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8b6f8f0a92044af89b17ac29fbcfe509c64d8e45 | # Dataset Card for "VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_1000"
[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_2.7b_VQAv2_visclues_ns_1000 | [
"region:us"
]
| 2023-02-15T04:19:26+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 25492223, "num_examples": 1000}], "download_size": 4915735, "dataset_size": 25492223}} | 2023-02-15T04:19:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_1000"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_1000\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_1000\"\n\nMore Information needed"
]
| [
6,
43
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_1000\"\n\nMore Information needed"
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|
fea8e4d61fb7d9d253d0fc8a9f1a93719285ade8 |
# Misc Datasets
Here i will upload datasets (images + captions) of concepts/styles/characters for anyone to use on their models, as i am not able to do LoRA's myself, alongside other datasets i've used for other models.</br>
Some are handcropped and/or handpicked, some not. If it's a big dataset it's probably automatically cropped (https://www.birme.net, 1280x1280, jpeg 95% quality) and not handpicked.
I've also included a python script for anyone that wants to use gallery-dl to download images, since its tags are pretty fucked up.</br>
It basically fixes its main problems and also removes metatags like 'commentary', 'translated' and similar, and gives the option to change underscores with spaces and other stuff.
<details>
<summary>Characters</summary>
- [Neru (Blue Archive)](https://huggingface.co/datasets/Cosk/misc-datasets/resolve/main/characters/neru_ba.rar)
- [Jibril (No Game No Life)](https://huggingface.co/datasets/Cosk/misc-datasets/resolve/main/characters/jibril.rar)
- [Fubuki (One Punch Man)](https://huggingface.co/datasets/Cosk/misc-datasets/resolve/main/characters/fubuki.rar) Doesn't include captions! You might want to use something like WD Tagger.
</details>
<details>
<summary>Styles</summary>
- [Cutesexyrobutts](https://huggingface.co/datasets/Cosk/cutesexyrobutts)
- [One Punch Man - Yuusuke Murata](https://huggingface.co/datasets/Cosk/misc-datasets/resolve/main/styles/opm_murata.rar) Doesn't include captions! You might want to use something like WD Tagger.
- [Phantom IX Row](https://huggingface.co/datasets/Cosk/misc-datasets/resolve/main/styles/phantom_ix_row.rar)
- [Mamimi (Mamamimi)](https://huggingface.co/datasets/Cosk/misc-datasets/resolve/main/styles/mamimi.rar)
</details>
<details>
<summary>Concepts</summary>
- [Breasts On Glass](https://huggingface.co/datasets/Cosk/misc-datasets/resolve/main/concepts/brst_gls.rar) Doesn't include captions! You might want to use something like WD Tagger.
- [Fingering](https://huggingface.co/datasets/Cosk/misc-datasets/resolve/main/concepts/fingering.rar)
- [Oversized Breast Cup](https://huggingface.co/datasets/Cosk/misc-datasets/resolve/main/concepts/oversized_cup.rar)
- [White Eyelashes](https://huggingface.co/datasets/Cosk/misc-datasets/resolve/main/concepts/white_eyelashes.rar)
- [Mizumizuni Fellatio](https://huggingface.co/datasets/Cosk/misc-datasets/resolve/main/concepts/mizumizuni.rar)
- [Unaligned Breasts Doggystyle](https://huggingface.co/datasets/Cosk/misc-datasets/resolve/main/concepts/unbr_doggy.rar)
- [Milking Handjob](https://huggingface.co/datasets/Cosk/misc-datasets/resolve/main/concepts/mlk_handjob.rar)
- [Fellatio + View Between Legs](https://huggingface.co/datasets/Cosk/misc-datasets/resolve/main/concepts/between_legs_fella.rar)
</details>
| cosc/misc-datasets | [
"language:en",
"license:creativeml-openrail-m",
"stable-diffusion",
"art",
"dataset",
"concept",
"character",
"style",
"dreambooth",
"lora",
"textual inversion",
"region:us"
]
| 2023-02-15T05:38:11+00:00 | {"language": ["en"], "license": "creativeml-openrail-m", "pipeline_tag": "text-to-image", "tags": ["stable-diffusion", "art", "dataset", "concept", "character", "style", "dreambooth", "lora", "textual inversion"]} | 2023-03-14T02:57:49+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-creativeml-openrail-m #stable-diffusion #art #dataset #concept #character #style #dreambooth #lora #textual inversion #region-us
|
# Misc Datasets
Here i will upload datasets (images + captions) of concepts/styles/characters for anyone to use on their models, as i am not able to do LoRA's myself, alongside other datasets i've used for other models.</br>
Some are handcropped and/or handpicked, some not. If it's a big dataset it's probably automatically cropped (URL, 1280x1280, jpeg 95% quality) and not handpicked.
I've also included a python script for anyone that wants to use gallery-dl to download images, since its tags are pretty fucked up.</br>
It basically fixes its main problems and also removes metatags like 'commentary', 'translated' and similar, and gives the option to change underscores with spaces and other stuff.
<details>
<summary>Characters</summary>
- Neru (Blue Archive)
- Jibril (No Game No Life)
- Fubuki (One Punch Man) Doesn't include captions! You might want to use something like WD Tagger.
</details>
<details>
<summary>Styles</summary>
- Cutesexyrobutts
- One Punch Man - Yuusuke Murata Doesn't include captions! You might want to use something like WD Tagger.
- Phantom IX Row
- Mamimi (Mamamimi)
</details>
<details>
<summary>Concepts</summary>
- Breasts On Glass Doesn't include captions! You might want to use something like WD Tagger.
- Fingering
- Oversized Breast Cup
- White Eyelashes
- Mizumizuni Fellatio
- Unaligned Breasts Doggystyle
- Milking Handjob
- Fellatio + View Between Legs
</details>
| [
"# Misc Datasets\nHere i will upload datasets (images + captions) of concepts/styles/characters for anyone to use on their models, as i am not able to do LoRA's myself, alongside other datasets i've used for other models.</br>\nSome are handcropped and/or handpicked, some not. If it's a big dataset it's probably automatically cropped (URL, 1280x1280, jpeg 95% quality) and not handpicked.\n\nI've also included a python script for anyone that wants to use gallery-dl to download images, since its tags are pretty fucked up.</br>\nIt basically fixes its main problems and also removes metatags like 'commentary', 'translated' and similar, and gives the option to change underscores with spaces and other stuff.\n\n<details>\n <summary>Characters</summary>\n \n - Neru (Blue Archive)\n - Jibril (No Game No Life)\n - Fubuki (One Punch Man) Doesn't include captions! You might want to use something like WD Tagger.\n</details>\n\n<details>\n <summary>Styles</summary>\n \n - Cutesexyrobutts\n - One Punch Man - Yuusuke Murata Doesn't include captions! You might want to use something like WD Tagger.\n - Phantom IX Row\n - Mamimi (Mamamimi)\n</details>\n\n<details>\n <summary>Concepts</summary>\n \n - Breasts On Glass Doesn't include captions! You might want to use something like WD Tagger.\n - Fingering\n - Oversized Breast Cup\n - White Eyelashes\n - Mizumizuni Fellatio\n - Unaligned Breasts Doggystyle\n - Milking Handjob\n - Fellatio + View Between Legs\n</details>"
]
| [
"TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #art #dataset #concept #character #style #dreambooth #lora #textual inversion #region-us \n",
"# Misc Datasets\nHere i will upload datasets (images + captions) of concepts/styles/characters for anyone to use on their models, as i am not able to do LoRA's myself, alongside other datasets i've used for other models.</br>\nSome are handcropped and/or handpicked, some not. If it's a big dataset it's probably automatically cropped (URL, 1280x1280, jpeg 95% quality) and not handpicked.\n\nI've also included a python script for anyone that wants to use gallery-dl to download images, since its tags are pretty fucked up.</br>\nIt basically fixes its main problems and also removes metatags like 'commentary', 'translated' and similar, and gives the option to change underscores with spaces and other stuff.\n\n<details>\n <summary>Characters</summary>\n \n - Neru (Blue Archive)\n - Jibril (No Game No Life)\n - Fubuki (One Punch Man) Doesn't include captions! You might want to use something like WD Tagger.\n</details>\n\n<details>\n <summary>Styles</summary>\n \n - Cutesexyrobutts\n - One Punch Man - Yuusuke Murata Doesn't include captions! You might want to use something like WD Tagger.\n - Phantom IX Row\n - Mamimi (Mamamimi)\n</details>\n\n<details>\n <summary>Concepts</summary>\n \n - Breasts On Glass Doesn't include captions! You might want to use something like WD Tagger.\n - Fingering\n - Oversized Breast Cup\n - White Eyelashes\n - Mizumizuni Fellatio\n - Unaligned Breasts Doggystyle\n - Milking Handjob\n - Fellatio + View Between Legs\n</details>"
]
| [
55,
423
]
| [
"passage: TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #art #dataset #concept #character #style #dreambooth #lora #textual inversion #region-us \n# Misc Datasets\nHere i will upload datasets (images + captions) of concepts/styles/characters for anyone to use on their models, as i am not able to do LoRA's myself, alongside other datasets i've used for other models.</br>\nSome are handcropped and/or handpicked, some not. If it's a big dataset it's probably automatically cropped (URL, 1280x1280, jpeg 95% quality) and not handpicked.\n\nI've also included a python script for anyone that wants to use gallery-dl to download images, since its tags are pretty fucked up.</br>\nIt basically fixes its main problems and also removes metatags like 'commentary', 'translated' and similar, and gives the option to change underscores with spaces and other stuff.\n\n<details>\n <summary>Characters</summary>\n \n - Neru (Blue Archive)\n - Jibril (No Game No Life)\n - Fubuki (One Punch Man) Doesn't include captions! You might want to use something like WD Tagger.\n</details>\n\n<details>\n <summary>Styles</summary>\n \n - Cutesexyrobutts\n - One Punch Man - Yuusuke Murata Doesn't include captions! You might want to use something like WD Tagger.\n - Phantom IX Row\n - Mamimi (Mamamimi)\n</details>\n\n<details>\n <summary>Concepts</summary>\n \n - Breasts On Glass Doesn't include captions! You might want to use something like WD Tagger.\n - Fingering\n - Oversized Breast Cup\n - White Eyelashes\n - Mizumizuni Fellatio\n - Unaligned Breasts Doggystyle\n - Milking Handjob\n - Fellatio + View Between Legs\n</details>"
]
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|
cabb3f889c7812202a8f4807a4016f7ba3ecfab7 | # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_1000"
[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_VQAv2_visclues_ns_1000 | [
"region:us"
]
| 2023-02-15T06:05:09+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 25491851, "num_examples": 1000}], "download_size": 4916575, "dataset_size": 25491851}} | 2023-02-15T06:05:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_1000"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_1000\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_1000\"\n\nMore Information needed"
