBG001SP5YH3', 'BBG001SP62C7', 'BBG001SP62F4', 'BBG001SP62Q2', 'BBG001SP66L8', 'BBG001SP68G0', 'BBG001SP6D06', 'BBG001SP6HB5', 'BBG001SP6NT3', 'BBG001SP6P27', 'BBG001SP6TY4', 'BBG001SP6Y91', 'BBG001SP6ZL4', 'BBG001SP76W5', 'BBG001SP77K6', 'BBG001SP7L61', 'BBG001SP7PB6', 'BBG001SP7PX2', 'BBG001SP7T00', 'BBG001SP7X10', 'BBG001SP7Z06', 'BBG001SP8208', 'BBG001SP8DR5', 'BBG001SP8DT3', 'BBG001SP8G34', 'BBG001SP8GP0', 'BBG001SP8JG4', 'BBG001SP8JP4', 'BBG001SP8JX5', 'BBG001SP8K26', 'BBG001SP8KW3', 'BBG001SP8M22', 'BBG001SP8MR5', 'BBG001SP8QM1', 'BBG001SP8RB1', 'BBG001SP8RR4', 'BBG001SP8RS3', 'BBG001SP8SS1', 'BBG001SP8TP2', 'BBG001SP8TQ1', 'BBG001SP8VG7', 'BBG001SP8VM0', 'BBG001SP8YD4', 'BBG001SP90J1', 'BBG001SP9591', 'BBG001SP95N5', 'BBG001SP98H6', 'BBG001SP9M85', 'BBG001SP9P33', 'BBG001SP9RS2', 'BBG001SP9TZ0', 'BBG001SP9WY4', 'BBG001SP9XM5', 'BBG001SP9XP2', 'BBG001SP9Z77', 'BBG001SPB1K4', 'BBG001SPBB86', 'BBG001SPBFS5', 'BBG001SPBG12', 'BBG001SPBRV5', 'BBG001SPBTG8', 'BBG001SPBTK3', 'BBG001SPBXV2', 'BBG001SPC3D7', 'BBG001SPC454', 'BBG001SPC6K2', 'BBG001SPCJ97', 'BBG001SPCJQ8', 'BBG001SPCRF2', 'BBG001SPCRL5', 'BBG001SPCSK4', 'BBG001SPCXW0', 'BBG001SPCY12', 'BBG001SPCYT2', 'BBG001SPD373', 'BBG001SPD3P3', 'BBG001SPD426', 'BBG001SPD4H0', 'BBG001SPDBC9', 'BBG001SPDGN6', 'BBG001SPDJ96', 'BBG001SPDND2', 'BBG001SPDRD3', 'BBG001SPDS41', 'BBG001SPDSF9', 'BBG001SPF068', 'BBG001SPF308', 'BBG001SPF5T2', 'BBG001SPF610', 'BBG001SPFKZ2', 'BBG001SPFMQ8', 'BBG001SPFRQ7', 'BBG001SPFX75', 'BBG001SPGCM3', 'BBG001SPGHF0', 'BBG001SPGV78', 'BBG001SPGVC2', 'BBG001SPH4P7', 'BBG001SPHCS6', 'BBG001SPHD86', 'BBG001SPHFR0', 'BBG001SPHFX3', 'BBG001SPHGX1', 'BBG001SPHKZ0', 'BBG001SPHL51', 'BBG001SPHL97', 'BBG001SPHLD2', 'BBG001SPHQJ5', 'BBG001SPHV22', 'BBG001SPHYW3', 'BBG001SPJ171', 'BBG001SPJ625', 'BBG001SPJ643', 'BBG001SPJ652', 'BBG001SPJ6C4', 'BBG001SPJH21', 'BBG001SPJN28', 'BBG001SPJN46', 'BBG001SPJPS5', 'BBG001SPJSD5', 'BBG001SPK017', 'BBG001SPK277', 'BBG001SPK286', 'BBG001SPK2B2', 'BBG001SPK6V1', 'BBG001SPKBJ4', 'BBG001SPKCM8', 'BBG001SPKCS2', 'BBG001SPKCV8', 'BBG001SPKDN5', 'BBG001SPKDQ2', 'BBG001SPKFB3', 'BBG001SPKHF5', 'BBG001SPKJS7', 'BBG001SPKMB8', 'BBG001SPKNS8', 'BBG001SPKP12', 'BBG001SPKRV5', 'BBG001SPKV19', 'BBG001SPKXV2', 'BBG001SPKYS4', 'BBG001SPKZC8', 'BBG001SPL025', 'BBG001SPL123', 'BBG001SPL5W1', 'BBG001SPLDY2', 'BBG001SPLNF1', 'BBG001SPLNK5', 'BBG001SPLWB5', 'BBG001SPM284', 'BBG001SPMFV9', 'BBG001SPMFY6', 'BBG001SPMJ05', 'BBG001SPMJ78', 'BBG001SPMJN0', 'BBG001SPMLR1', 'BBG001SPMQJ9', 'BBG001SPMVV4', 'BBG001SPMVX2', 'BBG001SPMW06', 'BBG001SPMXS4', 'BBG001SPMY48', 'BBG001SPN4X1', 'BBG001SPN997', 'BBG001SPNJT3', 'BBG001SPNK29', 'BBG001SPNL09', 'BBG001SPP343', 'BBG001SPP3S7', 'BBG001SPP986', 'BBG001SPPGX2', 'BBG001SPPL34', 'BBG001SPPQG9', 'BBG001SPPRR5', 'BBG001SPPWP6', 'BBG001SPQ770', 'BBG001SPQ798', 'BBG001SPQ823', 'BBG001SPQ8T4', 'BBG001SPQC15', 'BBG001SPQCB4', 'BBG001SPQCY9', 'BBG001SPQDD0', 'BBG001SPQFP2', 'BBG001SPQH05', 'BBG001SPQJW6', 'BBG001SPQLW1', 'BBG001SPQMZ6', 'BBG001SPQN39', 'BBG001SPQNH4', 'BBG001SPQVJ4', 'BBG001SPQXT9', 'BBG001SPR742', 'BBG001SPR760', 'BBG001SPR7B4', 'BBG001SPR886', 'BBG001SPR8H6', 'BBG001SPRC96', 'BBG001SPRG60', 'BBG001SPRJ28', 'BBG001SPRJ55', 'BBG001SPRNB9', 'BBG001SPRR39', 'BBG001SPRSJ0', 'BBG001SPRY52', 'BBG001SPS661', 'BBG001SPS723', 'BBG001SPSDJ2', 'BBG001SPSH76', 'BBG001SPSHC0', 'BBG001SPSJB7', 'BBG001SPSNV6', 'BBG001SPT287', 'BBG001SPT2Q7', 'BBG001SPT508', 'BBG001SPT5Z0', 'BBG001SPT6F0', 'BBG001SPT6G9', 'BBG001SPT759', 'BBG001SPT8K0', 'BBG001SPT8P5', 'BBG001SPT9T9', 'BBG001SPTB32', 'BBG001SPTB41', 'BBG001SPTB50', 'BBG001SPTB69', 'BBG001SPTB87', 'BBG001SPTBM1', 'BBG001SPTBN0', 'BBG001SPTBP8', 'BBG001SPTGD0', 'BBG001SPTQ39', 'BBG001SPTRF4', 'BBG001SPTX70', 'BBG001SPV1C1', 'BBG001SPV5M1', 'BBG001SPV854', 'BBG001SPVCQ2', 'BBG001SPVGM7', 'BBG001SPVKJ2', 'BBG001SPVL19', 'BBG001SPVM17', 'BBG001SPVN33', 'BBG001SPVXV0', 'BBG001SPW1R4', 'BBG001SPW5Q6', 'BBG001SPW700', 'BBG001SPWJZ6', 'BBG001SPWLV5', 'BBG001SPX5N8', 'BBG001SPX610', 'BBG001SPX870', 'BBG001SPX9K3', 'BBG001SPX9P8', 'BBG001SPXHX1', 'BBG001SPXL53', 'BBG001SPXLT7', 'BBG001SPXTJ1', 'BBG001SPXWN9', 'BBG001SPY030', 'BBG001SPY0W8', 'BBG001SPY0Z5', 'BBG001SPY4M1', 'BBG001SPY7K6', 'BBG001SPYB54', 'BBG001SPYFN5', 'BBG001SPYLH9', 'BBG001SPYN49', 'BBG001SPYN94', 'BBG001SPYP17', 'BBG001SPYSQ4', 'BBG001SPYVT4', 'BBG001SPYW58', 'BBG001SPZ5L8', 'BBG001SPZ9G6', 'BBG001SPZC33', 'BBG001SPZDL1', 'BBG001SQ0160', 'BBG001SQ0286', 'BBG001SQ04D6', 'BBG001SQ06K3', 'BBG001SQ08P4', 'BBG001SQ0C39', 'BBG001SQ0C48', 'BBG001SQ0DJ0', 'BBG001SQ0DL7', 'BBG001SQ0DN5', 'BBG001SQ0HM7', 'BBG001SQ0KL1', 'BBG001SQ0PM9', 'BBG001SQ0RS9', 'BBG001SQ1025', 'BBG001SQ11N0', 'BBG001SQ1882', 'BBG001SQ1RH0', 'BBG001SQ1Z91', 'BBG001SQ2300', 'BBG001SQ23K8', 'BBG001SQ25S5', 'BBG001SQ25W0', 'BBG001SQ2738', 'BBG001SQ2827', 'BBG001SQ2BX6', 'BBG001SQ2FS3', 'BBG001SQ2JP8', 'BBG001SQ2QL6', 'BBG001SQ2ZD5', 'BBG001SQ3GT9', 'BBG001SQ3K65', 'BBG001SQ3MC4', 'BBG001SQ3N96', 'BBG001SQ3NR6', 'BBG001SQ3Q08', 'BBG001SQ3QC5', 'BBG001SQ4923', 'BBG001SQ4996', 'BBG001SQ4DF0', 'BBG001SQ4DL3', 'BBG001SQ4DP9', 'BBG001SQ4ML3', 'BBG001SQ4NL1', 'BBG001SQ4P90', 'BBG001SQ4TD7', 'BBG001SQ4VN1', 'BBG001SQ4WV0', 'BBG001SQ4Y73', 'BBG001SQ4YC7', 'BBG001SQ50P6', 'BBG001SQ51M7', 'BBG001SQ52R0', 'BBG001SQ52W4', 'BBG001SQ52Y2', 'BBG001SQ5389', 'BBG001SQ5FD6', 'BBG001SQ5J84', 'BBG001SQ5M87', 'BBG001SQ5RM0', 'BBG001SQ6164', 'BBG001SQ62Q0', 'BBG001SQ6397', 'BBG001SQ67K5', 'BBG001SQ6887', 'BBG001SQ6F90', 'BBG001SQ6H05', 'BBG001SQ6V04', 'BBG001SQ7252', 'BBG001SQ77L3', 'BBG001SQ7H31', 'BBG001SQ7PD2', 'BBG001SQ83B2', 'BBG001SQ8714', 'BBG001SQ87R6', 'BBG001SQ87W0', 'BBG001SQ8CN9', 'BBG001SQ8K88', 'BBG001SQ8KV2', 'BBG001SQ8NB8', 'BBG001SQ8NH2', 'BBG001SQ8NS0', 'BBG001SQ8PW0', 'BBG001SQ8WP3', 'BBG001SQ8Y15', 'BBG001SQ9848', 'BBG001SQ9RJ0', 'BBG001SQ9SW3', 'BBG001SQ9WK7', 'BBG001SQ9XB5', 'BBG001SQB1N9', 'BBG001SQB382', 'BBG001SQB4C5', 'BBG001SQB9W2', 'BBG001SQBBX6', 'BBG001SQBFQ5', 'BBG001SQBGT0', 'BBG001SQBJT4', 'BBG001SQBJZ7', 'BBG001SQBK48', 'BBG001SQBK66', 'BBG001SQBKL9', 'BBG001SQBSD1', 'BBG001SQBSY8', 'BBG001SQBW15', 'BBG001SQBWB4', 'BBG001SQBZJ9', 'BBG001SQC1F7', 'BBG001SQC229', 'BBG001SQC2F5', 'BBG001SQC5P7', 'BBG001SQC5W9', 'BBG001SQC997', 'BBG001SQC9P9', 'BBG001SQCKV7', 'BBG001SQCLD5', 'BBG001SQCQC5', 'BBG001SQCRP9', 'BBG001SQD3M4', 'BBG001SQD5D9', 'BBG001SQD861', 'BBG001SQDFG4', 'BBG001SQDFT0', 'BBG001SQDK37', 'BBG001SQDKD6', 'BBG001SQDKG3', 'BBG001SQDKH2', 'BBG001SQDKJ0', 'BBG001SQF2F2', 'BBG001SQF805', 'BBG001SQF8Q7', 'BBG001SQFKK6', 'BBG001SQFP89', 'BBG001SQFQF9', 'BBG001SQFX64', 'BBG001SQG555', 'BBG001SQG9X6', 'BBG001SQGH95', 'BBG001SQGHS4', 'BBG001SQGTH0', 'BBG001SQGXG2', 'BBG001SQH8Q5', 'BBG001SQHLP7', 'BBG001SQHLR5', 'BBG001SQHMK0', 'BBG001SQHRC8', 'BBG001SQHV11', 'BBG001SQHVZ4', 'BBG001SQHWC7', 'BBG001SQHWP3', 'BBG001SQJ8T0', 'BBG001SQJ8X5', 'BBG001SQJ972', 'BBG001SQJHL8', 'BBG001SQJJ07', 'BBG001SQJKT3', 'BBG001SQJMZ2', 'BBG001SQJYV0', 'BBG001SQJZC8', 'BBG001SQK1G7', 'BBG001SQK2Y5', 'BBG001SQK3L7', 'BBG001SQK8Y2', 'BBG001SQK9P0', 'BBG001SQKG74', 'BBG001SQKGD7', 'BBG001SQKL19', 'BBG001SQKSL2', 'BBG001SQL1B1', 'BBG001SQLB47', 'BBG001SQLDM3', 'BBG001SQLFT1', 'BBG001SQLKK9', 'BBG001SQLKR2', 'BBG001SQLM25', 'BBG001SQLN69', 'BBG001SQLQT7', 'BBG001SQLV52', 'BBG001SQM5W8', 'BBG001SQM665', 'BBG001SQMG63', 'BBG001SQN1L8', 'BBG001SQN842', 'BBG001SQNDV1', 'BBG001SQNK54', 'BBG001SQNKR0', 'BBG001SQNQS6', 'BBG001SQNS57', 'BBG001SQNS66', 'BBG001SQNYQ1', 'BBG001SQP181', 'BBG001SQP3P8', 'BBG001SQP3W0', 'BBG001SQP5Z2', 'BBG001SQPL14', 'BBG001SQPNN6', 'BBG001SQPNV7', 'BBG001SQPR02', 'BBG001SQPSM6', 'BBG001SQPT71', 'BBG001SQQ4H4', 'BBG001SQQ5B7', 'BBG001SQQ5P2', 'BBG001SQQ6G0', 'BBG001SQQ8C0', 'BBG001SQQG69', 'BBG001SQQKX0', 'BBG001SQQVK0', 'BBG001SQQX44', 'BBG001SQR2X9', 'BBG001SQR6M2', 'BBG001SQR9C7', 'BBG001SQRCN8', 'BBG001SQRCY6', 'BBG001SQRDT0', 'BBG001SQRG86', 'BBG001SQRGC1', 'BBG001SQRHJ2', 'BBG001SQRHW7', 'BBG001SQRJS8', 'BBG001SQRMX5', 'BBG001SQRQ20', 'BBG001SQRWL6', 'BBG001SQS0X2', 'BBG001SQS6Z7', 'BBG001SQS7X7', 'BBG001SQS801', 'BBG001SQS9Y2', 'BBG001SQSBP7', 'BBG001SQSBX8', 'BBG001SQSCJ2', 'BBG001SQSDG3', 'BBG001SQSJX1', 'BBG001SQSKT3', 'BBG001SQSPF7', 'BBG001SQSRN4', 'BBG001SQSS43', 'BBG001SQST50', 'BBG001SQT2W8', 'BBG001SQTB21', 'BBG001SQTC10', 'BBG001SQTCC8', 'BBG001SQTF59', 'BBG001SQTTF8', 'BBG001SQTTP7', 'BBG001SQTYH5', 'BBG001SQTYK1', 'BBG001SQV3Z0', 'BBG001SQV7W4', 'BBG001SQVB91', 'BBG001SQVV42', 'BBG001SQW012', 'BBG001SQW4R6', 'BBG001SQWCZ9', 'BBG001SQWNQ5', 'BBG001SQWQC3', 'BBG001SQWTN5', 'BBG001SQWTR1', 'BBG001SQWVL2', 'BBG001SQWXQ3', 'BBG001SQX1T9', 'BBG001SQX4T3', 'BBG001SQX538', 'BBG001SQX565', 'BBG001SQX7D3', 'BBG001SQXCH8', 'BBG001SQXJL8', 'BBG001SQY3Y8', 'BBG001SQY4B1', 'BBG001SQY546', 'BBG001SQY555', 'BBG001SQYBG0', 'BBG001SQYBY0', 'BBG001SQYC14', 'BBG001SQYCG8', 'BBG001SQYNT0', 'BBG001SQYZC1', 'BBG001SQYZD0', 'BBG001SQZ7Z7', 'BBG001SQZ858', 'BBG001SQZ9W6', 'BBG001SQZCK2', 'BBG001SQZT52', 'BBG001SQZT98', 'BBG001SQZTC4', 'BBG001SQZTM3', 'BBG001SQZTR8', 'BBG001SQZW28', 'BBG001SR03L7', 'BBG001SR06G6', 'BBG001SR0CP3', 'BBG001SR0FM9', 'BBG001SR0GL8', 'BBG001SR0KH4', 'BBG001SR0KP5', 'BBG001SR0LL7', 'BBG001SR0LR1', 'BBG001SR0LS0', 'BBG001SR0MP1', 'BBG001SR0RT6', 'BBG001SR0WM2', 'BBG001SR0WP9', 'BBG001SR0YK0', 'BBG001SR1425', 'BBG001SR14H9', 'BBG001SR18T7', 'BBG001SR1G28', 'BBG001SR1J95', 'BBG001SR1VQ9', 'BBG001SR1Y29', 'BBG001SR22H2', 'BBG001SR25C0', 'BBG001SR27H1', 'BBG001SR2807', 'BBG001SR2J85', 'BBG001SR2JB1', 'BBG001SR2K91', 'BBG001SR2L71', 'BBG001SR2MN1', 'BBG001SR2MR7', 'BBG001SR2T01', 'BBG001SR2T29', 'BBG001SR2XP5', 'BBG001SR2Y28', 'BBG001SR33H9', 'BBG001SR34T4', 'BBG001SR3879', 'BBG001SR3BN4', 'BBG001SR3GT7', 'BBG001SR41D6', 'BBG001SR41Q2', 'BBG001SR4253', 'BBG001SR42Z0', 'BBG001SR4547', 'BBG001SR46J9', 'BBG001SR4F72', 'BBG001SR4GS7', 'BBG001SR4H14', 'BBG001SR4J92', 'BBG001SR4QH7', 'BBG001SR4TD5', 'BBG001SR4XN5', 'BBG001SR4XP3', 'BBG001SR4Z50', 'BBG001SR5092', 'BBG001SR5207', 'BBG001SR52L4', 'BBG001SR55Q2', 'BBG001SR57C3', 'BBG001SR5C50', 'BBG001SR5DY6', 'BBG001SR5KB5', 'BBG001SR5KK5', 'BBG001SR5QB2', 'BBG001SR5TY1', 'BBG001SR5X72', 'BBG001SR5XD5', 'BBG001SR5XF3', 'BBG001SR60L7', 'BBG001SR64K0', 'BBG001SR65P2', 'BBG001SR6D11', 'BBG001SR6GZ7', 'BBG001SR6H03', 'BBG001SR6H49', 'BBG001SR6H58', 'BBG001SR6H76', 'BBG001SR6H85', 'BBG001SR6H94', 'BBG001SR6HB1', 'BBG001SR6ZW8', 'BBG001SR70Y2', 'BBG001SR7508', 'BBG001SR77X8', 'BBG001SR7DC8', 'BBG001SR7DW6', 'BBG001SR7MB9', 'BBG001SR7NT8', 'BBG001SR80P1', 'BBG001SR82B2', 'BBG001SR8G12', 'BBG001SR8G21', 'BBG001SR8G30', 'BBG001SR8G49', 'BBG001SR8SJ7', 'BBG001SR8ZJ1', 'BBG001SR9230', 'BBG001SR92C0', 'BBG001SR97P5', 