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_1000\"\n\nMore Information needed"
]
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|
edc4edb5bded6de6c469fc518ce3cc35d2a9d1bc | # Dataset Card for "VALUE_wikitext103_got"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_wikitext103_got | [
"region:us"
]
| 2023-02-15T06:27:19+00:00 | {"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 99055, "num_examples": 123}, {"name": "train", "num_bytes": 44057216, "num_examples": 53727}, {"name": "validation", "num_bytes": 77641, "num_examples": 91}], "download_size": 27214474, "dataset_size": 44233912}} | 2023-02-15T06:27:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_wikitext103_got"
More Information needed | [
"# Dataset Card for \"VALUE_wikitext103_got\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_wikitext103_got\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext103_got\"\n\nMore Information needed"
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|
7d6f4c3b0af9c9c55e99914691a2002da4b0d4dd | # Dataset Card for "VALUE_wikitext103_negative_inversion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_wikitext103_negative_inversion | [
"region:us"
]
| 2023-02-15T06:30:47+00:00 | {"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 647, "num_examples": 1}, {"name": "train", "num_bytes": 1026, "num_examples": 1}, {"name": "validation", "num_bytes": 631, "num_examples": 1}], "download_size": 18138, "dataset_size": 2304}} | 2023-02-15T06:30:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_wikitext103_negative_inversion"
More Information needed | [
"# Dataset Card for \"VALUE_wikitext103_negative_inversion\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_wikitext103_negative_inversion\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext103_negative_inversion\"\n\nMore Information needed"
]
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|
d75bb029e05380ece15280d09668add7c493848e | # Dataset Card for "VALUE_wikitext103_negative_concord"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_wikitext103_negative_concord | [
"region:us"
]
| 2023-02-15T06:32:03+00:00 | {"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 168320, "num_examples": 185}, {"name": "train", "num_bytes": 76726939, "num_examples": 83977}, {"name": "validation", "num_bytes": 151295, "num_examples": 173}], "download_size": 47218418, "dataset_size": 77046554}} | 2023-02-15T06:32:09+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_wikitext103_negative_concord"
More Information needed | [
"# Dataset Card for \"VALUE_wikitext103_negative_concord\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_wikitext103_negative_concord\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext103_negative_concord\"\n\nMore Information needed"
]
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|
0fb05489dbc22df7f7c5e1658024b99081645e40 | # Dataset Card for "patched_test_p_20_m1_predictions_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_20_m1_predictions_v3 | [
"region:us"
]
| 2023-02-15T06:42:26+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": 1534784568, "num_examples": 2775054}], "download_size": 135840078, "dataset_size": 1534784568}} | 2023-02-15T06:42:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_20_m1_predictions_v3"
More Information needed | [
"# Dataset Card for \"patched_test_p_20_m1_predictions_v3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_20_m1_predictions_v3\"\n\nMore Information needed"
]
| [
6,
28
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_20_m1_predictions_v3\"\n\nMore Information needed"
]
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|
7739c1b59de9e7d167cbc36d812ad0e2236de6da | # Dataset Card for "VALUE_wikitext103_drop_aux"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_wikitext103_drop_aux | [
"region:us"
]
| 2023-02-15T06:53:16+00:00 | {"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 292533, "num_examples": 396}, {"name": "train", "num_bytes": 131089043, "num_examples": 174077}, {"name": "validation", "num_bytes": 232418, "num_examples": 340}], "download_size": 78562593, "dataset_size": 131613994}} | 2023-02-15T06:53:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_wikitext103_drop_aux"
More Information needed | [
"# Dataset Card for \"VALUE_wikitext103_drop_aux\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_wikitext103_drop_aux\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext103_drop_aux\"\n\nMore Information needed"
]
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|
fa5094290caf692b28f495eba8916a4641988e07 | # Dataset Card for "VALUE_wikitext103_dey_it"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_wikitext103_dey_it | [
"region:us"
]
| 2023-02-15T07:06:29+00:00 | {"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 121288, "num_examples": 141}, {"name": "train", "num_bytes": 50962893, "num_examples": 58184}, {"name": "validation", "num_bytes": 101616, "num_examples": 126}], "download_size": 31535043, "dataset_size": 51185797}} | 2023-02-15T07:06:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_wikitext103_dey_it"
More Information needed | [
"# Dataset Card for \"VALUE_wikitext103_dey_it\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_wikitext103_dey_it\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext103_dey_it\"\n\nMore Information needed"
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|
47c100c11102b3f059f35adafcee173a477168e2 | # Dataset Card for "VALUE_wikitext103_null_relcl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_wikitext103_null_relcl | [
"region:us"
]
| 2023-02-15T07:10:10+00:00 | {"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 465774, "num_examples": 528}, {"name": "train", "num_bytes": 199511886, "num_examples": 229800}, {"name": "validation", "num_bytes": 389980, "num_examples": 465}], "download_size": 120237073, "dataset_size": 200367640}} | 2023-02-15T07:10:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_wikitext103_null_relcl"
More Information needed | [
"# Dataset Card for \"VALUE_wikitext103_null_relcl\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_wikitext103_null_relcl\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext103_null_relcl\"\n\nMore Information needed"
]
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|
4f6f34d75b1c8cffe833a240b8848bb5045c8858 | # Dataset Card for "VALUE_wikitext103_been_done"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_wikitext103_been_done | [
"region:us"
]
| 2023-02-15T07:12:40+00:00 | {"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 631030, "num_examples": 752}, {"name": "train", "num_bytes": 250823074, "num_examples": 294089}, {"name": "validation", "num_bytes": 553077, "num_examples": 673}], "download_size": 148523130, "dataset_size": 252007181}} | 2023-02-15T07:12:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_wikitext103_been_done"
More Information needed | [
"# Dataset Card for \"VALUE_wikitext103_been_done\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_wikitext103_been_done\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext103_been_done\"\n\nMore Information needed"
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|
965dc8b6a47349e0494941396c23a8b66e7be2ee | # Dataset Card for "CHISTES_spanish_jokes"
Dataset from [Workshop for NLP introduction with Spanish jokes](https://github.com/liopic/chistes-nlp)
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mrm8488/CHISTES_spanish_jokes | [
"task_categories:text-classification",
"task_categories:text-generation",
"language:es",
"region:us"
]
| 2023-02-15T07:19:30+00:00 | {"language": ["es"], "task_categories": ["text-classification", "text-generation"], "pretty_name": "chistes", "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "keywords", "dtype": "string"}, {"name": "funny", "dtype": "int64"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 814817, "num_examples": 2419}], "download_size": 504749, "dataset_size": 814817}} | 2023-02-17T10:26:57+00:00 | []
| [
"es"
]
| TAGS
#task_categories-text-classification #task_categories-text-generation #language-Spanish #region-us
| # Dataset Card for "CHISTES_spanish_jokes"
Dataset from Workshop for NLP introduction with Spanish jokes
More Information needed | [
"# Dataset Card for \"CHISTES_spanish_jokes\"\n\nDataset from Workshop for NLP introduction with Spanish jokes\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-text-generation #language-Spanish #region-us \n",
"# Dataset Card for \"CHISTES_spanish_jokes\"\n\nDataset from Workshop for NLP introduction with Spanish jokes\n\nMore Information needed"
]
| [
33,
32
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-text-generation #language-Spanish #region-us \n# Dataset Card for \"CHISTES_spanish_jokes\"\n\nDataset from Workshop for NLP introduction with Spanish jokes\n\nMore Information needed"
]
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|
d3d9ea1690b70e5e4fcfe093807ca00ab2e780a8 | # Dataset Card for "VALUE_wikitext103_null_genetive"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_wikitext103_null_genetive | [
"region:us"
]
| 2023-02-15T07:19:36+00:00 | {"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 657089, "num_examples": 838}, {"name": "train", "num_bytes": 313568270, "num_examples": 385999}, {"name": "validation", "num_bytes": 619642, "num_examples": 793}], "download_size": 184632397, "dataset_size": 314845001}} | 2023-02-15T07:19:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_wikitext103_null_genetive"
More Information needed | [
"# Dataset Card for \"VALUE_wikitext103_null_genetive\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_wikitext103_null_genetive\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext103_null_genetive\"\n\nMore Information needed"
]
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|
c0d53aa05de6ebc0edd97dee39d94339ace34a25 |
# Dataset Card for OSCAR-2019-Burmese-fix
## Dataset Description
This dataset is a cleand version of Myanmar language in OSCAR 2019 dataset.
### Contributions
[Swan Htet Aung](https://github.com/swanhtet1992)
| 5w4n/OSCAR-2019-Burmese-fix | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|oscar",
"language:my",
"license:cc0-1.0",
"burmese",
"myanmar",
"myanmar-news",
"myanmar-corpus",
"region:us"
]
| 2023-02-15T07:31:43+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["my"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended|oscar"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "oscar", "pretty_name": "OSCAR-2019-Burmese-fix", "configs": ["unshuffled_deduplicated_cleaned_my"], "tags": ["burmese", "myanmar", "myanmar-news", "myanmar-corpus"]} | 2023-02-16T09:01:07+00:00 | []
| [
"my"
]
| TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|oscar #language-Burmese #license-cc0-1.0 #burmese #myanmar #myanmar-news #myanmar-corpus #region-us
|
# Dataset Card for OSCAR-2019-Burmese-fix
## Dataset Description
This dataset is a cleand version of Myanmar language in OSCAR 2019 dataset.
### Contributions
Swan Htet Aung
| [
"# Dataset Card for OSCAR-2019-Burmese-fix",
"## Dataset Description\n\nThis dataset is a cleand version of Myanmar language in OSCAR 2019 dataset.",
"### Contributions\n\nSwan Htet Aung"
]
| [
"TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|oscar #language-Burmese #license-cc0-1.0 #burmese #myanmar #myanmar-news #myanmar-corpus #region-us \n",
"# Dataset Card for OSCAR-2019-Burmese-fix",
"## Dataset Description\n\nThis dataset is a cleand version of Myanmar language in OSCAR 2019 dataset.",
"### Contributions\n\nSwan Htet Aung"
]
| [
137,
12,
22,
10
]
| [
"passage: TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|oscar #language-Burmese #license-cc0-1.0 #burmese #myanmar #myanmar-news #myanmar-corpus #region-us \n# Dataset Card for OSCAR-2019-Burmese-fix## Dataset Description\n\nThis dataset is a cleand version of Myanmar language in OSCAR 2019 dataset.### Contributions\n\nSwan Htet Aung"
]
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|
677c2ef1fbd49b53f9c3a945f8c4cf5255fc493c | A dataset of translated novels based on: https://opus.nlpl.eu/Books.php (English-Dutch)
To be used as part of the Machine Translation assignment (week 3: fine-tuning NMT)
BSc Information Science course, RUG
| GroNLP/ik-mt-2023-books | [
"region:us"
]
| 2023-02-15T08:22:32+00:00 | {} | 2023-02-15T08:32:14+00:00 | []
| []
| TAGS
#region-us
| A dataset of translated novels based on: URL (English-Dutch)
To be used as part of the Machine Translation assignment (week 3: fine-tuning NMT)
BSc Information Science course, RUG
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
| [
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|
c1bbe7aba13342ada22d029d37bded9f7c24adb5 |
# Dataset Card for XMediaSum
### Dataset Summary
We present XMediaSum, a cross-lingual dialogue summarization dataset with 40K English(dialogues)->Chinese(summaries) and 40K English (dialogues)->German(summaries) samples. XMediaSum is created by manually translating the English summaries of MediaSum (a English monolingual dialogue summarization dataset) to both Chinese and German.
- Paper: [ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization](https://aclanthology.org/2022.emnlp-main.526/) (EMNLP 2022)
- GitHub: https://github.com/krystalan/ClidSum
### Supported Task
- Cross-Lingual Summarization
- Cross-Lingual Dialogue Summarization
### Languages
- source language: English
- target language: Chinese and German
## Dataset Structure
### Data Instances
One example is given below in JSON format:
```json
{
"dialogue": "MADELELEINE BRAND, host: OK, here's some good news on the jobs front for both men and women. A new survey out today from the employment firm Manpower finds that about a quarter of employers will add jobs this summer. That's for adults, but for teenagers this summer's job market is shaping up to be the weakest in more than 50 years.\r\nALEX COHEN, host: So, how do you get your teenage kids not to spend the entire summer glued to the couch? You're about to get some tips from Michelle Singletary. She's Day to Day's personal finance contributor. Hi, Michelle!\r\nMICHELLE SINGLETARY: Hi!\r\nALEX COHEN, host: So why is the summer job market so hard for teens this year?\r\nMICHELLE SINGLETARY: Lot of things going on right now. We've got a tough economy. We've got a lot of college graduates going into the market. We have people who are losing their jobs and taking jobs that would traditionally go to teens, like in restaurants and retailers. And we have a lot of older people holding on to their jobs and not retiring because they can't afford to retire. And that puts teens at the end of the line when it comes to these types of jobs.\r\nALEX COHEN, host: So you've got a teenager at home, a little bit young for the working world just yet, but what would you say to a teenager who's out there hunting around for a job?\r\nMICHELLE SINGLETARY: If you absolutely need a job, keep looking. You know, obviously the types of jobs that teens tend to go for in retail, fast food, you know, they still need people. And oftentimes you know, listen, you may not get the job at the beginning of the summer, but hold on because in late summer, when some of those college students are going back and perhaps some of those people who lost their jobs are finding permanent positions with more pay, you might be able to still get that job. So don't give up, you may spend a month or month and a half without it, but go back to those retailers and those restaurants and those fast food places to see if they still need someone.\r\nALEX COHEN, host: And now I know parents like having the break from providing allowance. But, you know, is - are there reasons maybe not to push your teen towards taking a job?\r\nMICHELLE SINGLETARY: I think it absolutely is. In fact I think too many teens are working and they don't need to work. They're some who absolutely need, they're contributing to their household or they're putting money into their own college fund. But more often than not, what parents do is say you've got to get a job, and then the teens get the job and they spend all the money on clothes and you know videos and iPods and paying their cell phone bills because they don't need a cell phone anyway.\r\nALEX COHEN, host: So it's not going towards the college tuition at all.\r\nMICHELLE SINGLETARY: It is not. It's just disposable income that they're disposing of. And parents are not setting any limits and you know and then the kids get used to the fact that they're using all of their paycheck. That's another bad habit. Because they don't have to pay bills and all, all their income goes through you know this stuff.\r\nMICHELLE SINGLETARY: And when it comes time to get a real job, they're surprised they don't have enough money. And so you know what? You can wait to work. Instead, maybe they can spend the summer volunteering at a charitable organization or you know going back to school and boosting up their math skills or their English skills. We push the teens out into the market too soon, I think for some families.\r\nALEX COHEN, host: But now let's say your kid is working. What tips can parents provide in terms of holding on to that summer money?\r\nMICHELLE SINGLETARY: You know, before they get their job, they need to sit down with them and do a budget. So before they actually work and get that first paycheck I mean, you know, have them draw up a budge where the money is going. And you ought to have some requirements for some of their money. That's right, be a parent.\r\nMICHELLE SINGLETARY: So make them put some of it towards their college fund, if in fact they're headed for college. You know what? Make them put some away, I call it the tax fund, even though they may not have to pay taxes, but to pay for long-term things that they may want. You know, books once they get to college, or maybe they want to get a car, and they can actually pay cash for it, with some of these funds. Don't let them just go out and spend it on movies and stuff. You ought to set some guidelines - this is where you should put the money. And look at their budget.\r\nALEX COHEN, host: Day to Day's personal finance contributor Michelle Singletary. Thank you, Michelle!\r\nMICHELLE SINGLETARY: You're welcome.\r\nALEX COHEN, host: Stay with us. NPR's Day to Day continues.",
"summary": "The tight job market could be bad news for teens seeking summer work. If your teen does find a job, will he or she know how to manage those paychecks? Our personal finance contributor talks with Alex Cohen about ways to help teens find a job.",
"summary_de": "Der angespannte Arbeitsmarkt könnte für Jugendliche, die Sommerarbeit suchen, eine schlechte Nachricht sein. Wenn Ihr Teenager einen Job findet, wird er oder sie wissen, wie er mit diesen Gehaltsschecks umgeht? Unser Mitarbeiter für persönliche Finanzen spricht mit Alex Cohen darüber, wie Teenager bei der Jobsuche unterstützt werden können.",
"summary_zh": "紧张的就业市场对寻找暑期工作的青少年来说可能是个坏消息。如果你的孩子找到了一份工作，他/她懂得怎么管理这些薪水吗？我们的个人理财撰稿人与亚历克斯·科恩谈论如何帮助青少年找到工作。"
},
```
### Data Fields
- 'dialogue': An English dialogue
- 'summary': the original English summary of the corresponding dialogue (provided by MediaSum)
- 'summary_de': the human-translated German summary
- 'summary_zh': the human-translated Chinese summary
### Data Splits
- training set: 20K samples
- validation set: 10K samples
- testing set: 10K samples
## Dataset Creation
Please refer to [our paper](https://aclanthology.org/2022.emnlp-main.526/) for more details.
## Considerations for Using the Data
Please refer to [our paper](https://aclanthology.org/2022.emnlp-main.526/) for more details.
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/krystalan/ClidSum)
### Licensing Information
License: CC BY-NC-SA 4.0
### Citation Information
```
@inproceedings{wang-etal-2022-clidsum,
title = "{C}lid{S}um: A Benchmark Dataset for Cross-Lingual Dialogue Summarization",
author = "Wang, Jiaan and
Meng, Fandong and
Lu, Ziyao and
Zheng, Duo and
Li, Zhixu and
Qu, Jianfeng and
Zhou, Jie",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.526",
pages = "7716--7729",
abstract = "We present ClidSum, a benchmark dataset towards building cross-lingual summarization systems on dialogue documents. It consists of 67k+ dialogue documents and 112k+ annotated summaries in different target languages. Based on the proposed ClidSum, we introduce two benchmark settings for supervised and semi-supervised scenarios, respectively. We then build various baseline systems in different paradigms (pipeline and end-to-end) and conduct extensive experiments on ClidSum to provide deeper analyses. Furthermore, we propose mDialBART which extends mBART via further pre-training, where the multiple objectives help the pre-trained model capture the structural characteristics as well as key content in dialogues and the transformation from source to the target language. Experimental results show the superiority of mDialBART, as an end-to-end model, outperforms strong pipeline models on ClidSum. Finally, we discuss specific challenges that current approaches faced with this task and give multiple promising directions for future research. We have released the dataset and code at https://github.com/krystalan/ClidSum.",
}
```
### Contributions
Thanks to [@krystalan](https://github.com/krystalan) for adding this dataset. | Krystalan/xmediasum | [
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]
| 2023-02-15T08:50:38+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["en", "zh", "de"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": [], "pretty_name": "xmediasum", "tags": []} | 2023-02-15T13:58:33+00:00 | []
| [
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| TAGS
#task_categories-summarization #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-English #language-Chinese #language-German #license-cc-by-nc-sa-4.0 #region-us
|
# Dataset Card for XMediaSum
### Dataset Summary
We present XMediaSum, a cross-lingual dialogue summarization dataset with 40K English(dialogues)->Chinese(summaries) and 40K English (dialogues)->German(summaries) samples. XMediaSum is created by manually translating the English summaries of MediaSum (a English monolingual dialogue summarization dataset) to both Chinese and German.
- Paper: ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization (EMNLP 2022)
- GitHub: URL
### Supported Task
- Cross-Lingual Summarization
- Cross-Lingual Dialogue Summarization
### Languages
- source language: English
- target language: Chinese and German
## Dataset Structure
### Data Instances
One example is given below in JSON format:
### Data Fields
- 'dialogue': An English dialogue
- 'summary': the original English summary of the corresponding dialogue (provided by MediaSum)
- 'summary_de': the human-translated German summary
- 'summary_zh': the human-translated Chinese summary
### Data Splits
- training set: 20K samples
- validation set: 10K samples
- testing set: 10K samples
## Dataset Creation
Please refer to our paper for more details.
## Considerations for Using the Data
Please refer to our paper for more details.