'BBG001SR9G39', 'BBG001SR9GR3', 'BBG001SR9JV2', 'BBG001SR9KR4', 'BBG001SR9LP4', 'BBG001SR9LY4', 'BBG001SR9N16', 'BBG001SR9QP3', 'BBG001SR9SC3', 'BBG001SR9SW1', 'BBG001SR9W89', 'BBG001SR9WR8', 'BBG001SRB4Z8', 'BBG001SRB5K1', 'BBG001SRB683', 'BBG001SRB9M1', 'BBG001SRBKC7', 'BBG001SRBKD6', 'BBG001SRBM33', 'BBG001SRBQZ9', 'BBG001SRBRP8', 'BBG001SRBRZ7', 'BBG001SRBTG4', 'BBG001SRBYW5', 'BBG001SRC0W8', 'BBG001SRC3F1', 'BBG001SRC8Y9', 'BBG001SRCB54', 'BBG001SRCFY3', 'BBG001SRCL43', 'BBG001SRCM50', 'BBG001SRCM78', 'BBG001SRCRZ6', 'BBG001SRCTT9', 'BBG001SRCV05', 'BBG001SRCVP8', 'BBG001SRCW21', 'BBG001SRCW94', 'BBG001SRCY63', 'BBG001SRD2X2', 'BBG001SRD8F9', 'BBG001SRD9B1', 'BBG001SRD9N8', 'BBG001SRDBM4', 'BBG001SRDBV4', 'BBG001SRDDF8', 'BBG001SRDDT3', 'BBG001SRDDY7', 'BBG001SRDF45', 'BBG001SRDF54', 'BBG001SRDT90', 'BBG001SRDTJ9', 'BBG001SRDTL6', 'BBG001SRDV40', 'BBG001SRDV59', 'BBG001SRDY53', 'BBG001SRF0K8', 'BBG001SRF3P7', 'BBG001SRF439', 'BBG001SRF803', 'BBG001SRF9K9', 'BBG001SRFK88', 'BBG001SRFL86', 'BBG001SRFLF8', 'BBG001SRFP14', 'BBG001SRFQX7', 'BBG001SRFYR7', 'BBG001SRG4B9', 'BBG001SRG5G1', 'BBG001SRG6K4', 'BBG001SRG6N1', 'BBG001SRG9W5', 'BBG001SRH3P5', 'BBG001SRHH47', 'BBG001SRHKF8', 'BBG001SRHKR5', 'BBG001SRHRS9', 'BBG001SRHS16', 'BBG001SRHTP8', 'BBG001SRJ0B4', 'BBG001SRJPJ1', 'BBG001SRJQL6', 'BBG001SRJR07', 'BBG001SRJWN1', 'BBG001SRJYJ2', 'BBG001SRJYP5', 'BBG001SRK4T5', 'BBG001SRK4W1', 'BBG001SRK7N4', 'BBG001SRK950', 'BBG001SRKB82', 'BBG001SRKCK6', 'BBG001SRKCM4', 'BBG001SRKDH8', 'BBG001SRKFW6', 'BBG001SRKGQ1', 'BBG001SRKHW2', 'BBG001SRKK73', 'BBG001SRKMN1', 'BBG001SRKV15', 'BBG001SRL6N5', 'BBG001SRL8Z8', 'BBG001SRLFL7', 'BBG001SRM6N4', 'BBG001SRM8D1', 'BBG001SRML88', 'BBG001SRMLH8', 'BBG001SRMY71', 'BBG001SRMY80', 'BBG001SRN591', 'BBG001SRN626', 'BBG001SRN7S6', 'BBG001SRN7W1', 'BBG001SRNBD3', 'BBG001SRNBK5', 'BBG001SRNBL4', 'BBG001SRNL96', 'BBG001SRNLF9', 'BBG001SRNLG8', 'BBG001SRNLL2', 'BBG001SRNQB2', 'BBG001SRNT99', 'BBG001SRNTH0', 'BBG001SRNTJ8', 'BBG001SRNVY6', 'BBG001SRNW74', 'BBG001SRNYN2', 'BBG001SRP223', 'BBG001SRPRX4', 'BBG001SRPT24', 'BBG001SRQ2D0', 'BBG001SRQ3S2', 'BBG001SRQ455', 'BBG001SRQ5C4', 'BBG001SRQ5K5', 'BBG001SRQ6K3', 'BBG001SRQSJ7', 'BBG001SRQVB8', 'BBG001SRQYB2', 'BBG001SRR9R9', 'BBG001SRRFC1', 'BBG001SRRFG7', 'BBG001SRRWL4', 'BBG001SRRWR8', 'BBG001SRRWS7', 'BBG001SRRXT4', 'BBG001SRS2P5', 'BBG001SRS4S8', 'BBG001SRS4V4', 'BBG001SRS550', 'BBG001SRS9Y0', 'BBG001SRSDF2', 'BBG001SRSL00', 'BBG001SRSPD7', 'BBG001SRSPW6', 'BBG001SRSTL0', 'BBG001SRTF75', 'BBG001SRTF84', 'BBG001SRTHL5', 'BBG001SRTK92', 'BBG001SRTND1', 'BBG001SRTS59', 'BBG001SRTVC4', 'BBG001SRTXL0', 'BBG001SRV422', 'BBG001SRV8J5', 'BBG001SRVGT6', 'BBG001SRVHT4', 'BBG001SRVP25', 'BBG001SRVQP8', 'BBG001SRVR58', 'BBG001SRVSL8', 'BBG001SRVWT1', 'BBG001SRVWZ4', 'BBG001SRVX19', 'BBG001SRVYB6', 'BBG001SRW2G0', 'BBG001SRW680', 'BBG001SRW699', 'BBG001SRW9J2', 'BBG001SRWCJ5', 'BBG001SRWF17', 'BBG001SRWGK4', 'BBG001SRWH77', 'BBG001SRWJ82', 'BBG001SRWJJ0', 'BBG001SRWNF5', 'BBG001SRWZJ4', 'BBG001SRX260', 'BBG001SRX4Z4', 'BBG001SRX527', 'BBG001SRXJ09', 'BBG001SRXPY8', 'BBG001SRXR92', 'BBG001SRXRB9', 'BBG001SRXRC8', 'BBG001SRXRD7', 'BBG001SRXRJ1', 'BBG001SRXRK9', 'BBG001SRXRL8', 'BBG001SRXRM7', 'BBG001SRXRN6', 'BBG001SRXTT6', 'BBG001SRXTW2', 'BBG001SRY1B5', 'BBG001SRY1V3', 'BBG001SRY722', 'BBG001SRY740', 'BBG001SRYH02', 'BBG001SRYH48', 'BBG001SRYJW3', 'BBG001SRZ669', 'BBG001SRZ8J1', 'BBG001SRZBZ6', 'BBG001SRZFZ7', 'BBG001SRZG76', 'BBG001SRZGP6', 'BBG001SRZGQ5', 'BBG001SRZGY6', 'BBG001SRZH47', 'BBG001SRZH92', 'BBG001SRZHC8', 'BBG001SRZHH3', 'BBG001SRZHJ1', 'BBG001SRZL39', 'BBG001SRZTG8', 'BBG001SRZX06', 'BBG001SRZXJ6', 'BBG001SRZXL3', 'BBG001SRZXS6', 'BBG001SRZXV2', 'BBG001SRZXW1', 'BBG001SRZXX0', 'BBG001SRZYS4', 'BBG001SRZYT3', 'BBG001SRZYV0', 'BBG001SRZZ29', 'BBG001SRZZ65', 'BBG001SS0950', 'BBG001SS0996', 'BBG001SS09B3', 'BBG001SS09C2', 'BBG001SS09D1', 'BBG001SS0DL3', 'BBG001SS0FH3', 'BBG001SS0FJ1', 'BBG001SS0HX1', 'BBG001SS0J76', 'BBG001SS0KV6', 'BBG001SS0LD4', 'BBG001SS0V24', 'BBG001SS0VB4', 'BBG001SS0VK4', 'BBG001SS0YX4', 'BBG001SS0ZZ9', 'BBG001SS1085', 'BBG001SS10F7', 'BBG001SS15H4', 'BBG001SS17H0', 'BBG001SS1B09', 'BBG001SS1JB0', 'BBG001SS1K90', 'BBG001SS1LR8', 'BBG001SS1NH5', 'BBG001SS1PV4', 'BBG001SS1PY1', 'BBG001SS1PZ0', 'BBG001SS1WH5', 'BBG001SS22W3', 'BBG001SS2BH0', 'BBG001SS2DG7', 'BBG001SS2F27', 'BBG001SS2WV8', 'BBG001SS30F5', 'BBG001SS30K9', 'BBG001SS31W4', 'BBG001SS35B8', 'BBG001SS36Q0', 'BBG001SS3BH9', 'BBG001SS3KF1', 'BBG001SS3L23', 'BBG001SS3M03', 'BBG001SS3M30', 'BBG001SS3PY9', 'BBG001SS3R20', 'BBG001SS3R48', 'BBG001SS3SK8', 'BBG001SS3WV7', 'BBG001SS3Y16', 'BBG001SS40Z2', 'BBG001SS4634', 'BBG001SS4BL3', 'BBG001SS4KZ8', 'BBG001SS4P23', 'BBG001SS5DH3', 'BBG001SS5KV1', 'BBG001SS5LM9', 'BBG001SS5QC9', 'BBG001SS5QV8', 'BBG001SS5VX5', 'BBG001SS6CP5', 'BBG001SS6T14', 'BBG001SS70P0', 'BBG001SS72L0', 'BBG001SS7463', 'BBG001SS74D5', 'BBG001SS78K8', 'BBG001SS78M6', 'BBG001SS78R1', 'BBG001SS7P93', 'BBG001SS80F0', 'BBG001SS81P7', 'BBG001SS81R5', 'BBG001SS8220', 'BBG001SS8248', 'BBG001SS8658', 'BBG001SS8685', 'BBG001SS86F7', 'BBG001SS86G6', 'BBG001SS86K1', 'BBG001SS86M9', 'BBG001SS8729', 'BBG001SS8R16', 'BBG001SS8XS4', 'BBG001SS94R8', 'BBG001SS9G64', 'BBG001SSB3T5', 'BBG001SSBCM2', 'BBG001SSBCY9', 'BBG001SSBKX2', 'BBG001SSBLS6', 'BBG001SSBTC6', 'BBG001SSBTK7', 'BBG001SSC5B8', 'BBG001SSCGC3', 'BBG001SSCPS6', 'BBG001SSCS82', 'BBG001SSCT17', 'BBG001SSCT99', 'BBG001SSCTP1', 'BBG001SSCTQ0', 'BBG001SSCTT7', 'BBG001SSD803', 'BBG001SSD812', 'BBG001SSD830', 'BBG001SSD849', 'BBG001SSD858', 'BBG001SSD876', 'BBG001SSD885', 'BBG001SSD894', 'BBG001SSD8B1', 'BBG001SSDCC1', 'BBG001SSDL86', 'BBG001SSDLM0', 'BBG001SSDLP7', 'BBG001SSDLR5', 'BBG001SSDLS4', 'BBG001SSDLT3', 'BBG001SSDLZ6', 'BBG001SSF7F7', 'BBG001SSF7H5', 'BBG001SSFN44', 'BBG001SSFN80', 'BBG001SSFX42', 'BBG001SSG0F1', 'BBG001SSG0N2', 'BBG001SSG5G9', 'BBG001SSG953', 'BBG001SSG980', 'BBG001SSGBL0', 'BBG001SSGF22', 'BBG001SSGGL9', 'BBG001SSGHJ0', 'BBG001SSGJB4', 'BBG001SSGJC3', 'BBG001SSGXZ7', 'BBG001SSGYL0', 'BBG001SSGYY6', 'BBG001SSH3K8', 'BBG001SSHFZ5', 'BBG001SSHNW1', 'BBG001SSHQR0', 'BBG001SSHRS7', 'BBG001SSHS05', 'BBG001SSHXP7', 'BBG001SSJ0C1', 'BBG001SSJ2K8', 'BBG001SSJ2X4', 'BBG001SSJCM4', 'BBG001SSJLN3', 'BBG001SSJSP6', 'BBG001SSJYS0', 'BBG001SSK0K0', 'BBG001SSP7N7', 'BBG001SSPFD0', 'BBG001SSPRJ8', 'BBG001SSPWL4', 'BBG001SSPXC2', 'BBG001SSPXQ7', 'BBG001SSQ3R1', 'BBG001SSQ4R9', 'BBG001SSQ4Z0', 'BBG001SSQDF2', 'BBG001SSQDH0', 'BBG001SSQDL5', 'BBG001SSQGT0', 'BBG001SSQLY3', 'BBG001SSQMH0', 'BBG001SSQW15', 'BBG001SSR9R7', 'BBG001SSRDW2', 'BBG001SSRFZ4', 'BBG001SSRG00', 'BBG001SSRHD4', 'BBG001SSRPL7', 'BBG001SSRPN5', 'BBG001SSRPP3', 'BBG001SSS022', 'BBG001SSSBF4', 'BBG001SSSG18', 'BBG001SSSMG9', 'BBG001SSSNF8', 'BBG001SSSNP7', 'BBG001SSSNQ6', 'BBG001SSSS94', 'BBG001SSSWR5', 'BBG001SSSWY7', 'BBG001SSSZS7', 'BBG001SST003', 'BBG001SST049', 'BBG001SST0B1', 'BBG001SST0D9', 'BBG001SST0J3', 'BBG001SST0Z5', 'BBG001SSTCF1', 'BBG001SSTCJ7', 'BBG001SSTCK5', 'BBG001SSTCL4', 'BBG001SSTCM3', 'BBG001SSTCQ9', 'BBG001SSTCR8', 'BBG001SSTCT6', 'BBG001SSTCW2', 'BBG001SSTCY0', 'BBG001SSTCZ9', 'BBG001SSTD05', 'BBG001SSTD14', 'BBG001SSTD23', 'BBG001SSTD41', 'BBG001SSTD50', 'BBG001SSTD69', 'BBG001SSTD78', 'BBG001SSTD87', 'BBG001SSTDB3', 'BBG001SSTDC2', 'BBG001SSTDD1', 'BBG001SSTH88', 'BBG001SSTNC0', 'BBG001SSTQ33', 'BBG001SSTRN9', 'BBG001SSV2L3', 'BBG001SSV4X6', 'BBG001SSV992', 'BBG001SSVBZ8', 'BBG001SSVGF9', 'BBG001SSVQM9', 'BBG001SSVT89', 'BBG001SSW232', 'BBG001SSW269', 'BBG001SSW2K3', 'BBG001SSW9F4', 'BBG001SSWNX3', 'BBG001SSXF96', 'BBG001SSXGC0', 'BBG001SSXJ16', 'BBG001SSXJF1', 'BBG001SSXKF8', 'BBG001SSXKV0', 'BBG001SSXKY7', 'BBG001SSXL11', 'BBG001SSXL39', 'BBG001SSXL57', 'BBG001SSXL66', 'BBG001SSXNN3', 'BBG001SSXNQ0', 'BBG001SSY5L3', 'BBG001SSY855', 'BBG001SSYFF8', 'BBG001SSYS60', 'BBG001SSYTZ6', 'BBG001SSYVX3', 'BBG001SSYWD3', 'BBG001SSZ024', 'BBG001SSZ088', 'BBG001SSZ097', 'BBG001SSZ0Q8', 'BBG001SSZ2K0', 'BBG001SSZB57', 'BBG001SSZB66', 'BBG001SSZBC9', 'BBG001SSZBH4', 'BBG001SSZBN7', 'BBG001SSZGH3', 'BBG001SSZKQ4', 'BBG001SSZNJ6', 'BBG001SSZNY9', 'BBG001SSZNZ8', 'BBG001SSZR43', 'BBG001SSZW79', 'BBG001SSZZ36', 'BBG001ST00J2', 'BBG001ST00L9', 'BBG001ST07H9', 'BBG001ST08X9', 'BBG001ST0D13', 'BBG001ST0MH6', 'BBG001ST0RW8', 'BBG001ST0XR1', 'BBG001ST0ZQ7', 'BBG001ST10K9', 'BBG001ST12P0', 'BBG001ST18J4', 'BBG001ST1GJ6', 'BBG001ST1PL3', 'BBG001ST1Y70', 'BBG001ST24L9', 'BBG001ST24Z4', 'BBG001ST2581', 'BBG001ST29Y4', 'BBG001ST2G69', 'BBG001ST2G96', 'BBG001ST2GK3', 'BBG001ST2GM1', 'BBG001ST2JV5', 'BBG001ST2M66', 'BBG001ST2NZ2', 'BBG001ST2V84', 'BBG001ST2XF2', 'BBG001ST32J5', 'BBG001ST38G5', 'BBG001ST39Q2', 'BBG001ST39R1', 'BBG001ST39S0', 'BBG001ST3FM2', 'BBG001ST4703', 'BBG001ST4D64', 'BBG001ST4D73', 'BBG001ST4HV7', 'BBG001ST4T05', 'BBG001ST4WN3', 'BBG001ST4X42', 'BBG001ST4X51', 'BBG001ST5FL1', 'BBG001ST5GJ2', 'BBG001ST5W34', 'BBG001ST5XW0', 'BBG001ST63J0', 'BBG001ST67S1', 'BBG001ST68N4', 'BBG001ST68P2', 'BBG001ST68Q1', 'BBG001ST68R0', 'BBG001ST68S9', 'BBG001ST68V5', 'BBG001ST68W4', 'BBG001ST68X3', 'BBG001ST68Z1', 'BBG001ST6916', 'BBG001ST6934', 'BBG001ST6943', 'BBG001ST6952', 'BBG001ST6961', 'BBG001ST6970', 'BBG001ST6998', 'BBG001ST69T6', 'BBG001ST6GG4', 'BBG001ST6GJ1', 'BBG001ST6HL6', 'BBG001ST6J87', 'BBG001ST6WX0', 'BBG001ST7087', 'BBG001ST7158', 'BBG001ST7265', 'BBG001ST7B10', 'BBG001ST7G55', 'BBG001ST7G73', 'BBG001ST7GB8', 'BBG001ST7M52', 'BBG001ST7MZ9', 'BBG001ST7PG3', 'BBG001ST82P3', 'BBG001ST82Y3', 'BBG001ST83R9', 'BBG001ST8DV2', 'BBG001ST8K37', 'BBG001ST8L99', 'BBG001ST8QH9', 'BBG001ST8WF8', 'BBG001ST96N5', 'BBG001ST96V6', 'BBG001ST98V2', 'BBG001ST99N9', 'BBG001ST9B63', 'BBG001ST9G35', 'BBG001ST9GV4', 'BBG001ST9K09', 'BBG001ST9LP0', 'BBG001STB2W1', 'BBG001STB5B7', 'BBG001STBC13', 'BBG001STBDB0', 'BBG001STBDG5', 'BBG001STBDR3', 'BBG001STBM75', 'BBG001STBR92', 'BBG001STBRD7', 'BBG001STBRF5', 'BBG001STBRG4', 'BBG001STBRH3', 'BBG001STBRR2', 'BBG001STC1D3', 'BBG001STF1M0', 'BBG001STFFC0', 'BBG001STFTT2', 'BBG001STFV35', 'BBG001STG611', 'BBG001STG620', 'BBG001STGMK5', 'BBG001STGSR5', 'BBG001STGYX5', 'BBG001STH175', 'BBG001STH2P3', 'BBG001STHBJ0', 'BBG001STHC62', 'BBG001STHCY1', 'BBG001STHCZ0', 'BBG001STHDJ6', 'BBG001STHGR0', 'BBG001STHGW4', 'BBG001STHSQ5', 'BBG001STHTL8', 'BBG001STJD04', 'BBG001STJDS4', 'BBG001STJFY2', 'BBG001STJJN6', 'BBG001STJJR2', 'BBG001STJXR1', 'BBG001STJXS0', 'BBG001STK4M8', 'BBG001STK554', 'BBG001STK7F9', 'BBG001STK894', 'BBG001STKCW9', 'BBG001STKCX8', 'BBG001STKCY7', 'BBG001STKCZ6', 'BBG001STKD11', 'BBG001STKGB3', 'BBG001STKPK3', 'BBG001STKSP2', 'BBG001STKXP1', 'BBG001STL5S8', 'BBG001STLMJ1', 'BBG001STLRP3', 'BBG001STLSK6', 'BBG001STLVB9', 