## Additional Information
### Dataset Curators
### Licensing Information
License: CC BY-NC-SA 4.0
### Contributions
Thanks to @krystalan for adding this dataset. | [
"# Dataset Card for XMediaSum",
"### Dataset Summary\n\nWe present XMediaSum, a cross-lingual dialogue summarization dataset with 40K English(dialogues)->Chinese(summaries) and 40K English (dialogues)->German(summaries) samples. XMediaSum is created by manually translating the English summaries of MediaSum (a English monolingual dialogue summarization dataset) to both Chinese and German.\n\n- Paper: ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization (EMNLP 2022)\n- GitHub: URL",
"### Supported Task\n\n- Cross-Lingual Summarization\n- Cross-Lingual Dialogue Summarization",
"### Languages\n\n- source language: English\n- target language: Chinese and German",
"## Dataset Structure",
"### Data Instances\nOne example is given below in JSON format:",
"### Data Fields\n\n- 'dialogue': An English dialogue\n- 'summary': the original English summary of the corresponding dialogue (provided by MediaSum)\n- 'summary_de': the human-translated German summary\n- 'summary_zh': the human-translated Chinese summary",
"### Data Splits\n- training set: 20K samples\n- validation set: 10K samples\n- testing set: 10K samples",
"## Dataset Creation\n\nPlease refer to our paper for more details.",
"## Considerations for Using the Data\n\nPlease refer to our paper for more details.",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\n\nLicense: CC BY-NC-SA 4.0",
"### Contributions\n\nThanks to @krystalan for adding this dataset."
]
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"# Dataset Card for XMediaSum",
"### Dataset Summary\n\nWe present XMediaSum, a cross-lingual dialogue summarization dataset with 40K English(dialogues)->Chinese(summaries) and 40K English (dialogues)->German(summaries) samples. XMediaSum is created by manually translating the English summaries of MediaSum (a English monolingual dialogue summarization dataset) to both Chinese and German.\n\n- Paper: ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization (EMNLP 2022)\n- GitHub: URL",
"### Supported Task\n\n- Cross-Lingual Summarization\n- Cross-Lingual Dialogue Summarization",
"### Languages\n\n- source language: English\n- target language: Chinese and German",
"## Dataset Structure",
"### Data Instances\nOne example is given below in JSON format:",
"### Data Fields\n\n- 'dialogue': An English dialogue\n- 'summary': the original English summary of the corresponding dialogue (provided by MediaSum)\n- 'summary_de': the human-translated German summary\n- 'summary_zh': the human-translated Chinese summary",
"### Data Splits\n- training set: 20K samples\n- validation set: 10K samples\n- testing set: 10K samples",
"## Dataset Creation\n\nPlease refer to our paper for more details.",
"## Considerations for Using the Data\n\nPlease refer to our paper for more details.",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\n\nLicense: CC BY-NC-SA 4.0",
"### Contributions\n\nThanks to @krystalan for adding this dataset."
]
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"passage: TAGS\n#task_categories-summarization #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-English #language-Chinese #language-German #license-cc-by-nc-sa-4.0 #region-us \n# Dataset Card for XMediaSum### Dataset Summary\n\nWe present XMediaSum, a cross-lingual dialogue summarization dataset with 40K English(dialogues)->Chinese(summaries) and 40K English (dialogues)->German(summaries) samples. XMediaSum is created by manually translating the English summaries of MediaSum (a English monolingual dialogue summarization dataset) to both Chinese and German.\n\n- Paper: ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization (EMNLP 2022)\n- GitHub: URL### Supported Task\n\n- Cross-Lingual Summarization\n- Cross-Lingual Dialogue Summarization### Languages\n\n- source language: English\n- target language: Chinese and German## Dataset Structure### Data Instances\nOne example is given below in JSON format:### Data Fields\n\n- 'dialogue': An English dialogue\n- 'summary': the original English summary of the corresponding dialogue (provided by MediaSum)\n- 'summary_de': the human-translated German summary\n- 'summary_zh': the human-translated Chinese summary### Data Splits\n- training set: 20K samples\n- validation set: 10K samples\n- testing set: 10K samples## Dataset Creation\n\nPlease refer to our paper for more details.## Considerations for Using the Data\n\nPlease refer to our paper for more details.## Additional Information### Dataset Curators### Licensing Information\n\nLicense: CC BY-NC-SA 4.0### Contributions\n\nThanks to @krystalan for adding this dataset."
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|
1b30a0dacaaed9f63636c275246414713bf89283 | # Dataset Card for "helpful-raw-anthropic"
This is a dataset derived from Anthropic's [HH-RLHF data](https://huggingface.co/datasets/Anthropic/hh-rlhf) of instructions and model-generated demonstrations. We combined training splits from the following two subsets:
* `helpful-base`
* `helpful-online`
To convert the multi-turn dialogues into `(instruction, demonstration)` pairs, just the first response from the Assistant was included. This heuristic captures the most obvious answers, but overlooks more complex questions where multiple turns were required to get a helpful response. Some additional filtering is likely required (e.g. defining a minimun length or computing ROUGE-L scores with the instruction/demonstration). | HuggingFaceH4/helpful-anthropic-raw | [
"license:mit",
"human-feedback",
"region:us"
]
| 2023-02-15T10:06:51+00:00 | {"license": "mit", "pretty_name": "Helpful Raw Anthropic", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "demonstration", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 34540085.04363476, "num_examples": 65499}], "download_size": 0, "dataset_size": 34540085.04363476}, "tags": ["human-feedback"]} | 2023-02-20T09:00:56+00:00 | []
| []
| TAGS
#license-mit #human-feedback #region-us
| # Dataset Card for "helpful-raw-anthropic"
This is a dataset derived from Anthropic's HH-RLHF data of instructions and model-generated demonstrations. We combined training splits from the following two subsets:
* 'helpful-base'
* 'helpful-online'
To convert the multi-turn dialogues into '(instruction, demonstration)' pairs, just the first response from the Assistant was included. This heuristic captures the most obvious answers, but overlooks more complex questions where multiple turns were required to get a helpful response. Some additional filtering is likely required (e.g. defining a minimun length or computing ROUGE-L scores with the instruction/demonstration). | [
"# Dataset Card for \"helpful-raw-anthropic\"\n\nThis is a dataset derived from Anthropic's HH-RLHF data of instructions and model-generated demonstrations. We combined training splits from the following two subsets:\n\n* 'helpful-base'\n* 'helpful-online'\n\nTo convert the multi-turn dialogues into '(instruction, demonstration)' pairs, just the first response from the Assistant was included. This heuristic captures the most obvious answers, but overlooks more complex questions where multiple turns were required to get a helpful response. Some additional filtering is likely required (e.g. defining a minimun length or computing ROUGE-L scores with the instruction/demonstration)."
]
| [
"TAGS\n#license-mit #human-feedback #region-us \n",
"# Dataset Card for \"helpful-raw-anthropic\"\n\nThis is a dataset derived from Anthropic's HH-RLHF data of instructions and model-generated demonstrations. We combined training splits from the following two subsets:\n\n* 'helpful-base'\n* 'helpful-online'\n\nTo convert the multi-turn dialogues into '(instruction, demonstration)' pairs, just the first response from the Assistant was included. This heuristic captures the most obvious answers, but overlooks more complex questions where multiple turns were required to get a helpful response. Some additional filtering is likely required (e.g. defining a minimun length or computing ROUGE-L scores with the instruction/demonstration)."
]
| [
16,
171
]
| [
"passage: TAGS\n#license-mit #human-feedback #region-us \n# Dataset Card for \"helpful-raw-anthropic\"\n\nThis is a dataset derived from Anthropic's HH-RLHF data of instructions and model-generated demonstrations. We combined training splits from the following two subsets:\n\n* 'helpful-base'\n* 'helpful-online'\n\nTo convert the multi-turn dialogues into '(instruction, demonstration)' pairs, just the first response from the Assistant was included. This heuristic captures the most obvious answers, but overlooks more complex questions where multiple turns were required to get a helpful response. Some additional filtering is likely required (e.g. defining a minimun length or computing ROUGE-L scores with the instruction/demonstration)."
]
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|
8040edc6f111547bf80befe756703a45ecd07d09 | # 岁己SUI的sovits底模数据集
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
#### ForBaseModel.zip：
数据质量不高，只用于岁己音色的底模训练（洗去G_0.pth和D_0.pth的音色）
采样频率为44.1kHz，使用前请注意预处理
取自岁己22年12月、23年1月的录播（除电台，共计211:13:21），经过以下步骤筛选处理
1. 挑取BGM音量较低的直播片段（20:39:21）_[[LowBGM.zip]](https://huggingface.co/datasets/Miuzarte/SUISovitsDataForBaseModel/blob/main/%E6%9C%89%E7%9A%84%E6%B2%A1%E7%9A%84/LowBGM.zip)
2. [UVR5](https://github.com/Anjok07/ultimatevocalremovergui) VR Architecture 5_HP-Karaoke-UVR统一处理，尽量除去了BGM中的人声（20:39:20，反正确实就是少了1s）_[[UVR-ed.zip]](https://huggingface.co/datasets/Miuzarte/SUISovitsDataForBaseModel/blob/main/%E6%9C%89%E7%9A%84%E6%B2%A1%E7%9A%84/UVR-ed.zip)
3. [Audio Slicer](https://github.com/flutydeer/audio-slicer)切片（12:45:29）_[[Slice-d.zip]](https://huggingface.co/datasets/Miuzarte/SUISovitsDataForBaseModel/blob/main/%E6%9C%89%E7%9A%84%E6%B2%A1%E7%9A%84/Slice-d.zip)
4. [Fish Audio Preprocessor](https://github.com/fishaudio/audio-preprocess)响度标准化并删除过短过长的片段（11:24:06）_[[LoudnessNorm-ed.zip]](https://huggingface.co/datasets/Miuzarte/SUISovitsDataForBaseModel/blob/main/%E6%9C%89%E7%9A%84%E6%B2%A1%E7%9A%84/LoudnessNorm-ed.zip)
5. [Spliter Wav by IceKyrin](https://github.com/IceKyrin)声纹识别稳定数据（06:47:46）_[[ForBaseModel.zip]](https://huggingface.co/datasets/Miuzarte/SUISovitsDataForBaseModel/blob/main/ForBaseModel.zip)
文件结构：
```
ForBaseModel.zip
├── 25788785-20221201-195959-658_01_(Vocals)_1.wav
├── 25788785-20221201-195959-658_01_(Vocals)_3.wav
├── ......
├── 25788785-20230201-005152-235_03_(Vocals)_9.wav
└── 25788785-20230201-005152-235_03_(Vocals)_10.wav
```
#### ForBaseModel_sovits3.0.zip：
ForBaseModel.zip经过预处理后的数据集，可以直接投入sovits3.0_48k使用，采样频率为48kHz
文件结构：
```
ForBaseModel_sovits3.0.zip
├── configs
│ └── config.json
├── dataset
│ └── 48k
│ └── suijiSUI
│ ├── 25788785-20221201-195959-658_01_(Vocals)_1.wav
│ ├── 25788785-20221201-195959-658_01_(Vocals)_1.wav.f0.npy
│ ├── 25788785-20221201-195959-658_01_(Vocals)_1.wav.soft.pt
│ ├── ......
│ ├── 25788785-20230201-005152-235_03_(Vocals)_10.wav
│ ├── 25788785-20230201-005152-235_03_(Vocals)_10.wav.f0.npy
│ └── 25788785-20230201-005152-235_03_(Vocals)_10.wav.soft.pt
└── filelists
├── test.txt
├── train.txt
└── val.txt
```
#### ForBaseModel_sovits4.0.zip：
ForBaseModel.zip经过预处理后的数据集，可以直接投入sovits4.0使用，采样频率为44.1kHz
注意：4.0开始config.json中的batch_size默认为6，我又给改回12了
文件结构：
```
ForBaseModel_sovits4.0.zip
├── configs
│ └── config.json
├── dataset
│ └── 44k
│ └── suijiSUI
│ ├── 25788785-20221201-195959-658_01_(Vocals)_1.wav
│ ├── 25788785-20221201-195959-658_01_(Vocals)_1.wav.f0.npy
│ ├── 25788785-20221201-195959-658_01_(Vocals)_1.wav.soft.pt
│ ├── ......
│ ├── 25788785-20230201-005152-235_03_(Vocals)_10.wav
│ ├── 25788785-20230201-005152-235_03_(Vocals)_10.wav.f0.npy
│ └── 25788785-20230201-005152-235_03_(Vocals)_10.wav.soft.pt
└── filelists
├── test.txt
├── train.txt
└── val.txt
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Chinese(98%)
English(1%)
Japanese(1%)
[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] | Miuzarte/SUISovitsDataForBaseModel | [
"language:zh",
"AIvtuber",
"VirtuaReal",
"region:us"
]
| 2023-02-15T10:33:06+00:00 | {"language": ["zh"], "tags": ["AIvtuber", "VirtuaReal"]} | 2023-03-10T04:49:43+00:00 | []
| [
"zh"
]
| TAGS
#language-Chinese #AIvtuber #VirtuaReal #region-us
| # 岁己SUI的sovits底模数据集
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
#### URL：
数据质量不高，只用于岁己音色的底模训练（洗去G_0.pth和D_0.pth的音色）
采样频率为44.1kHz，使用前请注意预处理
取自岁己22年12月、23年1月的录播（除电台，共计211:13:21），经过以下步骤筛选处理
1. 挑取BGM音量较低的直播片段（20:39:21）_[[URL]](URL
2. UVR5 VR Architecture 5_HP-Karaoke-UVR统一处理，尽量除去了BGM中的人声（20:39:20，反正确实就是少了1s）_[[URL]](URL
3. Audio Slicer切片（12:45:29）_[[URL]](URL
4. Fish Audio Preprocessor响度标准化并删除过短过长的片段（11:24:06）_[[URL]](URL
5. Spliter Wav by IceKyrin声纹识别稳定数据（06:47:46）_[[URL]](URL
文件结构：
#### ForBaseModel_sovits3.0.zip：
ForBaseModel.zip经过预处理后的数据集，可以直接投入sovits3.0_48k使用，采样频率为48kHz
文件结构：
#### ForBaseModel_sovits4.0.zip：
ForBaseModel.zip经过预处理后的数据集，可以直接投入sovits4.0使用，采样频率为44.1kHz
注意：4.0开始config.json中的batch_size默认为6，我又给改回12了
文件结构：
### Supported Tasks and Leaderboards
### Languages
Chinese(98%)
English(1%)
Japanese(1%)
## 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
| [
"# 岁己SUI的sovits底模数据集",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary",
"#### URL：\n\n数据质量不高，只用于岁己音色的底模训练（洗去G_0.pth和D_0.pth的音色）\n\n采样频率为44.1kHz，使用前请注意预处理\n\n取自岁己22年12月、23年1月的录播（除电台，共计211:13:21），经过以下步骤筛选处理\n\n1. 挑取BGM音量较低的直播片段（20:39:21）_[[URL]](URL\n\n2. UVR5 VR Architecture 5_HP-Karaoke-UVR统一处理，尽量除去了BGM中的人声（20:39:20，反正确实就是少了1s）_[[URL]](URL\n\n3. Audio Slicer切片（12:45:29）_[[URL]](URL\n\n4. Fish Audio Preprocessor响度标准化并删除过短过长的片段（11:24:06）_[[URL]](URL\n\n5. Spliter Wav by IceKyrin声纹识别稳定数据（06:47:46）_[[URL]](URL\n\n文件结构：",
"#### ForBaseModel_sovits3.0.zip：\n\nForBaseModel.zip经过预处理后的数据集，可以直接投入sovits3.0_48k使用，采样频率为48kHz\n\n文件结构：",
"#### ForBaseModel_sovits4.0.zip：\n\nForBaseModel.zip经过预处理后的数据集，可以直接投入sovits4.0使用，采样频率为44.1kHz\n\n注意：4.0开始config.json中的batch_size默认为6，我又给改回12了\n\n文件结构：",
"### Supported Tasks and Leaderboards",
"### Languages\n\nChinese(98%)\n\nEnglish(1%)\n\nJapanese(1%)",
"## 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#language-Chinese #AIvtuber #VirtuaReal #region-us \n",
"# 岁己SUI的sovits底模数据集",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary",
"#### URL：\n\n数据质量不高，只用于岁己音色的底模训练（洗去G_0.pth和D_0.pth的音色）\n\n采样频率为44.1kHz，使用前请注意预处理\n\n取自岁己22年12月、23年1月的录播（除电台，共计211:13:21），经过以下步骤筛选处理\n\n1. 挑取BGM音量较低的直播片段（20:39:21）_[[URL]](URL\n\n2. UVR5 VR Architecture 5_HP-Karaoke-UVR统一处理，尽量除去了BGM中的人声（20:39:20，反正确实就是少了1s）_[[URL]](URL\n\n3. Audio Slicer切片（12:45:29）_[[URL]](URL\n\n4. Fish Audio Preprocessor响度标准化并删除过短过长的片段（11:24:06）_[[URL]](URL\n\n5. Spliter Wav by IceKyrin声纹识别稳定数据（06:47:46）_[[URL]](URL\n\n文件结构：",
"#### ForBaseModel_sovits3.0.zip：\n\nForBaseModel.zip经过预处理后的数据集，可以直接投入sovits3.0_48k使用，采样频率为48kHz\n\n文件结构：",
"#### ForBaseModel_sovits4.0.zip：\n\nForBaseModel.zip经过预处理后的数据集，可以直接投入sovits4.0使用，采样频率为44.1kHz\n\n注意：4.0开始config.json中的batch_size默认为6，我又给改回12了\n\n文件结构：",
"### Supported Tasks and Leaderboards",
"### Languages\n\nChinese(98%)\n\nEnglish(1%)\n\nJapanese(1%)",
"## 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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20,
14,
24,
6,
234,
50,
72,
10,
14,
6,
6,
5,
5,
5,
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10,
5,
5,
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8,
8,
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5
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| [
"passage: TAGS\n#language-Chinese #AIvtuber #VirtuaReal #region-us \n# 岁己SUI的sovits底模数据集## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary#### URL：\n\n数据质量不高，只用于岁己音色的底模训练（洗去G_0.pth和D_0.pth的音色）\n\n采样频率为44.1kHz，使用前请注意预处理\n\n取自岁己22年12月、23年1月的录播（除电台，共计211:13:21），经过以下步骤筛选处理\n\n1. 挑取BGM音量较低的直播片段（20:39:21）_[[URL]](URL\n\n2. UVR5 VR Architecture 5_HP-Karaoke-UVR统一处理，尽量除去了BGM中的人声（20:39:20，反正确实就是少了1s）_[[URL]](URL\n\n3. Audio Slicer切片（12:45:29）_[[URL]](URL\n\n4. Fish Audio Preprocessor响度标准化并删除过短过长的片段（11:24:06）_[[URL]](URL\n\n5. Spliter Wav by IceKyrin声纹识别稳定数据（06:47:46）_[[URL]](URL\n\n文件结构：#### ForBaseModel_sovits3.0.zip：\n\nForBaseModel.zip经过预处理后的数据集，可以直接投入sovits3.0_48k使用，采样频率为48kHz\n\n文件结构：#### ForBaseModel_sovits4.0.zip：\n\nForBaseModel.zip经过预处理后的数据集，可以直接投入sovits4.0使用，采样频率为44.1kHz\n\n注意：4.0开始config.json中的batch_size默认为6，我又给改回12了\n\n文件结构：### Supported Tasks and Leaderboards### Languages\n\nChinese(98%)\n\nEnglish(1%)\n\nJapanese(1%)## 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"
]
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|
24d09ffda4313e126ce37ef5f3217399d0719c6c | # Dataset Card for "t5-Europarl-en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tj-solergibert/t5-Europarl-en | [
"region:us"
]
| 2023-02-15T11:03:34+00:00 | {"dataset_info": {"features": [{"name": "dest_lang", "dtype": {"class_label": {"names": {"0": "de", "1": "en", "2": "es", "3": "fr", "4": "it", "5": "nl", "6": "pl", "7": "pt", "8": "ro"}}}}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 417227830, "num_examples": 561067}, {"name": "test", "num_bytes": 61238209, "num_examples": 80606}, {"name": "valid", "num_bytes": 57930051, "num_examples": 76911}], "download_size": 125777513, "dataset_size": 536396090}} | 2023-02-15T11:03:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "t5-Europarl-en"
More Information needed | [
"# Dataset Card for \"t5-Europarl-en\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"t5-Europarl-en\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"t5-Europarl-en\"\n\nMore Information needed"
]
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|
ea8e1b6f3a1d3b436d2e7f1e7adf17e738c25ec9 | # Dataset Card for Fill50K
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset is converted from fill50k example dataset of [ControlNet](https://github.com/lllyasviel/ControlNet)
### 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
[fill50k.zip](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip)
#### 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] | HighCWu/fill50k | [
"language:en",
"license:openrail",
"region:us"
]
| 2023-02-15T12:48:42+00:00 | {"language": ["en"], "license": "openrail", "pretty_name": "a", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "guide", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 454411979, "num_examples": 50000}], "download_size": 316021131, "dataset_size": 454411979}} | 2023-02-15T15:45:27+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-openrail #region-us
| # Dataset Card for Fill50K
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset is converted from fill50k example dataset of ControlNet
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
URL
#### 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 Fill50K",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset is converted from fill50k example dataset of ControlNet",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data\n\nURL",
"#### 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#language-English #license-openrail #region-us \n",
"# Dataset Card for Fill50K",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset is converted from fill50k example dataset of ControlNet",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data\n\nURL",
"#### 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"
]
| [
16,
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"passage: TAGS\n#language-English #license-openrail #region-us \n# Dataset Card for Fill50K## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset is converted from fill50k example dataset of ControlNet### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data\n\nURL#### 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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|
9037f14e7c8313e3e9f8d3d0af747e1f841dd9c0 | # Dataset Card for "helpful-anthropic-raw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lewtun/helpful-anthropic-raw | [
"region:us"
]
| 2023-02-15T13:42:37+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "demonstration", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 26008407, "num_examples": 65842}], "download_size": 15735838, "dataset_size": 26008407}} | 2023-02-15T13:42:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "helpful-anthropic-raw"
More Information needed | [
"# Dataset Card for \"helpful-anthropic-raw\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"helpful-anthropic-raw\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"helpful-anthropic-raw\"\n\nMore Information needed"
]
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|
15296bd1c7a8b69a5a9771e37f0f2686dbc0e9b1 |
# Dataset Card for XMediaSum
### Dataset Summary
We present XMediaSum, a cross-lingual dialogue summarization dataset with 40K English(dialogues)->Chinese(summaries) and 40K English (dialogues)->German(summaries) samples. XMediaSum is created by manually translating the English summaries of MediaSum (a English monolingual dialogue summarization dataset) to both Chinese and German.