'BBG001STLY96', 'BBG001STLYG8', 'BBG001STLZP5', 'BBG001STM1F0', 'BBG001STM838', 'BBG001STM9Y2', 'BBG001STMHG4', 'BBG001STMJG0', 'BBG001STMP49', 'BBG001STN3Y4', 'BBG001STN6W9', 'BBG001STNTP7', 'BBG001STNTY7', 'BBG001STNYV9', 'BBG001STNYY6', 'BBG001STP069', 'BBG001STP9N1', 'BBG001STPJW9', 'BBG001STPLG2', 'BBG001STPPK8', 'BBG001STPW14', 'BBG001STPW96', 'BBG001STQ0H6', 'BBG001STQ193', 'BBG001STQFY4', 'BBG001STQGL6', 'BBG001STQRH7', 'BBG001STR4S5', 'BBG001STR5N7', 'BBG001STR708', 'BBG001STRH79', 'BBG001STRP71', 'BBG001STRR86', 'BBG001STRS84', 'BBG001STRVL2', 'BBG001STRZ24', 'BBG001STS0K0', 'BBG001STS244', 'BBG001STSC42', 'BBG001STSKG1', 'BBG001STSMS4', 'BBG001STSMT3', 'BBG001STSXC7', 'BBG001STSZM1', 'BBG001STSZN0', 'BBG001STSZP8', 'BBG001STT6Q0', 'BBG001STT6T7', 'BBG001STTSL7', 'BBG001STTT99', 'BBG001STTZR5', 'BBG001STV3Q4', 'BBG001STV4H2', 'BBG001STV4X4', 'BBG001STV6N0', 'BBG001STV8J1', 'BBG001STV9K7', 'BBG001STV9L6', 'BBG001STV9N4', 'BBG001STVBR5', 'BBG001STVDG3', 'BBG001STVFX9', 'BBG001STW0V3', 'BBG001STW2Q5', 'BBG001STWCQ3', 'BBG001STWPZ4', 'BBG001STWVN4', 'BBG001STWVY2', 'BBG001STWW61', 'BBG001STX5B3', 'BBG001STX5R6', 'BBG001STX5S5', 'BBG001STX5Y8', 'BBG001STX5Z7', 'BBG001STX630', 'BBG001STX658', 'BBG001STXM62', 'BBG001STXPM7', 'BBG001STXSW0', 'BBG001STXVZ0', 'BBG001STXXW9', 'BBG001STXZS9', 'BBG001STXZV5', 'BBG001STXZX3', 'BBG001STY997', 'BBG001STYNV1', 'BBG001STYX58', 'BBG001STYZH0', 'BBG001STZBS0', 'BBG001STZCF2', 'BBG001STZD33', 'BBG001STZGL6', 'BBG001STZM33', 'BBG001SV01R6', 'BBG001SV01Y8', 'BBG001SV0267', 'BBG001SV03R2', 'BBG001SV0515', 'BBG001SV0SC3', 'BBG001SV0Y67', 'BBG001SV0YS3', 'BBG001SV1514', 'BBG001SV1SY8', 'BBG001SV1V17', 'BBG001SV2586', 'BBG001SV25M0', 'BBG001SV2G19', 'BBG001SV2HX2', 'BBG001SV2J77', 'BBG001SV2QM4', 'BBG001SV2ZZ0', 'BBG001SV34L3', 'BBG001SV3772', 'BBG001SV3K37', 'BBG001SV3KT9', 'BBG001SV3L17', 'BBG001SV3L44', 'BBG001SV3S67', 'BBG001SV4129', 'BBG001SV44J5', 'BBG001SV4904', 'BBG001SV4913', 'BBG001SV4F73', 'BBG001SV4FP3', 'BBG001SV4HB4', 'BBG001SV4J48', 'BBG001SV4JH4', 'BBG001SV4JT1', 'BBG001SV4KB7', 'BBG001SV4XJ1', 'BBG001SV4YJ9', 'BBG001SV4YK7', 'BBG001SV50F6', 'BBG001SV51C7', 'BBG001SV5CV2', 'BBG001SV5F45', 'BBG001SV5HS5', 'BBG001SV5WW7', 'BBG001SV6BN2', 'BBG001SV6C96', 'BBG001SV6RG5', 'BBG001SV77D1', 'BBG001SV77W0', 'BBG001SV7876', 'BBG001SV7DC9', 'BBG001SV7KH8', 'BBG001SV7NS0', 'BBG001SV7PY8', 'BBG001SV7RD7', 'BBG001SV7S09', 'BBG001SV7S36', 'BBG001SV7XL5', 'BBG001SV7XM4', 'BBG001SV8429', 'BBG001SV88R3', 'BBG001SV8955', 'BBG001SV8991', 'BBG001SV8B05', 'BBG001SV8G68', 'BBG001SV8P59', 'BBG001SV8T97', 'BBG001SV8TK4', 'BBG001SV8V47', 'BBG001SV96M1', 'BBG001SV97P6', 'BBG001SV9CJ2', 'BBG001SV9D28', 'BBG001SV9KB2', 'BBG001SV9WC5', 'BBG001SV9XY9', 'BBG001SV9YQ6', 'BBG001SV9YS4', 'BBG001SV9Z38', 'BBG001SV9Z74', 'BBG001SVB1B1', 'BBG001SVBFD8', 'BBG001SVBFG5', 'BBG001SVBFH4', 'BBG001SVBFK0', 'BBG001SVBGT9', 'BBG001SVBT66', 'BBG001SVBTF6', 'BBG001SVBTK0', 'BBG001SVBTL9', 'BBG001SVBV16', 'BBG001SVC237', 'BBG001SVC6C8', 'BBG001SVCBF4', 'BBG001SVCXZ4', 'BBG001SVCYM6', 'BBG001SVCYN5', 'BBG001SVD147', 'BBG001SVD1D7', 'BBG001SVD2P2', 'BBG001SVD2R0', 'BBG001SVDK72', 'BBG001SVDPP1', 'BBG001SVF9T1', 'BBG001SVS9X2', 'BBG001SVV6F4', 'BBG001SVVJ84', 'BBG001SVVJB0', 'BBG001SVVJF6', 'BBG001SVWG25', 'BBG001SW6QD8', 'BBG001SWHS51', 'BBG001SWQ2T2', 'BBG001SWYW17', 'BBG001SX2G97', 'BBG001SX2XR0', 'BBG001SX98C4', 'BBG001SXT9D9', 'BBG001SXT9K1', 'BBG001SXTH87', 'BBG001SXXC92', 'BBG001SY0WV7', 'BBG001SYB2H7', 'BBG001SYB8S2', 'BBG001SYBRJ0', 'BBG001SYBS44', 'BBG001SYBVV7', 'BBG001SYH860', 'BBG001SYRLX0', 'BBG001SYS083', 'BBG001SYVNP0', 'BBG001SYVWS7', 'BBG001SYVWT6', 'BBG001SYVWY0', 'BBG001SYWDT7', 'BBG001SYWFH5', 'BBG001SYXSJ4', 'BBG001SZ3FQ4', 'BBG001SZ97W9', 'BBG001SZCCQ1', 'BBG001SZDBR1', 'BBG001SZDCV4', 'BBG001SZDDR7', 'BBG001SZDDV2', 'BBG001SZDFM7', 'BBG001SZDM51', 'BBG001SZDQ25', 'BBG001SZDZD3', 'BBG001SZJ1L3', 'BBG001SZK2K0', 'BBG001SZKQ72', 'BBG001SZKZP2', 'BBG001SZTC83', 'BBG001SZTCB9', 'BBG001SZTM45', 'BBG001SZTY30', 'BBG001SZV282', 'BBG001SZV745', 'BBG001SZV8X1', 'BBG001SZV941', 'BBG001SZV978', 'BBG001SZV9C2', 'BBG001SZV9M1', 'BBG001SZVBZ2', 'BBG001SZVVN1', 'BBG001SZW5G5', 'BBG001SZW7P1', 'BBG001SZW7Q0', 'BBG001SZW7S8', 'BBG001SZWN85', 'BBG001SZWS93', 'BBG001SZWWC0', 'BBG001SZWXS1', 'BBG001SZWY27', 'BBG001SZWYY2', 'BBG001SZX397', 'BBG001SZXBZ0', 'BBG001SZXC15', 'BBG001SZXNQ4', 'BBG001SZY476', 'BBG001SZY4X7', 'BBG001SZY7G9', 'BBG001SZYQ86', 'BBG001SZYV03', 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'BBG01BN8Q306', 'BBG01BN8QHM1', 'BBG01BNNTQP2', 'BBG01BPH5MK3', 'BBG01BQ40S32', 'BBG01BTRTRF6', 'BBG01BTSPHM2', 'BBG01BTSR0M7', 'BBG01BTT0L11', 'BBG01BTTD0X8', 'BBG01BWBNB31', 'BBG01BWDFP53', 'BBG01BWDHVV9', 'BBG01BWDKSD2', 'BBG01BWRX8Q7', 'BBG01BWS8PR4', 'BBG01BWV1H47', 'BBG01BXXHCD6', 'BBG01BXXJCN3', 'BBG01BY3XQ06', 'BBG01BY3ZP60', 'BBG01BYD75L6'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 119505 |
| valid | 118419 |
| snowdere/autotrain-data-stratefied-processing | [
"task_categories:text-classification",
"region:us"
]
| 2023-02-23T11:59:00+00:00 | {"task_categories": ["text-classification"]} | 2023-02-23T12:00:19+00:00 | []
| []
| TAGS
#task_categories-text-classification #region-us
| AutoTrain Dataset for project: stratefied-processing
====================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project stratefied-processing.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-text-classification #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
17,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-text-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
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|
8afc7e3e962993e1f1ea5e92724948a053628d42 | # Dataset Card for "VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_400_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_400_open_ended | [
"region:us"
]
| 2023-02-23T12:12:54+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_32", "num_bytes": 58700, "num_examples": 400}], "download_size": 0, "dataset_size": 58700}} | 2023-02-23T13:23:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_400_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_400_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_400_open_ended\"\n\nMore Information needed"
]
| [
6,
50
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_400_open_ended\"\n\nMore Information needed"
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|
c0b62e28e0a6eb826f5022b89bb12183d6f659b4 | # Dataset Card for "spacy_sm_wnut17"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | davidberenstein1957/spacy_sm_wnut17 | [
"region:us"
]
| 2023-02-23T12:31:55+00:00 | {"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-CARDINAL", "2": "I-CARDINAL", "3": "B-DATE", "4": "I-DATE", "5": "B-EVENT", "6": "I-EVENT", "7": "B-FAC", "8": "I-FAC", "9": "B-GPE", "10": "I-GPE", "11": "B-LAW", "12": "I-LAW", "13": "B-LOC", "14": "I-LOC", "15": "B-MONEY", "16": "I-MONEY", "17": "B-NORP", "18": "I-NORP", "19": "B-ORDINAL", "20": "I-ORDINAL", "21": "B-ORG", "22": "I-ORG", "23": "B-PERCENT", "24": "I-PERCENT", "25": "B-PERSON", "26": "I-PERSON", "27": "B-QUANTITY", "28": "I-QUANTITY", "29": "B-TIME", "30": "I-TIME", "31": "B-WORK_OF_ART", "32": "I-WORK_OF_ART"}}}}], "splits": [{"name": "train", "num_bytes": 40051.2, "num_examples": 120}, {"name": "test", "num_bytes": 10012.8, "num_examples": 30}], "download_size": 19486, "dataset_size": 50064.0}} | 2023-03-23T08:56:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "spacy_sm_wnut17"
More Information needed | [
"# Dataset Card for \"spacy_sm_wnut17\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"spacy_sm_wnut17\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"spacy_sm_wnut17\"\n\nMore Information needed"
]
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|
c738e32f58dd1b06676615d6c4de2aabbdeb0540 |
This is a synthetically generated dataset you can generate your own with the help of below codes in this github repo: https://github.com/suchinthana00/Synthetic_OCR_Dataset_Generator | Suchinthana/Sinhala_OCR_Dataset_Synthetic | [
"size_categories:1K<n<10K",
"language:si",
"license:mit",
"region:us"
]
| 2023-02-23T12:56:52+00:00 | {"language": ["si"], "license": "mit", "size_categories": ["1K<n<10K"], "pretty_name": "g"} | 2024-01-30T21:07:17+00:00 | []
| [
"si"
]
| TAGS
#size_categories-1K<n<10K #language-Sinhala #license-mit #region-us
|
This is a synthetically generated dataset you can generate your own with the help of below codes in this github repo: URL | []
| [
"TAGS\n#size_categories-1K<n<10K #language-Sinhala #license-mit #region-us \n"
]
| [
29
]
| [
"passage: TAGS\n#size_categories-1K<n<10K #language-Sinhala #license-mit #region-us \n"
]
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|
71f514a0175a5747c1e9742c94aa0969aafa9703 |
## DeepFake electrocardiograms: the beginning of the end for privacy issues in medicine
[Paper](https://www.nature.com/articles/s41598-021-01295-2)
[GitHub](https://github.com/vlbthambawita/deepfake-ecg)
[Original-data-source](https://osf.io/6hved/)
[PyPI](https://pypi.org/project/deepfake-ecg/)
## How to download
### Option 1
``` python
from datasets import load_dataset
dataset = load_dataset("deepsynthbody/deepfake_ecg")
```
### Option 2
```bash
git lfs install
git clone https://huggingface.co/datasets/deepsynthbody/deepfake_ecg
# if you want to clone without large files – just their pointers
# prepend your git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1
```
## Demo of using the generator used to generate this dataset
https://huggingface.co/spaces/deepsynthbody/deepfake-ecg-generator
### Content
Big data is needed to implement personalized medicine, but privacy issues are a prevalent problem for collecting data and sharing them between researchers. A solution is synthetic data generated to represent real dataset carrying similar information. Here, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 12-lead 10-sec electrocardiograms (ECGs). We have developed and compare two methods, namely WaveGAN* and Pulse2Pulse GAN. We trained the GANs with 7,233 real normal ECG to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. These synthetic ECGs are fully anonymous and cannot be referred to any individual, hence they may be used freely. The synthetic dataset will be available as open access for researchers at OSF.io and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs. In conclusion, we were able to generate realistic synthetic ECGs using adversarial neural networks on normal ECGs from two population studies, i.e., there by solving the relevant privacy issues in medical datasets.
### Citation (cite this paper to use this dataset in your research)
```latex
@article{thambawita2021deepfake,
title={DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine},
author={Thambawita, Vajira and Isaksen, Jonas L and Hicks, Steven A and Ghouse, Jonas and Ahlberg, Gustav and Linneberg, Allan and Grarup, Niels and Ellervik, Christina and Olesen, Morten Salling and Hansen, Torben and others},
journal={Scientific reports},
volume={11},
number={1},
pages={1--8},
year={2021},
publisher={Nature Publishing Group}
}
``` | deepsynthbody/deepfake_ecg | [
"size_categories:10B<n<100B",
"language:en",
"license:cc-by-4.0",
"generation",
"ECG generation",
"region:us"
]
| 2023-02-23T12:58:42+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["10B<n<100B"], "pretty_name": "DeepFake-ECG", "tags": ["generation", "ECG generation"]} | 2023-03-10T20:24:49+00:00 | []
| [
"en"
]
| TAGS
#size_categories-10B<n<100B #language-English #license-cc-by-4.0 #generation #ECG generation #region-us
|
## DeepFake electrocardiograms: the beginning of the end for privacy issues in medicine
Paper
GitHub
Original-data-source
PyPI
## How to download
### Option 1
### Option 2
## Demo of using the generator used to generate this dataset
URL
### Content
Big data is needed to implement personalized medicine, but privacy issues are a prevalent problem for collecting data and sharing them between researchers. A solution is synthetic data generated to represent real dataset carrying similar information. Here, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 12-lead 10-sec electrocardiograms (ECGs). We have developed and compare two methods, namely WaveGAN* and Pulse2Pulse GAN. We trained the GANs with 7,233 real normal ECG to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. These synthetic ECGs are fully anonymous and cannot be referred to any individual, hence they may be used freely. The synthetic dataset will be available as open access for researchers at URL and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs. In conclusion, we were able to generate realistic synthetic ECGs using adversarial neural networks on normal ECGs from two population studies, i.e., there by solving the relevant privacy issues in medical datasets.
(cite this paper to use this dataset in your research)
| [
"## DeepFake electrocardiograms: the beginning of the end for privacy issues in medicine\n\nPaper \nGitHub \nOriginal-data-source \nPyPI",
"## How to download",
"### Option 1",
"### Option 2",
"## Demo of using the generator used to generate this dataset\n\nURL",
"### Content\n\nBig data is needed to implement personalized medicine, but privacy issues are a prevalent problem for collecting data and sharing them between researchers. A solution is synthetic data generated to represent real dataset carrying similar information. Here, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 12-lead 10-sec electrocardiograms (ECGs). We have developed and compare two methods, namely WaveGAN* and Pulse2Pulse GAN. We trained the GANs with 7,233 real normal ECG to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. These synthetic ECGs are fully anonymous and cannot be referred to any individual, hence they may be used freely. The synthetic dataset will be available as open access for researchers at URL and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs. In conclusion, we were able to generate realistic synthetic ECGs using adversarial neural networks on normal ECGs from two population studies, i.e., there by solving the relevant privacy issues in medical datasets.\n\n\n\n(cite this paper to use this dataset in your research)"
]
| [
"TAGS\n#size_categories-10B<n<100B #language-English #license-cc-by-4.0 #generation #ECG generation #region-us \n",
"## DeepFake electrocardiograms: the beginning of the end for privacy issues in medicine\n\nPaper \nGitHub \nOriginal-data-source \nPyPI",
"## How to download",
"### Option 1",
"### Option 2",
"## Demo of using the generator used to generate this dataset\n\nURL",
"### Content\n\nBig data is needed to implement personalized medicine, but privacy issues are a prevalent problem for collecting data and sharing them between researchers. A solution is synthetic data generated to represent real dataset carrying similar information. Here, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 12-lead 10-sec electrocardiograms (ECGs). We have developed and compare two methods, namely WaveGAN* and Pulse2Pulse GAN. We trained the GANs with 7,233 real normal ECG to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. These synthetic ECGs are fully anonymous and cannot be referred to any individual, hence they may be used freely. The synthetic dataset will be available as open access for researchers at URL and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs. In conclusion, we were able to generate realistic synthetic ECGs using adversarial neural networks on normal ECGs from two population studies, i.e., there by solving the relevant privacy issues in medical datasets.\n\n\n\n(cite this paper to use this dataset in your research)"
]
| [
38,
31,
4,
4,
4,
13,
351
]
| [
"passage: TAGS\n#size_categories-10B<n<100B #language-English #license-cc-by-4.0 #generation #ECG generation #region-us \n## DeepFake electrocardiograms: the beginning of the end for privacy issues in medicine\n\nPaper \nGitHub \nOriginal-data-source \nPyPI## How to download### Option 1### Option 2## Demo of using the generator used to generate this dataset\n\nURL### Content\n\nBig data is needed to implement personalized medicine, but privacy issues are a prevalent problem for collecting data and sharing them between researchers. A solution is synthetic data generated to represent real dataset carrying similar information. Here, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 12-lead 10-sec electrocardiograms (ECGs). We have developed and compare two methods, namely WaveGAN* and Pulse2Pulse GAN. We trained the GANs with 7,233 real normal ECG to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. These synthetic ECGs are fully anonymous and cannot be referred to any individual, hence they may be used freely. The synthetic dataset will be available as open access for researchers at URL and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs. In conclusion, we were able to generate realistic synthetic ECGs using adversarial neural networks on normal ECGs from two population studies, i.e., there by solving the relevant privacy issues in medical datasets.\n\n\n\n(cite this paper to use this dataset in your research)"
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f947ccc827ccd236464e19cd4cc23dfda7fc5575 |
# Dataset Card for Premise Selection in Isabelle
## Dataset Description
- Repository: https://github.com/Simontwice/MagnusData
- Paper: https://arxiv.org/abs/2303.04488
- Point of Contact: [email protected]
### Dataset Summary
The Isabelle Premise Selection Dataset is a collection of premise data mined from the [Archive of Formal Proofs](https://www.isa-afp.org/) and the Standard Library of the interactive theorem prover [Isabelle](https://isabelle.in.tum.de/).
The dataset is designed for the task of *premise selection*, which involves selecting the most relevant premises (also called lemmas) for a given proof state. It includes over **4 million** aligned pairs of proof context and relevant premises.
The dataset includes premises from the original proofs contained in the [Archive of Formal Proofs](https://www.isa-afp.org/) and the Standard Library, as well as additional premises from proofs automatically generated using [Isabelle](https://isabelle.in.tum.de/)'s Sledgehammer tool. This tool employs symbolic methods for automatic premise selection. In effect, the dataset combines multiple sources, which increases its diversity.
The Isabelle Premise Selection Dataset can be a valuable resource for researchers and practitioners in the fields of automated theorem proving, automated reasoning and information retrieval.
### Supported Tasks and Leaderboards
The Isabelle Premise Selection Dataset is designed for the task of premise selection, which involves selecting the most relevant premises for a given proof state. The typical setting for this task is a large developed library of
formally encoded mathematical knowledge, over which mathematicians attempt
to prove new lemmas and theorems. Read [this](https://paperswithcode.com/task/automated-theorem-proving) for more background on Automated Theorem Proving, and [this paper](https://arxiv.org/pdf/1606.04442.pdf) for Premise Selection.
The dataset has been used to train a model for premise selection using batch-contrastive learning in [Magnushammer: A Transformer-based Approach to Premise Selection](https://arxiv.org/abs/2303.04488).
The model achieved state-of-the-art (SOTA) performance on the [PISA](http://aitp-conference.org/2021/abstract/paper_17.pdf) benchmark, achieving a proof rate of 71%. Additionally, the model achieved a very competitive performance of 37.3% on the [miniF2F](https://github.com/openai/miniF2F) benchmark.
### Languages
All information contained in this dataset is written in English and using the Isabelle syntax, which represents mathematical expressions using syntax similar to LaTeX.
## Dataset Structure
### Data Instances
```
{
'statement': 'lemma congruence_congruence_inv [simp]:\n assumes "mat_det M \\<noteq> 0"\n shows "congruence M (congruence (mat_inv M) H) = H"'
'state': 'proof (prove)\nusing this:\n mat_det M \\<noteq> 0\n congruence M (congruence (mat_inv M) H) =\n congruence (mat_inv M *\\<^sub>m\\<^sub>m M) H\n\ngoal (1 subgoal):\n 1. congruence M (congruence (mat_inv M) H) = H'
'step': 'by (metis mat_inv_1 mat_inv_def)'
'premise_name': 'mat_inv_l'
'premise_statement': ' mat_inv_l: fixes A :: "complex \\<times> complex \\<times> complex \\<times> complex" assumes "mat_det A \\<noteq> 0" shows "mat_inv A *\\<^sub>m\\<^sub>m A = eye"'
}
```
### Data Fields
- statement: Description of the original statement of the problem, together with assumptions (if any)
- state: The *poof state* describing the current goals of the proof; it may also contain type information of the objects referenced in the *statement* field
- step: The *proof step*, which is a command that advances the proof. It may introduce definitions or conjectures, or solve the current conjecture. **Note** that including this field as part of the proof state representation results in a data leak, since premise names are always included in the step. This field should not be used for training premise selection models; read more [here](https://github.com/Simontwice/MagnusData).
- premise_name: The name of the ground-truth premise as it would appear in the proof library
- premise_statement: The statement of the ground-truth premise
**Note** that this is **not the only possible format** (fields, number of premise per statement) for this dataset; the scripts used to generate this and other datasets (for e.g. *proof step generation*) are available on [github](https://github.com/Simontwice/MagnusData).
### Data Splits
We make the data available in a single file, but any train/val/test splitting is possible.
### Curation Rationale
This dataset was created to facilitate the training of models for premise selection in Isabelle and potentially other Interactive Theorem Provers ([Lean](https://leanprover.github.io/), [Coq](https://github.com/coq/coq) etc.). There were no existing datasets for Isabelle that used raw text format; prior existing datasets used a translation scheme to selected logics, making the engineering during inference much more involved.
### Source Data
The dataset was created using the proofs included in the [Archive of Formal Proofs](https://www.isa-afp.org/) and the Standard library included in the [Isabelle](https://isabelle.in.tum.de/) 2021-1 distribution.
### Known Limitations
The data included in this dataset is mostly untyped, meaning that there is little information about the objects referenced in the statement or premise statements. Adding type information would be a valuable contribution.
### Licensing Information
This dataset is made available under the Apache License, Version 2.0. This license allows you to use, copy, and modify the dataset, as long as you comply with the terms and conditions of the license. You may also distribute the dataset, either in its original form or as a modified work, provided that you include the license terms with any distribution. There is no warranty for this dataset, and it is provided "as is". If you have any questions or concerns about the licensing or use of the dataset, please open an issue.
### Citation
If you use this dataset in your research, please cite the associated arXiv paper: [Magnushammer: A Transformer-based Approach to Premise Selection](https://arxiv.org/abs/2303.04488)
### Acknowledgements
We would like to express our gratitude to the following individuals and organizations for their contributions to this project:
* We would like to acknowledge [@jinpz](https://github.com/jinpz) for their contributions to the data mining aspect of this project. Their expertise and hard work greatly assisted us in achieving our project goals.
* PISA API: We also want to thank the developers of the [PISA](https://github.com/albertqjiang/Portal-to-ISAbelle) API for creating a powerful tool that allowed us to interact with Isabelle through Python.
* Google TRC Compute: Finally, we want to acknowledge Google's [TPU Reasearch Cloud](https://sites.research.google/trc/about/) for providing compute necessary to develop the code infrastructure needed for the mining procedure.