- Paper: [ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization](https://aclanthology.org/2022.emnlp-main.526/) (EMNLP 2022)
- GitHub: https://github.com/krystalan/ClidSum
### Supported Task
- Cross-Lingual Summarization
- Cross-Lingual Dialogue Summarization
### Languages
- source language: English
- target language: Chinese and German
## Dataset Structure
### Data Instances
One example is given below in JSON format:
```json
{
"dialogue": "MADELELEINE BRAND, host: OK, here's some good news on the jobs front for both men and women. A new survey out today from the employment firm Manpower finds that about a quarter of employers will add jobs this summer. That's for adults, but for teenagers this summer's job market is shaping up to be the weakest in more than 50 years.\r\nALEX COHEN, host: So, how do you get your teenage kids not to spend the entire summer glued to the couch? You're about to get some tips from Michelle Singletary. She's Day to Day's personal finance contributor. Hi, Michelle!\r\nMICHELLE SINGLETARY: Hi!\r\nALEX COHEN, host: So why is the summer job market so hard for teens this year?\r\nMICHELLE SINGLETARY: Lot of things going on right now. We've got a tough economy. We've got a lot of college graduates going into the market. We have people who are losing their jobs and taking jobs that would traditionally go to teens, like in restaurants and retailers. And we have a lot of older people holding on to their jobs and not retiring because they can't afford to retire. And that puts teens at the end of the line when it comes to these types of jobs.\r\nALEX COHEN, host: So you've got a teenager at home, a little bit young for the working world just yet, but what would you say to a teenager who's out there hunting around for a job?\r\nMICHELLE SINGLETARY: If you absolutely need a job, keep looking. You know, obviously the types of jobs that teens tend to go for in retail, fast food, you know, they still need people. And oftentimes you know, listen, you may not get the job at the beginning of the summer, but hold on because in late summer, when some of those college students are going back and perhaps some of those people who lost their jobs are finding permanent positions with more pay, you might be able to still get that job. So don't give up, you may spend a month or month and a half without it, but go back to those retailers and those restaurants and those fast food places to see if they still need someone.\r\nALEX COHEN, host: And now I know parents like having the break from providing allowance. But, you know, is - are there reasons maybe not to push your teen towards taking a job?\r\nMICHELLE SINGLETARY: I think it absolutely is. In fact I think too many teens are working and they don't need to work. They're some who absolutely need, they're contributing to their household or they're putting money into their own college fund. But more often than not, what parents do is say you've got to get a job, and then the teens get the job and they spend all the money on clothes and you know videos and iPods and paying their cell phone bills because they don't need a cell phone anyway.\r\nALEX COHEN, host: So it's not going towards the college tuition at all.\r\nMICHELLE SINGLETARY: It is not. It's just disposable income that they're disposing of. And parents are not setting any limits and you know and then the kids get used to the fact that they're using all of their paycheck. That's another bad habit. Because they don't have to pay bills and all, all their income goes through you know this stuff.\r\nMICHELLE SINGLETARY: And when it comes time to get a real job, they're surprised they don't have enough money. And so you know what? You can wait to work. Instead, maybe they can spend the summer volunteering at a charitable organization or you know going back to school and boosting up their math skills or their English skills. We push the teens out into the market too soon, I think for some families.\r\nALEX COHEN, host: But now let's say your kid is working. What tips can parents provide in terms of holding on to that summer money?\r\nMICHELLE SINGLETARY: You know, before they get their job, they need to sit down with them and do a budget. So before they actually work and get that first paycheck I mean, you know, have them draw up a budge where the money is going. And you ought to have some requirements for some of their money. That's right, be a parent.\r\nMICHELLE SINGLETARY: So make them put some of it towards their college fund, if in fact they're headed for college. You know what? Make them put some away, I call it the tax fund, even though they may not have to pay taxes, but to pay for long-term things that they may want. You know, books once they get to college, or maybe they want to get a car, and they can actually pay cash for it, with some of these funds. Don't let them just go out and spend it on movies and stuff. You ought to set some guidelines - this is where you should put the money. And look at their budget.\r\nALEX COHEN, host: Day to Day's personal finance contributor Michelle Singletary. Thank you, Michelle!\r\nMICHELLE SINGLETARY: You're welcome.\r\nALEX COHEN, host: Stay with us. NPR's Day to Day continues.",
"summary": "The tight job market could be bad news for teens seeking summer work. If your teen does find a job, will he or she know how to manage those paychecks? Our personal finance contributor talks with Alex Cohen about ways to help teens find a job.",
"summary_de": "Der angespannte Arbeitsmarkt könnte für Jugendliche, die Sommerarbeit suchen, eine schlechte Nachricht sein. Wenn Ihr Teenager einen Job findet, wird er oder sie wissen, wie er mit diesen Gehaltsschecks umgeht? Unser Mitarbeiter für persönliche Finanzen spricht mit Alex Cohen darüber, wie Teenager bei der Jobsuche unterstützt werden können.",
"summary_zh": "紧张的就业市场对寻找暑期工作的青少年来说可能是个坏消息。如果你的孩子找到了一份工作，他/她懂得怎么管理这些薪水吗？我们的个人理财撰稿人与亚历克斯·科恩谈论如何帮助青少年找到工作。"
},
```
### Data Fields
- 'dialogue': An English dialogue
- 'summary': the original English summary of the corresponding dialogue (provided by MediaSum)
- 'summary_de': the human-translated German summary
- 'summary_zh': the human-translated Chinese summary
### Data Splits
- training set: 20K samples
- validation set: 10K samples
- testing set: 10K samples
## Dataset Creation
Please refer to [our paper](https://aclanthology.org/2022.emnlp-main.526/) for more details.
## Considerations for Using the Data
Please refer to [our paper](https://aclanthology.org/2022.emnlp-main.526/) for more details.
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/krystalan/ClidSum)
### Licensing Information
License: CC BY-NC-SA 4.0
### Citation Information
```
@inproceedings{wang-etal-2022-clidsum,
title = "{C}lid{S}um: A Benchmark Dataset for Cross-Lingual Dialogue Summarization",
author = "Wang, Jiaan and
Meng, Fandong and
Lu, Ziyao and
Zheng, Duo and
Li, Zhixu and
Qu, Jianfeng and
Zhou, Jie",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.526",
pages = "7716--7729",
abstract = "We present ClidSum, a benchmark dataset towards building cross-lingual summarization systems on dialogue documents. It consists of 67k+ dialogue documents and 112k+ annotated summaries in different target languages. Based on the proposed ClidSum, we introduce two benchmark settings for supervised and semi-supervised scenarios, respectively. We then build various baseline systems in different paradigms (pipeline and end-to-end) and conduct extensive experiments on ClidSum to provide deeper analyses. Furthermore, we propose mDialBART which extends mBART via further pre-training, where the multiple objectives help the pre-trained model capture the structural characteristics as well as key content in dialogues and the transformation from source to the target language. Experimental results show the superiority of mDialBART, as an end-to-end model, outperforms strong pipeline models on ClidSum. Finally, we discuss specific challenges that current approaches faced with this task and give multiple promising directions for future research. We have released the dataset and code at https://github.com/krystalan/ClidSum.",
}
```
### Contributions
Thanks to [@krystalan](https://github.com/krystalan) for adding this dataset. | GEM/xmediasum | [
"task_categories:summarization",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
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"language:en",
"language:zh",
"language:de",
"license:cc-by-nc-sa-4.0",
"region:us"
]
| 2023-02-15T14:01:13+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["en", "zh", "de"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": [], "pretty_name": "xmediasum", "tags": []} | 2023-02-15T14:01:56+00:00 | []
| [
"en",
"zh",
"de"
]
| TAGS
#task_categories-summarization #annotations_creators-expert-generated #language_creators-crowdsourced #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-English #language-Chinese #language-German #license-cc-by-nc-sa-4.0 #region-us
|
# Dataset Card for XMediaSum
### Dataset Summary
We present XMediaSum, a cross-lingual dialogue summarization dataset with 40K English(dialogues)->Chinese(summaries) and 40K English (dialogues)->German(summaries) samples. XMediaSum is created by manually translating the English summaries of MediaSum (a English monolingual dialogue summarization dataset) to both Chinese and German.
- Paper: ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization (EMNLP 2022)
- GitHub: URL
### Supported Task
- Cross-Lingual Summarization
- Cross-Lingual Dialogue Summarization
### Languages
- source language: English
- target language: Chinese and German
## Dataset Structure
### Data Instances
One example is given below in JSON format:
### Data Fields
- 'dialogue': An English dialogue
- 'summary': the original English summary of the corresponding dialogue (provided by MediaSum)
- 'summary_de': the human-translated German summary
- 'summary_zh': the human-translated Chinese summary
### Data Splits
- training set: 20K samples
- validation set: 10K samples
- testing set: 10K samples
## Dataset Creation
Please refer to our paper for more details.
## Considerations for Using the Data
Please refer to our paper for more details.