We are grateful for the support and contributions of each of these individuals and organizations, and we would not have been able to accomplish this project without them. | Simontwice/premise_selection_in_isabelle | [
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| 2023-02-23T13:22:11+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["expert-generated", "machine-generated", "other"], "language": ["en"], "license": "apache-2.0", "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-retrieval"], "task_ids": [], "pretty_name": "Premise Selection in Isabelle", "tags": ["lemma-selection", "premise-selection", "automated-reasoning", "automated-theorem-proving", "text-retrieval"]} | 2023-03-13T10:12:47+00:00 | [
"2303.04488",
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]
| [
"en"
]
| TAGS
#task_categories-text-retrieval #annotations_creators-machine-generated #language_creators-expert-generated #language_creators-machine-generated #language_creators-other #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-apache-2.0 #lemma-selection #premise-selection #automated-reasoning #automated-theorem-proving #text-retrieval #arxiv-2303.04488 #arxiv-1606.04442 #region-us
|
# Dataset Card for Premise Selection in Isabelle
## Dataset Description
- Repository: URL
- Paper: URL
- Point of Contact: szymon.antoniak1@URL
### Dataset Summary
The Isabelle Premise Selection Dataset is a collection of premise data mined from the Archive of Formal Proofs and the Standard Library of the interactive theorem prover Isabelle.
The dataset is designed for the task of *premise selection*, which involves selecting the most relevant premises (also called lemmas) for a given proof state. It includes over 4 million aligned pairs of proof context and relevant premises.
The dataset includes premises from the original proofs contained in the Archive of Formal Proofs and the Standard Library, as well as additional premises from proofs automatically generated using Isabelle's Sledgehammer tool. This tool employs symbolic methods for automatic premise selection. In effect, the dataset combines multiple sources, which increases its diversity.
The Isabelle Premise Selection Dataset can be a valuable resource for researchers and practitioners in the fields of automated theorem proving, automated reasoning and information retrieval.
### Supported Tasks and Leaderboards
The Isabelle Premise Selection Dataset is designed for the task of premise selection, which involves selecting the most relevant premises for a given proof state. The typical setting for this task is a large developed library of
formally encoded mathematical knowledge, over which mathematicians attempt
to prove new lemmas and theorems. Read this for more background on Automated Theorem Proving, and this paper for Premise Selection.
The dataset has been used to train a model for premise selection using batch-contrastive learning in Magnushammer: A Transformer-based Approach to Premise Selection.
The model achieved state-of-the-art (SOTA) performance on the PISA benchmark, achieving a proof rate of 71%. Additionally, the model achieved a very competitive performance of 37.3% on the miniF2F benchmark.
### Languages
All information contained in this dataset is written in English and using the Isabelle syntax, which represents mathematical expressions using syntax similar to LaTeX.
## Dataset Structure
### Data Instances
### Data Fields
- statement: Description of the original statement of the problem, together with assumptions (if any)
- state: The *poof state* describing the current goals of the proof; it may also contain type information of the objects referenced in the *statement* field
- step: The *proof step*, which is a command that advances the proof. It may introduce definitions or conjectures, or solve the current conjecture. Note that including this field as part of the proof state representation results in a data leak, since premise names are always included in the step. This field should not be used for training premise selection models; read more here.
- premise_name: The name of the ground-truth premise as it would appear in the proof library
- premise_statement: The statement of the ground-truth premise
Note that this is not the only possible format (fields, number of premise per statement) for this dataset; the scripts used to generate this and other datasets (for e.g. *proof step generation*) are available on github.
### Data Splits
We make the data available in a single file, but any train/val/test splitting is possible.
### Curation Rationale
This dataset was created to facilitate the training of models for premise selection in Isabelle and potentially other Interactive Theorem Provers (Lean, Coq etc.). There were no existing datasets for Isabelle that used raw text format; prior existing datasets used a translation scheme to selected logics, making the engineering during inference much more involved.
### Source Data
The dataset was created using the proofs included in the Archive of Formal Proofs and the Standard library included in the Isabelle 2021-1 distribution.
### Known Limitations
The data included in this dataset is mostly untyped, meaning that there is little information about the objects referenced in the statement or premise statements. Adding type information would be a valuable contribution.
### Licensing Information
This dataset is made available under the Apache License, Version 2.0. This license allows you to use, copy, and modify the dataset, as long as you comply with the terms and conditions of the license. You may also distribute the dataset, either in its original form or as a modified work, provided that you include the license terms with any distribution. There is no warranty for this dataset, and it is provided "as is". If you have any questions or concerns about the licensing or use of the dataset, please open an issue.
If you use this dataset in your research, please cite the associated arXiv paper: Magnushammer: A Transformer-based Approach to Premise Selection
### Acknowledgements
We would like to express our gratitude to the following individuals and organizations for their contributions to this project:
* We would like to acknowledge @jinpz for their contributions to the data mining aspect of this project. Their expertise and hard work greatly assisted us in achieving our project goals.
* PISA API: We also want to thank the developers of the PISA API for creating a powerful tool that allowed us to interact with Isabelle through Python.
* Google TRC Compute: Finally, we want to acknowledge Google's TPU Reasearch Cloud for providing compute necessary to develop the code infrastructure needed for the mining procedure.
We are grateful for the support and contributions of each of these individuals and organizations, and we would not have been able to accomplish this project without them. | [
"# Dataset Card for Premise Selection in Isabelle",
"## Dataset Description\n\n- Repository: URL \n- Paper: URL\n- Point of Contact: szymon.antoniak1@URL",
"### Dataset Summary\n\nThe Isabelle Premise Selection Dataset is a collection of premise data mined from the Archive of Formal Proofs and the Standard Library of the interactive theorem prover Isabelle.\n\nThe dataset is designed for the task of *premise selection*, which involves selecting the most relevant premises (also called lemmas) for a given proof state. It includes over 4 million aligned pairs of proof context and relevant premises.\n\nThe dataset includes premises from the original proofs contained in the Archive of Formal Proofs and the Standard Library, as well as additional premises from proofs automatically generated using Isabelle's Sledgehammer tool. This tool employs symbolic methods for automatic premise selection. In effect, the dataset combines multiple sources, which increases its diversity.\n\nThe Isabelle Premise Selection Dataset can be a valuable resource for researchers and practitioners in the fields of automated theorem proving, automated reasoning and information retrieval.",
"### Supported Tasks and Leaderboards\n\nThe Isabelle Premise Selection Dataset is designed for the task of premise selection, which involves selecting the most relevant premises for a given proof state. The typical setting for this task is a large developed library of\nformally encoded mathematical knowledge, over which mathematicians attempt\nto prove new lemmas and theorems. Read this for more background on Automated Theorem Proving, and this paper for Premise Selection.\n\nThe dataset has been used to train a model for premise selection using batch-contrastive learning in Magnushammer: A Transformer-based Approach to Premise Selection.\nThe model achieved state-of-the-art (SOTA) performance on the PISA benchmark, achieving a proof rate of 71%. Additionally, the model achieved a very competitive performance of 37.3% on the miniF2F benchmark.",
"### Languages\n\nAll information contained in this dataset is written in English and using the Isabelle syntax, which represents mathematical expressions using syntax similar to LaTeX.",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n- statement: Description of the original statement of the problem, together with assumptions (if any)\n- state: The *poof state* describing the current goals of the proof; it may also contain type information of the objects referenced in the *statement* field\n- step: The *proof step*, which is a command that advances the proof. It may introduce definitions or conjectures, or solve the current conjecture. Note that including this field as part of the proof state representation results in a data leak, since premise names are always included in the step. This field should not be used for training premise selection models; read more here.\n- premise_name: The name of the ground-truth premise as it would appear in the proof library\n- premise_statement: The statement of the ground-truth premise\n\nNote that this is not the only possible format (fields, number of premise per statement) for this dataset; the scripts used to generate this and other datasets (for e.g. *proof step generation*) are available on github.",
"### Data Splits\nWe make the data available in a single file, but any train/val/test splitting is possible.",
"### Curation Rationale\n\nThis dataset was created to facilitate the training of models for premise selection in Isabelle and potentially other Interactive Theorem Provers (Lean, Coq etc.). There were no existing datasets for Isabelle that used raw text format; prior existing datasets used a translation scheme to selected logics, making the engineering during inference much more involved.",
"### Source Data\nThe dataset was created using the proofs included in the Archive of Formal Proofs and the Standard library included in the Isabelle 2021-1 distribution.",
"### Known Limitations\n\nThe data included in this dataset is mostly untyped, meaning that there is little information about the objects referenced in the statement or premise statements. Adding type information would be a valuable contribution.",
"### Licensing Information\n\nThis dataset is made available under the Apache License, Version 2.0. This license allows you to use, copy, and modify the dataset, as long as you comply with the terms and conditions of the license. You may also distribute the dataset, either in its original form or as a modified work, provided that you include the license terms with any distribution. There is no warranty for this dataset, and it is provided \"as is\". If you have any questions or concerns about the licensing or use of the dataset, please open an issue.\n\nIf you use this dataset in your research, please cite the associated arXiv paper: Magnushammer: A Transformer-based Approach to Premise Selection",
"### Acknowledgements\nWe would like to express our gratitude to the following individuals and organizations for their contributions to this project:\n\n* We would like to acknowledge @jinpz for their contributions to the data mining aspect of this project. Their expertise and hard work greatly assisted us in achieving our project goals.\n\n* PISA API: We also want to thank the developers of the PISA API for creating a powerful tool that allowed us to interact with Isabelle through Python.\n\n* Google TRC Compute: Finally, we want to acknowledge Google's TPU Reasearch Cloud for providing compute necessary to develop the code infrastructure needed for the mining procedure.\n\nWe are grateful for the support and contributions of each of these individuals and organizations, and we would not have been able to accomplish this project without them."
]
| [
"TAGS\n#task_categories-text-retrieval #annotations_creators-machine-generated #language_creators-expert-generated #language_creators-machine-generated #language_creators-other #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-apache-2.0 #lemma-selection #premise-selection #automated-reasoning #automated-theorem-proving #text-retrieval #arxiv-2303.04488 #arxiv-1606.04442 #region-us \n",
"# Dataset Card for Premise Selection in Isabelle",
"## Dataset Description\n\n- Repository: URL \n- Paper: URL\n- Point of Contact: szymon.antoniak1@URL",
"### Dataset Summary\n\nThe Isabelle Premise Selection Dataset is a collection of premise data mined from the Archive of Formal Proofs and the Standard Library of the interactive theorem prover Isabelle.\n\nThe dataset is designed for the task of *premise selection*, which involves selecting the most relevant premises (also called lemmas) for a given proof state. It includes over 4 million aligned pairs of proof context and relevant premises.\n\nThe dataset includes premises from the original proofs contained in the Archive of Formal Proofs and the Standard Library, as well as additional premises from proofs automatically generated using Isabelle's Sledgehammer tool. This tool employs symbolic methods for automatic premise selection. In effect, the dataset combines multiple sources, which increases its diversity.\n\nThe Isabelle Premise Selection Dataset can be a valuable resource for researchers and practitioners in the fields of automated theorem proving, automated reasoning and information retrieval.",
"### Supported Tasks and Leaderboards\n\nThe Isabelle Premise Selection Dataset is designed for the task of premise selection, which involves selecting the most relevant premises for a given proof state. The typical setting for this task is a large developed library of\nformally encoded mathematical knowledge, over which mathematicians attempt\nto prove new lemmas and theorems. Read this for more background on Automated Theorem Proving, and this paper for Premise Selection.\n\nThe dataset has been used to train a model for premise selection using batch-contrastive learning in Magnushammer: A Transformer-based Approach to Premise Selection.\nThe model achieved state-of-the-art (SOTA) performance on the PISA benchmark, achieving a proof rate of 71%. Additionally, the model achieved a very competitive performance of 37.3% on the miniF2F benchmark.",
"### Languages\n\nAll information contained in this dataset is written in English and using the Isabelle syntax, which represents mathematical expressions using syntax similar to LaTeX.",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n- statement: Description of the original statement of the problem, together with assumptions (if any)\n- state: The *poof state* describing the current goals of the proof; it may also contain type information of the objects referenced in the *statement* field\n- step: The *proof step*, which is a command that advances the proof. It may introduce definitions or conjectures, or solve the current conjecture. Note that including this field as part of the proof state representation results in a data leak, since premise names are always included in the step. This field should not be used for training premise selection models; read more here.\n- premise_name: The name of the ground-truth premise as it would appear in the proof library\n- premise_statement: The statement of the ground-truth premise\n\nNote that this is not the only possible format (fields, number of premise per statement) for this dataset; the scripts used to generate this and other datasets (for e.g. *proof step generation*) are available on github.",
"### Data Splits\nWe make the data available in a single file, but any train/val/test splitting is possible.",
"### Curation Rationale\n\nThis dataset was created to facilitate the training of models for premise selection in Isabelle and potentially other Interactive Theorem Provers (Lean, Coq etc.). There were no existing datasets for Isabelle that used raw text format; prior existing datasets used a translation scheme to selected logics, making the engineering during inference much more involved.",
"### Source Data\nThe dataset was created using the proofs included in the Archive of Formal Proofs and the Standard library included in the Isabelle 2021-1 distribution.",
"### Known Limitations\n\nThe data included in this dataset is mostly untyped, meaning that there is little information about the objects referenced in the statement or premise statements. Adding type information would be a valuable contribution.",
"### Licensing Information\n\nThis dataset is made available under the Apache License, Version 2.0. This license allows you to use, copy, and modify the dataset, as long as you comply with the terms and conditions of the license. You may also distribute the dataset, either in its original form or as a modified work, provided that you include the license terms with any distribution. There is no warranty for this dataset, and it is provided \"as is\". If you have any questions or concerns about the licensing or use of the dataset, please open an issue.\n\nIf you use this dataset in your research, please cite the associated arXiv paper: Magnushammer: A Transformer-based Approach to Premise Selection",
"### Acknowledgements\nWe would like to express our gratitude to the following individuals and organizations for their contributions to this project:\n\n* We would like to acknowledge @jinpz for their contributions to the data mining aspect of this project. Their expertise and hard work greatly assisted us in achieving our project goals.\n\n* PISA API: We also want to thank the developers of the PISA API for creating a powerful tool that allowed us to interact with Isabelle through Python.\n\n* Google TRC Compute: Finally, we want to acknowledge Google's TPU Reasearch Cloud for providing compute necessary to develop the code infrastructure needed for the mining procedure.\n\nWe are grateful for the support and contributions of each of these individuals and organizations, and we would not have been able to accomplish this project without them."
]
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"passage: TAGS\n#task_categories-text-retrieval #annotations_creators-machine-generated #language_creators-expert-generated #language_creators-machine-generated #language_creators-other #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-apache-2.0 #lemma-selection #premise-selection #automated-reasoning #automated-theorem-proving #text-retrieval #arxiv-2303.04488 #arxiv-1606.04442 #region-us \n# Dataset Card for Premise Selection in Isabelle## Dataset Description\n\n- Repository: URL \n- Paper: URL\n- Point of Contact: szymon.antoniak1@URL### Dataset Summary\n\nThe Isabelle Premise Selection Dataset is a collection of premise data mined from the Archive of Formal Proofs and the Standard Library of the interactive theorem prover Isabelle.\n\nThe dataset is designed for the task of *premise selection*, which involves selecting the most relevant premises (also called lemmas) for a given proof state. It includes over 4 million aligned pairs of proof context and relevant premises.\n\nThe dataset includes premises from the original proofs contained in the Archive of Formal Proofs and the Standard Library, as well as additional premises from proofs automatically generated using Isabelle's Sledgehammer tool. This tool employs symbolic methods for automatic premise selection. In effect, the dataset combines multiple sources, which increases its diversity.\n\nThe Isabelle Premise Selection Dataset can be a valuable resource for researchers and practitioners in the fields of automated theorem proving, automated reasoning and information retrieval.",
"passage: ### Supported Tasks and Leaderboards\n\nThe Isabelle Premise Selection Dataset is designed for the task of premise selection, which involves selecting the most relevant premises for a given proof state. The typical setting for this task is a large developed library of\nformally encoded mathematical knowledge, over which mathematicians attempt\nto prove new lemmas and theorems. Read this for more background on Automated Theorem Proving, and this paper for Premise Selection.\n\nThe dataset has been used to train a model for premise selection using batch-contrastive learning in Magnushammer: A Transformer-based Approach to Premise Selection.\nThe model achieved state-of-the-art (SOTA) performance on the PISA benchmark, achieving a proof rate of 71%. Additionally, the model achieved a very competitive performance of 37.3% on the miniF2F benchmark.### Languages\n\nAll information contained in this dataset is written in English and using the Isabelle syntax, which represents mathematical expressions using syntax similar to LaTeX.## Dataset Structure### Data Instances### Data Fields\n\n- statement: Description of the original statement of the problem, together with assumptions (if any)\n- state: The *poof state* describing the current goals of the proof; it may also contain type information of the objects referenced in the *statement* field\n- step: The *proof step*, which is a command that advances the proof. It may introduce definitions or conjectures, or solve the current conjecture. Note that including this field as part of the proof state representation results in a data leak, since premise names are always included in the step. This field should not be used for training premise selection models; read more here.\n- premise_name: The name of the ground-truth premise as it would appear in the proof library\n- premise_statement: The statement of the ground-truth premise\n\nNote that this is not the only possible format (fields, number of premise per statement) for this dataset; the scripts used to generate this and other datasets (for e.g. *proof step generation*) are available on github.### Data Splits\nWe make the data available in a single file, but any train/val/test splitting is possible.### Curation Rationale\n\nThis dataset was created to facilitate the training of models for premise selection in Isabelle and potentially other Interactive Theorem Provers (Lean, Coq etc.). There were no existing datasets for Isabelle that used raw text format; prior existing datasets used a translation scheme to selected logics, making the engineering during inference much more involved.### Source Data\nThe dataset was created using the proofs included in the Archive of Formal Proofs and the Standard library included in the Isabelle 2021-1 distribution.### Known Limitations\n\nThe data included in this dataset is mostly untyped, meaning that there is little information about the objects referenced in the statement or premise statements. Adding type information would be a valuable contribution."
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|
482420b4997cdfde53692baf2d99a32f9386028d |
Amazon Multilingual Counterfactual Dataset (https://arxiv.org/abs/2104.06893)
The dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary classification. Counterfactual statements describe events that did not or cannot take place. Counterfactual statements may be identified as statements of the form – If p was true, then q would be true (i.e. assertions whose antecedent (p) and consequent (q) are known or assumed to be false).
The key features of this dataset are:
The dataset is multilingual and contains sentences in English, German, and Japanese.
The labeling was done by professional linguists and high quality was ensured.
The dataset is supplemented with the annotation guidelines and definitions, which were worked out by professional linguists. We also provide the clue word lists, which are typical for counterfactual sentences and were used for initial data filtering. The clue word lists were also compiled by professional linguists.
Please see the paper for the data statistics, detailed description of data collection and annotation.
GitHub repo URL: https://github.com/amazon-research/amazon-multilingual-counterfactual-dataset | UKPLab/amazon_counterfactual_en | [
"arxiv:2104.06893",
"region:us"
]
| 2023-02-23T13:32:00+00:00 | {} | 2023-02-23T13:38:57+00:00 | [
"2104.06893"
]
| []
| TAGS
#arxiv-2104.06893 #region-us
|
Amazon Multilingual Counterfactual Dataset (URL
The dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary classification. Counterfactual statements describe events that did not or cannot take place. Counterfactual statements may be identified as statements of the form – If p was true, then q would be true (i.e. assertions whose antecedent (p) and consequent (q) are known or assumed to be false).
The key features of this dataset are:
The dataset is multilingual and contains sentences in English, German, and Japanese.
The labeling was done by professional linguists and high quality was ensured.
The dataset is supplemented with the annotation guidelines and definitions, which were worked out by professional linguists. We also provide the clue word lists, which are typical for counterfactual sentences and were used for initial data filtering. The clue word lists were also compiled by professional linguists.
Please see the paper for the data statistics, detailed description of data collection and annotation.
GitHub repo URL: URL | []
| [
"TAGS\n#arxiv-2104.06893 #region-us \n"
]
| [
15
]
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"passage: TAGS\n#arxiv-2104.06893 #region-us \n"
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|
3dea4fa1803f5cf4d7d69229eb7bd6f9b4ccff27 | # AutoTrain Dataset for project: i-bert-twitter-sentiment
## Dataset Description
This dataset has been automatically processed by AutoTrain for project i-bert-twitter-sentiment.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "Thanks\\u002c Dave! Great show tonight. Sorry\\u002c Craig. I\\u2019ve got to get to bed. I\\u2019ll catch you tomorrow. @user David Letterman",
"target": 2
},
{
"text": "\"I've been watching Gilmore Girls for the past 3 hours. Oops, happy Thursday!\"",
"target": 2
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['negative', 'neutral', 'positive'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 36491 |
| valid | 9124 |
| derek-thomas/autotrain-data-i-bert-twitter-sentiment | [
"task_categories:text-classification",
"region:us"
]
| 2023-02-23T13:32:12+00:00 | {"task_categories": ["text-classification"]} | 2023-02-23T13:35:59+00:00 | []
| []
| TAGS
#task_categories-text-classification #region-us
| AutoTrain Dataset for project: i-bert-twitter-sentiment
=======================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project i-bert-twitter-sentiment.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-text-classification #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
17,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-text-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
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|
a87305906752a5f3f446def63d3e265e62fdd484 | # Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | iotengtr/github-issues | [
"region:us"
]
| 2023-02-23T13:33:04+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", 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"dtype": "null"}, {"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": "null"}, {"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"}, {"name": "handling_time", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 16667996, "num_examples": 5575}], "download_size": 3986763, "dataset_size": 16667996}} | 2023-02-23T13:33:06+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"
]
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|
30ffb4ee109f872844c755424264c2312d4f639c | # Dataset Card for "instruction-summarization"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instruction-summarization | [
"region:us"
]
| 2023-02-23T13:34:20+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 419416243.7369942, "num_examples": 377694}, {"name": "test", "num_bytes": 11038029.364368305, "num_examples": 9940}, {"name": "validation", "num_bytes": 11036918.898637483, "num_examples": 9939}], "download_size": 256885060, "dataset_size": 441491192.0}} | 2023-02-27T16:26:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instruction-summarization"
More Information needed | [
"# Dataset Card for \"instruction-summarization\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instruction-summarization\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instruction-summarization\"\n\nMore Information needed"
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|
cb9f4accacb2ceb0bf8a2ba0ca92b0906b37db76 | This is a version of the Quora Insincere Questions Classification (https://www.kaggle.com/c/quora-insincere-questions-classification) task with a validation split. | UKPLab/insincere-questions | [
"region:us"
]
| 2023-02-23T13:43:00+00:00 | {} | 2023-02-23T13:48:27+00:00 | []
| []
| TAGS
#region-us
| This is a version of the Quora Insincere Questions Classification (URL task with a validation split. | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
524cd750516c48248797cd4181d848e74e62da54 | This is a binary version of the LIAR dataset from https://aclanthology.org/P17-2067/, where the labels have been collapsed into either true or false. | UKPLab/liar | [
"region:us"
]
| 2023-02-23T13:44:34+00:00 | {} | 2023-02-23T13:46:36+00:00 | []
| []
| TAGS
#region-us
| This is a binary version of the LIAR dataset from URL where the labels have been collapsed into either true or false. | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
c92f0f0257ed93b4eb5c53b48fd637ea84db1466 | This is a version of Jigsaw Unintended Bias in Toxicity Classification (https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview) with a train, test, and validation split. | UKPLab/toxic_conversations | [
"region:us"
]
| 2023-02-23T13:52:11+00:00 | {} | 2023-02-23T13:59:13+00:00 | []
| []
| TAGS
#region-us
| This is a version of Jigsaw Unintended Bias in Toxicity Classification (URL with a train, test, and validation split. | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
e4407cabc81b6c3b87c1d65f37878ca36698ad14 | This is a version of Hate Speech Offensive (https://huggingface.co/datasets/hate_speech_offensive) with a train, validation, and test split. https://arxiv.org/abs/1703.04009 | UKPLab/hate_speech_offensive | [
"arxiv:1703.04009",
"region:us"
]
| 2023-02-23T13:53:53+00:00 | {} | 2023-02-23T13:57:03+00:00 | [
"1703.04009"
]
| []
| TAGS
#arxiv-1703.04009 #region-us
| This is a version of Hate Speech Offensive (URL with a train, validation, and test split. URL | []
| [
"TAGS\n#arxiv-1703.04009 #region-us \n"
]
| [
14
]
| [
"passage: TAGS\n#arxiv-1703.04009 #region-us \n"
]
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|
885fba3f4063b68d0d6623658a902390be1f2a15 |
On this repository, I just downloaded and processed the `translate_train.tar.gz` file with `train.jsonl` & `dev.jsonl` here: `https://github.com/ir-nlp-csui/indonli/tree/main/data`
How to use? As simple as this:
```python
!wget https://huggingface.co/datasets/muhammadravi251001/augmented-indo-nli/raw/main/dev_augmented.jsonl
!wget https://huggingface.co/datasets/muhammadravi251001/augmented-indo-nli/resolve/main/train_augmented.jsonl
import pandas as pd
data_train_augmented_indonli = pd.read_json(path_or_buf='train.jsonl', lines=True)
data_dev_augmented_indonli = pd.read_json(path_or_buf='dev.jsonl', lines=True)
```
Voila~!
## Reference
The dataset I used is by IndoNLI.
```
@inproceedings{indonli,
title = "IndoNLI: A Natural Language Inference Dataset for Indonesian",
author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
publisher = "Association for Computational Linguistics",
}
``` | muhammadravi251001/augmented-indo-nli | [
"license:bigscience-openrail-m",
"augmented-indonli",
"region:us"
]
| 2023-02-23T14:57:31+00:00 | {"license": "bigscience-openrail-m", "tags": ["augmented-indonli"], "datasets": ["indonli"]} | 2023-02-23T15:33:50+00:00 | []
| []
| TAGS
#license-bigscience-openrail-m #augmented-indonli #region-us
|
On this repository, I just downloaded and processed the 'translate_train.URL' file with 'URL' & 'URL' here: 'URL
How to use? As simple as this:
Voila~!
## Reference
The dataset I used is by IndoNLI.
| [
"## Reference\n\nThe dataset I used is by IndoNLI."
]
| [
"TAGS\n#license-bigscience-openrail-m #augmented-indonli #region-us \n",
"## Reference\n\nThe dataset I used is by IndoNLI."
]
| [
25,
13
]
| [
"passage: TAGS\n#license-bigscience-openrail-m #augmented-indonli #region-us \n## Reference\n\nThe dataset I used is by IndoNLI."
]
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|
1241b20b58fdbf220fd2e4dbbaa08078bbb844a7 | # Dataset Card for "splits-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lewtun/splits-test | [
"region:us"
]
| 2023-02-23T15:03:48+00:00 | {"dataset_info": {"features": [{"name": "a", "dtype": "int64"}], "splits": [{"name": "foo", "num_bytes": 24, "num_examples": 3}, {"name": "bar", "num_bytes": 24, "num_examples": 3}, {"name": "baz", "num_bytes": 24, "num_examples": 3}], "download_size": 1737, "dataset_size": 72}} | 2023-02-23T15:04:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "splits-test"
More Information needed | [
"# Dataset Card for \"splits-test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"splits-test\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"splits-test\"\n\nMore Information needed"
]
| [
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|
a9a1c92ba233681fbab81a620caab101198b7815 | # Dataset Card for "test_david"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | davidberenstein1957/test_david | [
"region:us"
]
| 2023-02-23T15:12:19+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 837, "num_examples": 11}], "download_size": 2208, "dataset_size": 837}} | 2023-02-23T15:29:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test_david"
More Information needed | [
"# Dataset Card for \"test_david\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test_david\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test_david\"\n\nMore Information needed"
]
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|
e4be15ff6850cac95ed26d83a32f7a35cfcee325 | # Dataset Card for "miniwob_snippet_improved"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LucasThil/miniwob_snippet_improved | [
"region:us"
]
| 2023-02-23T15:26:28+00:00 | {"dataset_info": {"features": [{"name": "episodes", "dtype": "string"}, {"name": "refs", "dtype": "int64"}, {"name": "click", "dtype": "int64"}, {"name": "dblclick", "dtype": "int64"}, {"name": "keydown", "dtype": "int64"}, {"name": "keypress", "dtype": "int64"}, {"name": "keyup", "dtype": "int64"}, {"name": "mousedown", "dtype": "int64"}, {"name": "mouseup", "dtype": "int64"}, {"name": "scroll", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 602371925, "num_examples": 464060}, {"name": "test", "num_bytes": 75459160, "num_examples": 58068}, {"name": "validate", "num_bytes": 75560379, "num_examples": 57976}], "download_size": 121921413, "dataset_size": 753391464}} | 2023-02-23T15:27:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "miniwob_snippet_improved"
More Information needed | [
"# Dataset Card for \"miniwob_snippet_improved\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"miniwob_snippet_improved\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"miniwob_snippet_improved\"\n\nMore Information needed"
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|
768b9edaaf433c00ae7d1a32abc09f95b563f1f8 | # Dataset Card for "NN"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Shirali/NN | [
"region:us"
]
| 2023-02-23T16:31:12+00:00 | {"dataset_info": {"features": [{"name": "path", "dtype": "string"}, {"name": "audio", "dtype": "audio"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2047707247.448, "num_examples": 9341}], "download_size": 2229162573, "dataset_size": 2047707247.448}} | 2023-02-23T16:34:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "NN"
More Information needed | [
"# Dataset Card for \"NN\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"NN\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"NN\"\n\nMore Information needed"
]
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4e41c4d02c0e0bb9abceffcc286e630815836f24 |
## Source:
Copied from the [original dataset](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic))
### Creators:
1. Dr. William H. Wolberg, General Surgery Dept.
University of Wisconsin, Clinical Sciences Center
Madison, WI 53792
wolberg '@' eagle.surgery.wisc.edu
2. W. Nick Street, Computer Sciences Dept.
University of Wisconsin, 1210 West Dayton St., Madison, WI 53706
street '@' cs.wisc.edu 608-262-6619
3. Olvi L. Mangasarian, Computer Sciences Dept.
University of Wisconsin, 1210 West Dayton St., Madison, WI 53706
olvi '@' cs.wisc.edu
### Donor:
Nick Street
## Data Set Information:
Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at [Web Link]
Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.
The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].
This database is also available through the UW CS ftp server:
ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WDBC/
### Attribute Information:
1) ID number
2) Diagnosis (M = malignant, B = benign)
3-32)
Ten real-valued features are computed for each cell nucleus:
a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension ("coastline approximation" - 1) | wwydmanski/wisconsin-breast-cancer | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"tabular",
"breast-cancer",
"region:us"
]
| 2023-02-23T16:54:47+00:00 | {"size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "WisconsinBreastCancerDiagnostic", "tags": ["tabular", "breast-cancer"]} | 2023-02-23T19:11:33+00:00 | []
| []
| TAGS
#task_categories-tabular-classification #size_categories-n<1K #tabular #breast-cancer #region-us
|
## Source:
Copied from the original dataset)
### Creators:
1. Dr. William H. Wolberg, General Surgery Dept.
University of Wisconsin, Clinical Sciences Center
Madison, WI 53792
wolberg '@' URL
2. W. Nick Street, Computer Sciences Dept.
University of Wisconsin, 1210 West Dayton St., Madison, WI 53706
street '@' URL 608-262-6619
3. Olvi L. Mangasarian, Computer Sciences Dept.
University of Wisconsin, 1210 West Dayton St., Madison, WI 53706
olvi '@' URL
### Donor:
Nick Street
## Data Set Information:
Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at [Web Link]
Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.
The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].