## Additional Information
### Dataset Curators
### Licensing Information
License: CC BY-NC-SA 4.0
### Contributions
Thanks to @krystalan for adding this dataset. | [
"# Dataset Card for XMediaSum",
"### Dataset Summary\r\n\r\nWe present XMediaSum, a cross-lingual dialogue summarization dataset with 40K English(dialogues)->Chinese(summaries) and 40K English (dialogues)->German(summaries) samples. XMediaSum is created by manually translating the English summaries of MediaSum (a English monolingual dialogue summarization dataset) to both Chinese and German.\r\n\r\n- Paper: ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization (EMNLP 2022)\r\n- GitHub: URL",
"### Supported Task\r\n\r\n- Cross-Lingual Summarization\r\n- Cross-Lingual Dialogue Summarization",
"### Languages\r\n\r\n- source language: English\r\n- target language: Chinese and German",
"## Dataset Structure",
"### Data Instances\r\nOne example is given below in JSON format:",
"### Data Fields\r\n\r\n- 'dialogue': An English dialogue\r\n- 'summary': the original English summary of the corresponding dialogue (provided by MediaSum)\r\n- 'summary_de': the human-translated German summary\r\n- 'summary_zh': the human-translated Chinese summary",
"### Data Splits\r\n- training set: 20K samples\r\n- validation set: 10K samples\r\n- testing set: 10K samples",
"## Dataset Creation\r\n\r\nPlease refer to our paper for more details.",
"## Considerations for Using the Data\r\n\r\nPlease refer to our paper for more details.",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\r\n\r\nLicense: CC BY-NC-SA 4.0",
"### Contributions\r\n\r\nThanks to @krystalan for adding this dataset."
]
| [
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"# Dataset Card for XMediaSum",
"### Dataset Summary\r\n\r\nWe present XMediaSum, a cross-lingual dialogue summarization dataset with 40K English(dialogues)->Chinese(summaries) and 40K English (dialogues)->German(summaries) samples. XMediaSum is created by manually translating the English summaries of MediaSum (a English monolingual dialogue summarization dataset) to both Chinese and German.\r\n\r\n- Paper: ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization (EMNLP 2022)\r\n- GitHub: URL",
"### Supported Task\r\n\r\n- Cross-Lingual Summarization\r\n- Cross-Lingual Dialogue Summarization",
"### Languages\r\n\r\n- source language: English\r\n- target language: Chinese and German",
"## Dataset Structure",
"### Data Instances\r\nOne example is given below in JSON format:",
"### Data Fields\r\n\r\n- 'dialogue': An English dialogue\r\n- 'summary': the original English summary of the corresponding dialogue (provided by MediaSum)\r\n- 'summary_de': the human-translated German summary\r\n- 'summary_zh': the human-translated Chinese summary",
"### Data Splits\r\n- training set: 20K samples\r\n- validation set: 10K samples\r\n- testing set: 10K samples",
"## Dataset Creation\r\n\r\nPlease refer to our paper for more details.",
"## Considerations for Using the Data\r\n\r\nPlease refer to our paper for more details.",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\r\n\r\nLicense: CC BY-NC-SA 4.0",
"### Contributions\r\n\r\nThanks to @krystalan for adding this dataset."
]
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|
5fe98b0c7725c5974e3f296c87d2b969c088f5eb | # Dataset Card for "trainval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rroyc20/trainval | [
"region:us"
]
| 2023-02-15T14:18:54+00:00 | {"dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "clean_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3933706, "num_examples": 42415}, {"name": "val", "num_bytes": 1691755, "num_examples": 18178}], "download_size": 3490856, "dataset_size": 5625461}} | 2023-02-15T14:46:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "trainval"
More Information needed | [
"# Dataset Card for \"trainval\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"trainval\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"trainval\"\n\nMore Information needed"
]
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|
3651b36b6337f3f8fdfa6991d5095e1ecbefab69 | # Dataset Card for "salvadoran-news-edh"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | justinian336/salvadoran-news-edh | [
"region:us"
]
| 2023-02-15T14:34:40+00:00 | {"dataset_info": {"features": [{"name": "image_src", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "category", "dtype": {"class_label": {"names": {"0": "fotogalerias", "1": "noticias", "2": "deportes/zona-mundialista", "3": "entretenimiento", "4": "vida", "5": "opinion", "7": "opinion/caricaturas", "8": "deportes", "9": "videos"}}}}, {"name": "link", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 196407515, "num_examples": 55345}], "download_size": 111585532, "dataset_size": 196407515}} | 2024-02-12T00:59:15+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "salvadoran-news-edh"
More Information needed | [
"# Dataset Card for \"salvadoran-news-edh\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"salvadoran-news-edh\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"salvadoran-news-edh\"\n\nMore Information needed"
]
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|
6986f37a1f34467621dd78a5b0263a7400133897 | # Dataset Card for "salvadoran-news-elmundo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | justinian336/salvadoran-news-elmundo | [
"region:us"
]
| 2023-02-15T14:38:15+00:00 | {"dataset_info": {"features": [{"name": "image_src", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "category", "dtype": {"class_label": {"names": {"0": "Tecnomundo", "1": "Guia Mundialista", "2": "Economia", "3": "Confidencial", "4": "Editorial", "5": "Politica", "6": "El Mundo", "7": "Nacionales"}}}}, {"name": "date", "dtype": "string"}, {"name": "link", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 89785805, "num_examples": 45983}], "download_size": 49911188, "dataset_size": 89785805}} | 2024-02-12T01:01:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "salvadoran-news-elmundo"
More Information needed | [
"# Dataset Card for \"salvadoran-news-elmundo\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"salvadoran-news-elmundo\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"salvadoran-news-elmundo\"\n\nMore Information needed"
]
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|
edd945d6e6ab10d339d7a847ee2f8bcd613d9d94 |
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | j-krzywdziak/test2 | [
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"language:pl",
"license:mit",
"region:us"
]
| 2023-02-15T14:53:13+00:00 | {"annotations_creators": ["expert-generated"], "language": ["pl"], "license": ["mit"], "multilinguality": ["monolingual"], "dataset_info": [{"config_name": "config", "features": [{"name": "audio_id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "text", "dtype": "string"}]}]} | 2023-02-17T13:13:40+00:00 | []
| [
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# Dataset Card for [Dataset Name]
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
Thanks to @github-username for adding this dataset. | [
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|
8b289705d3be6e47c53b7859a9c0f15b1112dac5 | # Dataset Card for "fer2013test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Piro17/fer2013test | [
"region:us"
]
| 2023-02-15T15:02:15+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "angry", "1": "disgust", "2": "fear", "3": "happy", "4": "neutral", "5": "sad", "6": "surprise"}}}}], "splits": [{"name": "train", "num_bytes": 11521798.802, "num_examples": 7178}], "download_size": 10231842, "dataset_size": 11521798.802}} | 2023-02-15T15:02:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "fer2013test"
More Information needed | [
"# Dataset Card for \"fer2013test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"fer2013test\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"fer2013test\"\n\nMore Information needed"
]
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|
4bccca2357674563484cd290ef21bfe7fe54ac1b |
This dataset contains the tokens for ImageNet* from the paper [Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation](https://arxiv.org/abs/2302.07865)
Download the tokens from the files page, or run:
```
wget https://huggingface.co/datasets/madrylab/imagenet-star-tokens/resolve/main/tokens.zip
``` | madrylab/imagenet-star-tokens | [
"license:mit",
"arxiv:2302.07865",
"region:us"
]
| 2023-02-15T15:06:23+00:00 | {"license": "mit"} | 2023-02-16T02:45:47+00:00 | [
"2302.07865"
]
| []
| TAGS
#license-mit #arxiv-2302.07865 #region-us
|
This dataset contains the tokens for ImageNet* from the paper Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation
Download the tokens from the files page, or run:
| []
| [
"TAGS\n#license-mit #arxiv-2302.07865 #region-us \n"
]
| [
19
]
| [
"passage: TAGS\n#license-mit #arxiv-2302.07865 #region-us \n"
]
| [
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|
314720d976222834b3fe09a215738299ba2abe3b |
# Dataset Card for FrenchMedMCQA : A French Multiple-Choice Question Answering Corpus for Medical domain
## Table of Contents
- [Dataset Card for FrenchMedMCQA : A French Multiple-Choice Question Answering Corpus for Medical domain](#dataset-card-for-frenchmedmcqa--a-french-multiple-choice-question-answering-corpus-for-medical-domain)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contact](#contact)
## Dataset Description
- **Homepage:** https://deft2023.univ-avignon.fr/
- **Repository:** https://deft2023.univ-avignon.fr/
- **Paper:** [FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain](https://hal.science/hal-03824241/document)
- **Leaderboard:** Coming soon
- **Point of Contact:** [Yanis LABRAK](mailto:[email protected])
### Dataset Summary
This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers.
Each instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s).
We also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.
### Supported Tasks and Leaderboards
Multiple-Choice Question Answering (MCQA)
### Languages
The questions and answers are available in French.
## Dataset Structure
### Data Instances
```json
{
"id": "1863462668476003678",
"question": "Parmi les propositions suivantes, laquelle (lesquelles) est (sont) exacte(s) ? Les chylomicrons plasmatiques :",
"answers": {
"a": "Sont plus riches en cholestérol estérifié qu'en triglycérides",
"b": "Sont synthétisés par le foie",
"c": "Contiennent de l'apolipoprotéine B48",
"d": "Contiennent de l'apolipoprotéine E",
"e": "Sont transformés par action de la lipoprotéine lipase"
},
"correct_answers": [
"c",
"d",
"e"
],
"subject_name": "pharmacie",
"type": "multiple"
}
```
### Data Fields
- `id` : a string question identifier for each example
- `question` : question text (a string)
- `answer_a` : Option A
- `answer_b` : Option B
- `answer_c` : Option C
- `answer_d` : Option D
- `answer_e` : Option E
- `correct_answers` : Correct options, i.e., A, D and E
- `choice_type` ({"single", "multiple"}): Question choice type.
- "single": Single-choice question, where each choice contains a single option.
- "multiple": Multi-choice question, where each choice contains a combination of multiple options.
### Data Splits
| # Answers | Training | Validation | Test | Total |
|:---------:|:--------:|:----------:|:----:|:-----:|
| 1 | 595 | 164 | 321 | 1,080 |
| 2 | 528 | 45 | 97 | 670 |
| 3 | 718 | 71 | 141 | 930 |
| 4 | 296 | 30 | 56 | 382 |
| 5 | 34 | 2 | 7 | 43 |
| Total | 2171 | 312 | 622 | 3,105 |
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The questions and their associated candidate answer(s) were collected from real French pharmacy exams on the remede website. Questions and answers were manually created by medical experts and used during examinations. The dataset is composed of 2,025 questions with multiple answers and 1,080 with a single one, for a total of 3,105 questions. Each instance of the dataset contains an identifier, a question, five options (labeled from A to E) and correct answer(s). The average question length is 14.17 tokens and the average answer length is 6.44 tokens. The vocabulary size is of 13k words, of which 3.8k are estimated medical domain-specific words (i.e. a word related to the medical field). We find an average of 2.49 medical domain-specific words in each question (17 % of the words) and 2 in each answer (36 % of the words). On average, a medical domain-specific word is present in 2 questions and in 8 answers.