This database is also available through the UW CS ftp server:
ftp URL
cd math-prog/cpo-dataset/machine-learn/WDBC/
### Attribute Information:
1) ID number
2) Diagnosis (M = malignant, B = benign)
3-32)
Ten real-valued features are computed for each cell nucleus:
a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension ("coastline approximation" - 1) | [
"## Source:\nCopied from the original dataset)",
"### Creators:\n\n1. Dr. William H. Wolberg, General Surgery Dept.\nUniversity of Wisconsin, Clinical Sciences Center\nMadison, WI 53792\nwolberg '@' URL\n\n2. W. Nick Street, Computer Sciences Dept.\nUniversity of Wisconsin, 1210 West Dayton St., Madison, WI 53706\nstreet '@' URL 608-262-6619\n\n3. Olvi L. Mangasarian, Computer Sciences Dept.\nUniversity of Wisconsin, 1210 West Dayton St., Madison, WI 53706\nolvi '@' URL",
"### Donor:\n\nNick Street",
"## Data Set Information:\n\nFeatures are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at [Web Link]\n\nSeparating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree Construction Via Linear Programming.\" Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.\n\nThe actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: \"Robust Linear Programming Discrimination of Two Linearly Inseparable Sets\", Optimization Methods and Software 1, 1992, 23-34].\n\nThis database is also available through the UW CS ftp server:\nftp URL\ncd math-prog/cpo-dataset/machine-learn/WDBC/",
"### Attribute Information:\n\n1) ID number\n2) Diagnosis (M = malignant, B = benign)\n3-32)\n\nTen real-valued features are computed for each cell nucleus:\n\na) radius (mean of distances from center to points on the perimeter)\n\nb) texture (standard deviation of gray-scale values)\n\nc) perimeter\n\nd) area\n\ne) smoothness (local variation in radius lengths)\n\nf) compactness (perimeter^2 / area - 1.0)\n\ng) concavity (severity of concave portions of the contour)\n\nh) concave points (number of concave portions of the contour)\n\ni) symmetry\n\nj) fractal dimension (\"coastline approximation\" - 1)"
]
| [
"TAGS\n#task_categories-tabular-classification #size_categories-n<1K #tabular #breast-cancer #region-us \n",
"## Source:\nCopied from the original dataset)",
"### Creators:\n\n1. Dr. William H. Wolberg, General Surgery Dept.\nUniversity of Wisconsin, Clinical Sciences Center\nMadison, WI 53792\nwolberg '@' URL\n\n2. W. Nick Street, Computer Sciences Dept.\nUniversity of Wisconsin, 1210 West Dayton St., Madison, WI 53706\nstreet '@' URL 608-262-6619\n\n3. Olvi L. Mangasarian, Computer Sciences Dept.\nUniversity of Wisconsin, 1210 West Dayton St., Madison, WI 53706\nolvi '@' URL",
"### Donor:\n\nNick Street",
"## Data Set Information:\n\nFeatures are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at [Web Link]\n\nSeparating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree Construction Via Linear Programming.\" Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.\n\nThe actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: \"Robust Linear Programming Discrimination of Two Linearly Inseparable Sets\", Optimization Methods and Software 1, 1992, 23-34].\n\nThis database is also available through the UW CS ftp server:\nftp URL\ncd math-prog/cpo-dataset/machine-learn/WDBC/",
"### Attribute Information:\n\n1) ID number\n2) Diagnosis (M = malignant, B = benign)\n3-32)\n\nTen real-valued features are computed for each cell nucleus:\n\na) radius (mean of distances from center to points on the perimeter)\n\nb) texture (standard deviation of gray-scale values)\n\nc) perimeter\n\nd) area\n\ne) smoothness (local variation in radius lengths)\n\nf) compactness (perimeter^2 / area - 1.0)\n\ng) concavity (severity of concave portions of the contour)\n\nh) concave points (number of concave portions of the contour)\n\ni) symmetry\n\nj) fractal dimension (\"coastline approximation\" - 1)"
]
| [
36,
11,
121,
7,
283,
174
]
| [
"passage: TAGS\n#task_categories-tabular-classification #size_categories-n<1K #tabular #breast-cancer #region-us \n## Source:\nCopied from the original dataset)### Creators:\n\n1. Dr. William H. Wolberg, General Surgery Dept.\nUniversity of Wisconsin, Clinical Sciences Center\nMadison, WI 53792\nwolberg '@' URL\n\n2. W. Nick Street, Computer Sciences Dept.\nUniversity of Wisconsin, 1210 West Dayton St., Madison, WI 53706\nstreet '@' URL 608-262-6619\n\n3. Olvi L. Mangasarian, Computer Sciences Dept.\nUniversity of Wisconsin, 1210 West Dayton St., Madison, WI 53706\nolvi '@' URL### Donor:\n\nNick Street## Data Set Information:\n\nFeatures are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at [Web Link]\n\nSeparating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree Construction Via Linear Programming.\" Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.\n\nThe actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: \"Robust Linear Programming Discrimination of Two Linearly Inseparable Sets\", Optimization Methods and Software 1, 1992, 23-34].\n\nThis database is also available through the UW CS ftp server:\nftp URL\ncd math-prog/cpo-dataset/machine-learn/WDBC/"
]
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|
ee6149db344e6f8bbaeae6dc6254ca6ef8b2c361 | # Dataset Card for "climate-tagging-labelled-datasets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | maayansharon/climate-tagging-labelled-datasets | [
"region:us"
]
| 2023-02-23T17:24:18+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 88730, "num_examples": 160}, {"name": "test", "num_bytes": 24841, "num_examples": 40}], "download_size": 78600, "dataset_size": 113571}} | 2023-02-23T17:24:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "climate-tagging-labelled-datasets"
More Information needed | [
"# Dataset Card for \"climate-tagging-labelled-datasets\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"climate-tagging-labelled-datasets\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"climate-tagging-labelled-datasets\"\n\nMore Information needed"
]
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|
047f604f5847abbb3b2f7fd0204eabd8257a883f | # Dataset Card for "spacy_sm_wnut17"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | argilla/spacy_sm_wnut17 | [
"region:us"
]
| 2023-02-23T17:36:31+00:00 | {"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-CARDINAL", "2": "I-CARDINAL", "3": "B-DATE", "4": "I-DATE", "5": "B-EVENT", "6": "I-EVENT", "7": "B-FAC", "8": "I-FAC", "9": "B-GPE", "10": "I-GPE", "11": "B-LAW", "12": "I-LAW", "13": "B-LOC", "14": "I-LOC", "15": "B-MONEY", "16": "I-MONEY", "17": "B-NORP", "18": "I-NORP", "19": "B-ORDINAL", "20": "I-ORDINAL", "21": "B-ORG", "22": "I-ORG", "23": "B-PERCENT", "24": "I-PERCENT", "25": "B-PERSON", "26": "I-PERSON", "27": "B-QUANTITY", "28": "I-QUANTITY", "29": "B-TIME", "30": "I-TIME", "31": "B-WORK_OF_ART", "32": "I-WORK_OF_ART"}}}}], "splits": [{"name": "train", "num_bytes": 39558.31543624161, "num_examples": 119}, {"name": "test", "num_bytes": 9972.68456375839, "num_examples": 30}], "download_size": 19265, "dataset_size": 49531.0}} | 2023-02-23T17:36:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "spacy_sm_wnut17"
More Information needed | [
"# Dataset Card for \"spacy_sm_wnut17\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"spacy_sm_wnut17\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"spacy_sm_wnut17\"\n\nMore Information needed"
]
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|
bf9eec207741a99de46c2c58603558c75c4ab396 | # Dataset Card for "ray-summit-classy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | argilla/ray-summit-classy | [
"region:us"
]
| 2023-02-23T17:37:47+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "BUSINESS", "1": "SCI/TECH", "2": "SPORTS", "3": "WORLD"}}}}], "splits": [{"name": "train", "num_bytes": 111748.62132352941, "num_examples": 435}, {"name": "test", "num_bytes": 28001.378676470587, "num_examples": 109}], "download_size": 97886, "dataset_size": 139750.0}} | 2023-02-23T17:38:00+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ray-summit-classy"
More Information needed | [
"# Dataset Card for \"ray-summit-classy\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ray-summit-classy\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ray-summit-classy\"\n\nMore Information needed"
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|
372d0e99a9f0d5428b197044fb08eb2b7fb042a1 | # Dataset Card for "french-snli"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pvisnrt/french-snli | [
"region:us"
]
| 2023-02-23T18:04:07+00:00 | {"dataset_info": {"features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}, {"name": "translated_premise", "dtype": "string"}, {"name": "translated_hypothesis", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 2291311, "num_examples": 10000}, {"name": "train", "num_bytes": 122397311, "num_examples": 550152}, {"name": "validation", "num_bytes": 2301319, "num_examples": 10000}], "download_size": 40410905, "dataset_size": 126989941}} | 2023-02-23T18:04:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "french-snli"
More Information needed | [
"# Dataset Card for \"french-snli\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"french-snli\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"french-snli\"\n\nMore Information needed"
]
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|
eb60171b0a06d6a8432728c705438f884d4ed447 | # Dataset Card for "miniwob_snippets_refs_onehot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LucasThil/miniwob_snippets_refs_onehot | [
"region:us"
]
| 2023-02-23T18:10:25+00:00 | {"dataset_info": {"features": [{"name": "episodes", "dtype": "string"}, {"name": "refs", "dtype": "int64"}, {"name": "click", "dtype": "int64"}, {"name": "dblclick", "dtype": "int64"}, {"name": "keydown", "dtype": "int64"}, {"name": "keypress", "dtype": "int64"}, {"name": "keyup", "dtype": "int64"}, {"name": "mousedown", "dtype": "int64"}, {"name": "mouseup", "dtype": "int64"}, {"name": "scroll", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", 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148857112, "num_examples": 58068}, {"name": "validate", "num_bytes": 148842043, "num_examples": 57976}], "download_size": 150978553, "dataset_size": 1486642920}} | 2023-02-23T18:12:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "miniwob_snippets_refs_onehot"
More Information needed | [
"# Dataset Card for \"miniwob_snippets_refs_onehot\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"miniwob_snippets_refs_onehot\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"miniwob_snippets_refs_onehot\"\n\nMore Information needed"
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67c1b091d6c785e9a12fef6cef5cbfdde49ad409 | # Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | Ezi/test_PII | [
"task_categories:table-question-answering",
"size_categories:1K<n<10K",
"license:openrail",
"region:us"
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| 2023-02-23T19:46:09+00:00 | {"license": "openrail", "size_categories": ["1K<n<10K"], "task_categories": ["table-question-answering"], "dataset": ["bookcorpus"]} | 2023-03-02T02:51:34+00:00 | []
| []
| TAGS
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| # Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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|
2bb7c12af3651122feeaa238b54618621a194bde | This dataset contains 14 novels written by H G Wells. Each novel has been divided into 20 parts, so every 20 lines contain one novel. | MinzaKhan/HGWells | [
"region:us"
]
| 2023-02-23T20:06:54+00:00 | {} | 2023-02-23T20:48:07+00:00 | []
| []
| TAGS
#region-us
| This dataset contains 14 novels written by H G Wells. Each novel has been divided into 20 parts, so every 20 lines contain one novel. | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
e0f9a7711275dab655f5d9bee0e6a08f92d60c0e |
# Dataset Card for WikiTableQuestions-wtq
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [WikiTableQuestions homepage](https://nlp.stanford.edu/software/sempre/wikitable)
- **Repository:** [WikiTableQuestions repository](https://github.com/ppasupat/WikiTableQuestions)
- **Paper:** [Compositional Semantic Parsing on Semi-Structured Tables](https://arxiv.org/abs/1508.00305)
- **Leaderboard:** [WikiTableQuestions leaderboard on PaperWithCode](https://paperswithcode.com/dataset/wikitablequestions)
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The WikiTableQuestions-wtq dataset is a small-scale dataset for the task of question answering on semi-structured tables.
This data includes the `aggregation_label` and `answer_coordinates` to make it easy to train this model on any [TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas#usage-finetuning) based modles.
### Supported Tasks and Leaderboards
question-answering, table-question-answering
### Languages
en
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 27.91 MB
- **Size of the generated dataset:** 45.68 MB
- **Total amount of disk used:** 73.60 MB
An example of 'validation' looks as follows:
```
{
"id": "nt-0",
"question": "What is the total average attendance at all USL First Division matches?",
"answers": [
"36755"
],
"table": {
"header": [
"Year",
"Division",
"League",
"Regular Season",
"Playoffs",
"Open Cup",
"Avg. Attendance"
],
"rows": [
[
"2001",
"2",
"USL A-League",
"4th, Western",
"Quarterfinals",
"Did not qualify",
"7,169"
],
...
],
"name": "csv/204-csv/590.tsv"
},
"aggregation_label": "SUM",
"answer_coordinates": [
[
4,
6
],
...
]
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `id`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a `list` of `string` feature.
- `answers_coordinates`: a `list` of `int,int` tuples.
- `aggregation_label`: a `string` feature.
- `table`: a dictionary feature containing:
- `header`: a `list` of `string` features.
- `rows`: a `list` of `list` of `string` features:
- `name`: a `string` feature.
### Data Splits
TBA
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Panupong Pasupat and Percy Liang
### Licensing Information
Creative Commons Attribution Share Alike 4.0 International
### Citation Information
```
@inproceedings{pasupat-liang-2015-compositional,
title = "Compositional Semantic Parsing on Semi-Structured Tables",
author = "Pasupat, Panupong and Liang, Percy",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = jul,
year = "2015",
address = "Beijing, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P15-1142",
doi = "10.3115/v1/P15-1142",
pages = "1470--1480",
}
```
### Contributions
Thanks to [@SivilTaram](https://github.com/SivilTaram) for adding this dataset. | danwakeem/wikitablequestions-wtq | [
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:wikitablequestions",
"language:en",
"license:cc-by-4.0",
"table-question-answering",
"arxiv:1508.00305",
"region:us"
]
| 2023-02-23T20:32:31+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["wikitablequestions"], "task_categories": ["question-answering"], "task_ids": [], "pretty_name": "WikiTableQuestions-wtq", "tags": ["table-question-answering"]} | 2023-02-23T22:56:30+00:00 | [
"1508.00305"
]
| [
"en"
]
| TAGS
#task_categories-question-answering #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-wikitablequestions #language-English #license-cc-by-4.0 #table-question-answering #arxiv-1508.00305 #region-us
|
# Dataset Card for WikiTableQuestions-wtq
## Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
## Dataset Description
- Homepage: WikiTableQuestions homepage
- Repository: WikiTableQuestions repository
- Paper: Compositional Semantic Parsing on Semi-Structured Tables
- Leaderboard: WikiTableQuestions leaderboard on PaperWithCode
- Point of Contact:
### Dataset Summary
The WikiTableQuestions-wtq dataset is a small-scale dataset for the task of question answering on semi-structured tables.
This data includes the 'aggregation_label' and 'answer_coordinates' to make it easy to train this model on any TAPAS based modles.
### Supported Tasks and Leaderboards
question-answering, table-question-answering
### Languages
en
## Dataset Structure
### Data Instances
#### default
- Size of downloaded dataset files: 27.91 MB
- Size of the generated dataset: 45.68 MB
- Total amount of disk used: 73.60 MB
An example of 'validation' looks as follows:
### Data Fields
The data fields are the same among all splits.
#### default
- 'id': a 'string' feature.
- 'question': a 'string' feature.
- 'answers': a 'list' of 'string' feature.
- 'answers_coordinates': a 'list' of 'int,int' tuples.
- 'aggregation_label': a 'string' feature.
- 'table': a dictionary feature containing:
- 'header': a 'list' of 'string' features.
- 'rows': a 'list' of 'list' of 'string' features:
- 'name': a 'string' feature.
### Data Splits
TBA
## 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
Panupong Pasupat and Percy Liang
### Licensing Information
Creative Commons Attribution Share Alike 4.0 International
### Contributions
Thanks to @SivilTaram for adding this dataset. | [
"# Dataset Card for WikiTableQuestions-wtq",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information",
"## Dataset Description\n\n- Homepage: WikiTableQuestions homepage\n- Repository: WikiTableQuestions repository\n- Paper: Compositional Semantic Parsing on Semi-Structured Tables\n- Leaderboard: WikiTableQuestions leaderboard on PaperWithCode\n- Point of Contact:",
"### Dataset Summary\n\nThe WikiTableQuestions-wtq dataset is a small-scale dataset for the task of question answering on semi-structured tables.\n\nThis data includes the 'aggregation_label' and 'answer_coordinates' to make it easy to train this model on any TAPAS based modles.",
"### Supported Tasks and Leaderboards\n\nquestion-answering, table-question-answering",
"### Languages\n\nen",
"## Dataset Structure",
"### Data Instances",
"#### default\n\n- Size of downloaded dataset files: 27.91 MB\n- Size of the generated dataset: 45.68 MB\n- Total amount of disk used: 73.60 MB\n\nAn example of 'validation' looks as follows:",
"### Data Fields\n\nThe data fields are the same among all splits.",
"#### default\n- 'id': a 'string' feature.\n- 'question': a 'string' feature.\n- 'answers': a 'list' of 'string' feature.\n- 'answers_coordinates': a 'list' of 'int,int' tuples.\n- 'aggregation_label': a 'string' feature.\n- 'table': a dictionary feature containing:\n - 'header': a 'list' of 'string' features.\n - 'rows': a 'list' of 'list' of 'string' features:\n - 'name': a 'string' feature.",
"### Data Splits\n\nTBA",
"## 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\n\nPanupong Pasupat and Percy Liang",
"### Licensing Information\n\nCreative Commons Attribution Share Alike 4.0 International",
"### Contributions\n\nThanks to @SivilTaram for adding this dataset."
]
| [
"TAGS\n#task_categories-question-answering #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-wikitablequestions #language-English #license-cc-by-4.0 #table-question-answering #arxiv-1508.00305 #region-us \n",
"# Dataset Card for WikiTableQuestions-wtq",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information",
"## Dataset Description\n\n- Homepage: WikiTableQuestions homepage\n- Repository: WikiTableQuestions repository\n- Paper: Compositional Semantic Parsing on Semi-Structured Tables\n- Leaderboard: WikiTableQuestions leaderboard on PaperWithCode\n- Point of Contact:",
"### Dataset Summary\n\nThe WikiTableQuestions-wtq dataset is a small-scale dataset for the task of question answering on semi-structured tables.\n\nThis data includes the 'aggregation_label' and 'answer_coordinates' to make it easy to train this model on any TAPAS based modles.",
"### Supported Tasks and Leaderboards\n\nquestion-answering, table-question-answering",
"### Languages\n\nen",
"## Dataset Structure",
"### Data Instances",
"#### default\n\n- Size of downloaded dataset files: 27.91 MB\n- Size of the generated dataset: 45.68 MB\n- Total amount of disk used: 73.60 MB\n\nAn example of 'validation' looks as follows:",
"### Data Fields\n\nThe data fields are the same among all splits.",
"#### default\n- 'id': a 'string' feature.\n- 'question': a 'string' feature.\n- 'answers': a 'list' of 'string' feature.\n- 'answers_coordinates': a 'list' of 'int,int' tuples.\n- 'aggregation_label': a 'string' feature.\n- 'table': a dictionary feature containing:\n - 'header': a 'list' of 'string' features.\n - 'rows': a 'list' of 'list' of 'string' features:\n - 'name': a 'string' feature.",
"### Data Splits\n\nTBA",
"## 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\n\nPanupong Pasupat and Percy Liang",
"### Licensing Information\n\nCreative Commons Attribution Share Alike 4.0 International",
"### Contributions\n\nThanks to @SivilTaram for adding this dataset."
]
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| [
"passage: TAGS\n#task_categories-question-answering #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-wikitablequestions #language-English #license-cc-by-4.0 #table-question-answering #arxiv-1508.00305 #region-us \n# Dataset Card for WikiTableQuestions-wtq## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information## Dataset Description\n\n- Homepage: WikiTableQuestions homepage\n- Repository: WikiTableQuestions repository\n- Paper: Compositional Semantic Parsing on Semi-Structured Tables\n- Leaderboard: WikiTableQuestions leaderboard on PaperWithCode\n- Point of Contact:### Dataset Summary\n\nThe WikiTableQuestions-wtq dataset is a small-scale dataset for the task of question answering on semi-structured tables.\n\nThis data includes the 'aggregation_label' and 'answer_coordinates' to make it easy to train this model on any TAPAS based modles.### Supported Tasks and Leaderboards\n\nquestion-answering, table-question-answering### Languages\n\nen## Dataset Structure### Data Instances#### default\n\n- Size of downloaded dataset files: 27.91 MB\n- Size of the generated dataset: 45.68 MB\n- Total amount of disk used: 73.60 MB\n\nAn example of 'validation' looks as follows:### Data Fields\n\nThe data fields are the same among all splits."
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|
ae9db45de4177e42a6bc45623efe1ace9ee5db08 | # Dataset Card for "VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_10_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_10_open_ended | [
"region:us"
]
| 2023-02-23T20:43:02+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_32", "num_bytes": 1769, "num_examples": 10}], "download_size": 0, "dataset_size": 1769}} | 2023-02-23T20:50:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_10_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_10_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_10_open_ended\"\n\nMore Information needed"
]
| [
6,
50
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_validation_facebook_opt_2.7b_mode_VQAv2_visclues_detection_ns_10_open_ended\"\n\nMore Information needed"
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|
64e6518c84f7f95da5efbf4bbc810e9ee7a213ea | # Dataset Card for "DA_Restaurants"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/DA_Restaurants | [
"region:us"
]
| 2023-02-23T20:54:42+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}, {"name": "generated", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1064689, "num_examples": 3588}], "download_size": 452653, "dataset_size": 1064689}} | 2023-03-29T23:21:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DA_Restaurants"
More Information needed | [
"# Dataset Card for \"DA_Restaurants\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DA_Restaurants\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DA_Restaurants\"\n\nMore Information needed"
]
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|
afa0ebca8819168ea0372a0eb7fe76fe44b95654 | # Dataset Card for "synthetic_compassion"
Synthetic compassion is a dataset consisting of generated compassionate speech for meditation and psychology advice.
The dataset consists of unique voices as each audio file has its own synthetic voice.
The speech is generated with Tortoise TTS
## How to use
Here is how you can load the dataset
```python
from datasets import load_dataset
dataset = load_dataset("birgermoell/synthetic_compassion")
``` | birgermoell/synthetic_compassion | [
"size_categories:n<1K",
"language:en",
"license:mit",
"region:us"
]
| 2023-02-23T21:12:40+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["n<1K"], "pretty_name": "Synthetic Compassion", "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 117098145, "num_examples": 439}], "download_size": 111238398, "dataset_size": 117098145}} | 2023-02-23T22:24:24+00:00 | []
| [
"en"
]
| TAGS
#size_categories-n<1K #language-English #license-mit #region-us
| # Dataset Card for "synthetic_compassion"
Synthetic compassion is a dataset consisting of generated compassionate speech for meditation and psychology advice.
The dataset consists of unique voices as each audio file has its own synthetic voice.
The speech is generated with Tortoise TTS
## How to use
Here is how you can load the dataset
| [
"# Dataset Card for \"synthetic_compassion\"\nSynthetic compassion is a dataset consisting of generated compassionate speech for meditation and psychology advice. \nThe dataset consists of unique voices as each audio file has its own synthetic voice.\nThe speech is generated with Tortoise TTS",
"## How to use\nHere is how you can load the dataset"
]
| [
"TAGS\n#size_categories-n<1K #language-English #license-mit #region-us \n",
"# Dataset Card for \"synthetic_compassion\"\nSynthetic compassion is a dataset consisting of generated compassionate speech for meditation and psychology advice. \nThe dataset consists of unique voices as each audio file has its own synthetic voice.\nThe speech is generated with Tortoise TTS",
"## How to use\nHere is how you can load the dataset"
]
| [
25,
70,
13
]
| [
"passage: TAGS\n#size_categories-n<1K #language-English #license-mit #region-us \n# Dataset Card for \"synthetic_compassion\"\nSynthetic compassion is a dataset consisting of generated compassionate speech for meditation and psychology advice. \nThe dataset consists of unique voices as each audio file has its own synthetic voice.\nThe speech is generated with Tortoise TTS## How to use\nHere is how you can load the dataset"
]
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|
04e8dfd6da45d4f2d47b290879549fa4d34e36b3 | # Dataset Card for "VQAv2_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_100_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_100_open_ended | [
"region:us"
]
| 2023-02-23T21:27:22+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 13464, "num_examples": 100}], "download_size": 7220, "dataset_size": 13464}} | 2023-02-23T21:27:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_100_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_100_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_100_open_ended\"\n\nMore Information needed"
]
| [
6,
51
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_100_open_ended\"\n\nMore Information needed"
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d8d108b9ba052dceaead2e8c222a63e64ded0f44 | # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_100_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_100_open_ended | [
"region:us"
]
| 2023-02-23T21:33:00+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 14212, "num_examples": 100}], "download_size": 8402, "dataset_size": 14212}} | 2023-02-24T17:10:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_100_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_100_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_100_open_ended\"\n\nMore Information needed"
]
| [
6,
54
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_100_open_ended\"\n\nMore Information needed"
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3f94cedbbb11582e5d54db60af8161f0facaf042 | # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_1000_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_1000_open_ended | [
"region:us"
]
| 2023-02-23T22:07:34+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 144834, "num_examples": 1000}], "download_size": 0, "dataset_size": 144834}} | 2023-02-28T17:21:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_1000_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_1000_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_1000_open_ended\"\n\nMore Information needed"
]
| [
6,
54
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_1000_open_ended\"\n\nMore Information needed"
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|
ad5834bae7c40f470123440b25d41384eeb3ca5b | # Dataset Card for "VQAv2_sample_validation_facebook_opt_13b_mode_VQAv2_visclues_detection_ns_1000_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_13b_mode_VQAv2_visclues_detection_ns_1000_open_ended | [
"region:us"
]
| 2023-02-23T22:16:14+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_32", "num_bytes": 149581, "num_examples": 1000}], "download_size": 58110, "dataset_size": 149581}} | 2023-02-23T22:16:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_13b_mode_VQAv2_visclues_detection_ns_1000_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_13b_mode_VQAv2_visclues_detection_ns_1000_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_13b_mode_VQAv2_visclues_detection_ns_1000_open_ended\"\n\nMore Information needed"
]
| [
6,
52
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_13b_mode_VQAv2_visclues_detection_ns_1000_open_ended\"\n\nMore Information needed"
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faffbf5306cd4a3fc251d0d858fea2073cf7b6de | # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_1000_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_1000_open_ended | [
"region:us"
]
| 2023-02-23T22:22:58+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_32", "num_bytes": 143135, "num_examples": 1000}], "download_size": 54496, "dataset_size": 143135}} | 2023-02-23T22:23:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_1000_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_1000_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_1000_open_ended\"\n\nMore Information needed"
]
| [
6,
52
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_mode_VQAv2_visclues_detection_ns_1000_open_ended\"\n\nMore Information needed"
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|
0dd7b72be381c976fb484de2cee709885612d387 | # Dataset Card for "synthetic_compassion_wav"
Synthetic compassion is a dataset consisting of generated compassionate speech for meditation and psychology advice.
The dataset consists of unique voices as each audio file has its own synthetic voice.
The speech is generated with Tortoise TTS
## How to use
Here is how you can load the dataset
```python
from datasets import load_dataset
dataset = load_dataset("birgermoell/synthetic_compassion_wav")
``` | birgermoell/synthetic_compassion_wav | [
"doi:10.57967/hf/0401",
"region:us"
]
| 2023-02-23T22:42:23+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 308051793.096, "num_examples": 1722}], "download_size": 302072835, "dataset_size": 308051793.096}} | 2023-02-24T23:00:28+00:00 | []
| []
| TAGS
#doi-10.57967/hf/0401 #region-us
| # Dataset Card for "synthetic_compassion_wav"
Synthetic compassion is a dataset consisting of generated compassionate speech for meditation and psychology advice.
The dataset consists of unique voices as each audio file has its own synthetic voice.
The speech is generated with Tortoise TTS
## How to use
Here is how you can load the dataset
| [
"# Dataset Card for \"synthetic_compassion_wav\"\nSynthetic compassion is a dataset consisting of generated compassionate speech for meditation and psychology advice. \nThe dataset consists of unique voices as each audio file has its own synthetic voice.\nThe speech is generated with Tortoise TTS",
"## How to use\nHere is how you can load the dataset"
]
| [
"TAGS\n#doi-10.57967/hf/0401 #region-us \n",
"# Dataset Card for \"synthetic_compassion_wav\"\nSynthetic compassion is a dataset consisting of generated compassionate speech for meditation and psychology advice. \nThe dataset consists of unique voices as each audio file has its own synthetic voice.\nThe speech is generated with Tortoise TTS",
"## How to use\nHere is how you can load the dataset"
]
| [
18,
73,
13
]
| [
"passage: TAGS\n#doi-10.57967/hf/0401 #region-us \n# Dataset Card for \"synthetic_compassion_wav\"\nSynthetic compassion is a dataset consisting of generated compassionate speech for meditation and psychology advice. \nThe dataset consists of unique voices as each audio file has its own synthetic voice.\nThe speech is generated with Tortoise TTS## How to use\nHere is how you can load the dataset"
]
| [
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76cc4262a1b1c919aa67404859ee3936dc8c7388 | # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_ns_100_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_ns_100_open_ended | [
"region:us"
]
| 2023-02-23T23:09:11+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 14044, "num_examples": 100}], "download_size": 0, "dataset_size": 14044}} | 2023-02-24T19:00:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_ns_100_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_ns_100_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_ns_100_open_ended\"\n\nMore Information needed"
]
| [
6,
51
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_ns_100_open_ended\"\n\nMore Information needed"
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|
4016c35590fad3c395c9ed919d0560b9d9faaf6f | # Dataset Card for "NER_VIS_5076"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Yamei/NER_VISBank | [
"region:us"
]
| 2023-02-23T23:14:22+00:00 | {"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "B_DATA", "1": "I_DATA", "2": "B_APPLICATION", "3": "I_APPLICATION", "4": "B_METHOD", "5": "I_METHOD", "6": "B_VISUALIZATION", "7": "I_VISUALIZATION", "8": "B_EVALUATION", "9": "I_EVALUATION", "10": "O"}}}}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1627524.584712372, "num_examples": 4568}, {"name": "test", "num_bytes": 90497.20764381402, "num_examples": 254}, {"name": "valid", "num_bytes": 90497.20764381402, "num_examples": 254}], "download_size": 397990, "dataset_size": 1808518.9999999998}} | 2023-02-23T23:15:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "NER_VIS_5076"
More Information needed | [
"# Dataset Card for \"NER_VIS_5076\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"NER_VIS_5076\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"NER_VIS_5076\"\n\nMore Information needed"
]
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|
1cd93ee2b47e41bde02d484ff810d2bf5a1fc870 | # Dataset Card for "clevr-with-depth-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | erkam/clevr-with-depth-full | [
"region:us"
]
| 2023-02-23T23:56:34+00:00 | {"dataset_info": {"features": [{"name": "target_img", "dtype": "image"}, {"name": "source_img", "dtype": "image"}, {"name": "target_obj", "sequence": "int64"}, {"name": "source_obj", "sequence": "int64"}, {"name": "target_box", "sequence": {"sequence": "float32"}}, {"name": "source_box", "sequence": {"sequence": "float32"}}, {"name": "target_depth", "dtype": "image"}, {"name": "source_depth", "dtype": "image"}, {"name": "target_tri", "sequence": {"sequence": "int64"}}, {"name": "source_tri", "sequence": {"sequence": "int64"}}, {"name": "pos_prompt", "dtype": "string"}, {"name": "neg_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 231136332.0, "num_examples": 1400}, {"name": "test", "num_bytes": 49635336.0, "num_examples": 300}, {"name": "val", "num_bytes": 49611825.0, "num_examples": 300}], "download_size": 328113230, "dataset_size": 330383493.0}} | 2023-02-23T23:56:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "clevr-with-depth-full"
More Information needed | [
"# Dataset Card for \"clevr-with-depth-full\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"clevr-with-depth-full\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"clevr-with-depth-full\"\n\nMore Information needed"
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9340cdf28dcda9aae43cd991b893ba08c839e835 | # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_ns_1000_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_ns_1000_open_ended | [
"region:us"
]
| 2023-02-24T00:11:38+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 143654, "num_examples": 1000}], "download_size": 0, "dataset_size": 143654}} | 2023-02-28T17:16:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_ns_1000_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_ns_1000_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_ns_1000_open_ended\"\n\nMore Information needed"
]
| [
6,
51
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_ns_1000_open_ended\"\n\nMore Information needed"
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|
6d1c640c346a179f744d4629aaf10c9e4d85e9cd |
# Dataset Card for Neon Isometric
Do i know what i'm doing? NO. XD So if anyone wants to help with this go for gold
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | EarthnDusk/NeonIsometric | [
"task_categories:text-to-image",
"size_categories:n<1K",
"language:en",
"license:creativeml-openrail-m",
"region:us"
]
| 2023-02-24T00:31:20+00:00 | {"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "pretty_name": "Neon Isometric"} | 2023-02-25T09:20:39+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-to-image #size_categories-n<1K #language-English #license-creativeml-openrail-m #region-us
|
# Dataset Card for Neon Isometric
Do i know what i'm doing? NO. XD So if anyone wants to help with this go for gold
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# Dataset Card for Neon Isometric\n\n Do i know what i'm doing? NO. XD So if anyone wants to help with this go for gold",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
| [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #language-English #license-creativeml-openrail-m #region-us \n",
"# Dataset Card for Neon Isometric\n\n Do i know what i'm doing? NO. XD So if anyone wants to help with this go for gold",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
| [
44,
32,
24,
32,
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4,
10,
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| [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #language-English #license-creativeml-openrail-m #region-us \n# Dataset Card for Neon Isometric\n\n Do i know what i'm doing? NO. XD So if anyone wants to help with this go for gold## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
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|
079d310163b36c9a72fe327d905cf21a21b8d5a6 |
Constraint@AAAI2021 - COVID19 Fake News Detection in English
```
@misc{patwa2020fighting,
title={Fighting an Infodemic: COVID-19 Fake News Dataset},
author={Parth Patwa and Shivam Sharma and Srinivas PYKL and Vineeth Guptha and Gitanjali Kumari and Md Shad Akhtar and Asif Ekbal and Amitava Das and Tanmoy Chakraborty},
year={2020},
eprint={2011.03327},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | nanyy1025/covid_fake_news | [
"task_categories:text-classification",
"task_categories:zero-shot-classification",
"language:en",
"arxiv:2011.03327",
"region:us"
]
| 2023-02-24T01:01:04+00:00 | {"language": ["en"], "task_categories": ["text-classification", "zero-shot-classification"]} | 2023-02-24T01:36:24+00:00 | [
"2011.03327"
]
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-zero-shot-classification #language-English #arxiv-2011.03327 #region-us
|
Constraint@AAAI2021 - COVID19 Fake News Detection in English
| []
| [
"TAGS\n#task_categories-text-classification #task_categories-zero-shot-classification #language-English #arxiv-2011.03327 #region-us \n"
]
| [
43
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-zero-shot-classification #language-English #arxiv-2011.03327 #region-us \n"
]
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]
|
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