### Personal and Sensitive Information
The corpora is free of personal or sensitive information.
## Additional Information
### Dataset Curators
The dataset was created by Labrak Yanis and Bazoge Adrien and Dufour Richard and Daille Béatrice and Gourraud Pierre-Antoine and Morin Emmanuel and Rouvier Mickael.
### Licensing Information
Apache 2.0
### Citation Information
If you find this useful in your research, please consider citing the dataset paper :
```latex
@inproceedings{labrak-etal-2022-frenchmedmcqa,
title = "{F}rench{M}ed{MCQA}: A {F}rench Multiple-Choice Question Answering Dataset for Medical domain",
author = "Labrak, Yanis and
Bazoge, Adrien and
Dufour, Richard and
Daille, Beatrice and
Gourraud, Pierre-Antoine and
Morin, Emmanuel and
Rouvier, Mickael",
booktitle = "Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.louhi-1.5",
pages = "41--46",
abstract = "This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers. Each instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s). We also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.",
}
```
### Contact
Thanks to contact [Yanis LABRAK](https://github.com/qanastek) for more information about this dataset.
| DEFT-2023/DEFT2023 | [
"task_categories:question-answering",
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1k<n<10k",
"source_datasets:original",
"language:fr",
"license:apache-2.0",
"region:us"
]
| 2023-02-15T15:23:14+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["fr"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["1k<n<10k"], "source_datasets": ["original"], "task_categories": ["question-answering", "multiple-choice"], "task_ids": ["multiple-choice-qa", "open-domain-qa"], "paperswithcode_id": "frenchmedmcqa", "pretty_name": "FrenchMedMCQA"} | 2023-05-28T16:37:23+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-question-answering #task_categories-multiple-choice #task_ids-multiple-choice-qa #task_ids-open-domain-qa #annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-monolingual #size_categories-1k<n<10k #source_datasets-original #language-French #license-apache-2.0 #region-us
| Dataset Card for FrenchMedMCQA : A French Multiple-Choice Question Answering Corpus for Medical domain
======================================================================================================
Table of Contents
-----------------
* Dataset Card for FrenchMedMCQA : A French Multiple-Choice Question Answering Corpus for Medical domain
+ Table of Contents
+ Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
+ Dataset Structure
- Data Instances
- Data Fields
- Data Splits
+ Dataset Creation
- Source Data
* Initial Data Collection and Normalization
- Personal and Sensitive Information
+ Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contact
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain
* Leaderboard: Coming soon
* Point of Contact: Yanis LABRAK
### Dataset Summary
This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers.
Each instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s).
We also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.
### Supported Tasks and Leaderboards
Multiple-Choice Question Answering (MCQA)
### Languages
The questions and answers are available in French.
Dataset Structure
-----------------
### Data Instances
### Data Fields
* 'id' : a string question identifier for each example
* 'question' : question text (a string)
* 'answer\_a' : Option A
* 'answer\_b' : Option B
* 'answer\_c' : Option C
* 'answer\_d' : Option D
* 'answer\_e' : Option E
* 'correct\_answers' : Correct options, i.e., A, D and E
* 'choice\_type' ({"single", "multiple"}): Question choice type.
+ "single": Single-choice question, where each choice contains a single option.
+ "multiple": Multi-choice question, where each choice contains a combination of multiple options.
### Data Splits
Dataset Creation
----------------
### Source Data
#### Initial Data Collection and Normalization
The questions and their associated candidate answer(s) were collected from real French pharmacy exams on the remede website. Questions and answers were manually created by medical experts and used during examinations. The dataset is composed of 2,025 questions with multiple answers and 1,080 with a single one, for a total of 3,105 questions. Each instance of the dataset contains an identifier, a question, five options (labeled from A to E) and correct answer(s). The average question length is 14.17 tokens and the average answer length is 6.44 tokens. The vocabulary size is of 13k words, of which 3.8k are estimated medical domain-specific words (i.e. a word related to the medical field). We find an average of 2.49 medical domain-specific words in each question (17 % of the words) and 2 in each answer (36 % of the words). On average, a medical domain-specific word is present in 2 questions and in 8 answers.
### Personal and Sensitive Information
The corpora is free of personal or sensitive information.
Additional Information
----------------------
### Dataset Curators
The dataset was created by Labrak Yanis and Bazoge Adrien and Dufour Richard and Daille Béatrice and Gourraud Pierre-Antoine and Morin Emmanuel and Rouvier Mickael.
### Licensing Information
Apache 2.0
If you find this useful in your research, please consider citing the dataset paper :
### Contact
Thanks to contact Yanis LABRAK for more information about this dataset.
| [
"### Dataset Summary\n\n\nThis paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers.\n\n\nEach instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s).\n\n\nWe also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.",
"### Supported Tasks and Leaderboards\n\n\nMultiple-Choice Question Answering (MCQA)",
"### Languages\n\n\nThe questions and answers are available in French.\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields\n\n\n* 'id' : a string question identifier for each example\n* 'question' : question text (a string)\n* 'answer\\_a' : Option A\n* 'answer\\_b' : Option B\n* 'answer\\_c' : Option C\n* 'answer\\_d' : Option D\n* 'answer\\_e' : Option E\n* 'correct\\_answers' : Correct options, i.e., A, D and E\n* 'choice\\_type' ({\"single\", \"multiple\"}): Question choice type.\n\t+ \"single\": Single-choice question, where each choice contains a single option.\n\t+ \"multiple\": Multi-choice question, where each choice contains a combination of multiple options.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe questions and their associated candidate answer(s) were collected from real French pharmacy exams on the remede website. Questions and answers were manually created by medical experts and used during examinations. The dataset is composed of 2,025 questions with multiple answers and 1,080 with a single one, for a total of 3,105 questions. Each instance of the dataset contains an identifier, a question, five options (labeled from A to E) and correct answer(s). The average question length is 14.17 tokens and the average answer length is 6.44 tokens. The vocabulary size is of 13k words, of which 3.8k are estimated medical domain-specific words (i.e. a word related to the medical field). We find an average of 2.49 medical domain-specific words in each question (17 % of the words) and 2 in each answer (36 % of the words). On average, a medical domain-specific word is present in 2 questions and in 8 answers.",
"### Personal and Sensitive Information\n\n\nThe corpora is free of personal or sensitive information.\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nThe dataset was created by Labrak Yanis and Bazoge Adrien and Dufour Richard and Daille Béatrice and Gourraud Pierre-Antoine and Morin Emmanuel and Rouvier Mickael.",
"### Licensing Information\n\n\nApache 2.0\n\n\nIf you find this useful in your research, please consider citing the dataset paper :",
"### Contact\n\n\nThanks to contact Yanis LABRAK for more information about this dataset."
]
| [
"TAGS\n#task_categories-question-answering #task_categories-multiple-choice #task_ids-multiple-choice-qa #task_ids-open-domain-qa #annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-monolingual #size_categories-1k<n<10k #source_datasets-original #language-French #license-apache-2.0 #region-us \n",
"### Dataset Summary\n\n\nThis paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers.\n\n\nEach instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s).\n\n\nWe also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.",
"### Supported Tasks and Leaderboards\n\n\nMultiple-Choice Question Answering (MCQA)",
"### Languages\n\n\nThe questions and answers are available in French.\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields\n\n\n* 'id' : a string question identifier for each example\n* 'question' : question text (a string)\n* 'answer\\_a' : Option A\n* 'answer\\_b' : Option B\n* 'answer\\_c' : Option C\n* 'answer\\_d' : Option D\n* 'answer\\_e' : Option E\n* 'correct\\_answers' : Correct options, i.e., A, D and E\n* 'choice\\_type' ({\"single\", \"multiple\"}): Question choice type.\n\t+ \"single\": Single-choice question, where each choice contains a single option.\n\t+ \"multiple\": Multi-choice question, where each choice contains a combination of multiple options.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe questions and their associated candidate answer(s) were collected from real French pharmacy exams on the remede website. Questions and answers were manually created by medical experts and used during examinations. The dataset is composed of 2,025 questions with multiple answers and 1,080 with a single one, for a total of 3,105 questions. Each instance of the dataset contains an identifier, a question, five options (labeled from A to E) and correct answer(s). The average question length is 14.17 tokens and the average answer length is 6.44 tokens. The vocabulary size is of 13k words, of which 3.8k are estimated medical domain-specific words (i.e. a word related to the medical field). We find an average of 2.49 medical domain-specific words in each question (17 % of the words) and 2 in each answer (36 % of the words). On average, a medical domain-specific word is present in 2 questions and in 8 answers.",
"### Personal and Sensitive Information\n\n\nThe corpora is free of personal or sensitive information.\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nThe dataset was created by Labrak Yanis and Bazoge Adrien and Dufour Richard and Daille Béatrice and Gourraud Pierre-Antoine and Morin Emmanuel and Rouvier Mickael.",
"### Licensing Information\n\n\nApache 2.0\n\n\nIf you find this useful in your research, please consider citing the dataset paper :",
"### Contact\n\n\nThanks to contact Yanis LABRAK for more information about this dataset."
]
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"passage: TAGS\n#task_categories-question-answering #task_categories-multiple-choice #task_ids-multiple-choice-qa #task_ids-open-domain-qa #annotations_creators-no-annotation #language_creators-expert-generated #multilinguality-monolingual #size_categories-1k<n<10k #source_datasets-original #language-French #license-apache-2.0 #region-us \n### Dataset Summary\n\n\nThis paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers.\n\n\nEach instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s).\n\n\nWe also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.### Supported Tasks and Leaderboards\n\n\nMultiple-Choice Question Answering (MCQA)### Languages\n\n\nThe questions and answers are available in French.\n\n\nDataset Structure\n-----------------### Data Instances"
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|
2d1967b67f938d206926effa200d115b5d29a672 | # Dataset Card for "model_cards_with_readmes_sections"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | davanstrien/model_cards_with_readmes_sections | [
"region:us"
]
| 2023-02-15T15:32:03+00:00 | {"dataset_info": {"features": [{"name": "license", "dtype": "string"}, {"name": "tags", "dtype": "string"}, {"name": "is_nc", "dtype": "bool"}, {"name": "readme_section", "dtype": "string"}, {"name": "hash", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28801782.8572217, "num_examples": 32124}], "download_size": 13668782, "dataset_size": 28801782.8572217}} | 2023-02-15T15:32:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "model_cards_with_readmes_sections"
More Information needed | [
"# Dataset Card for \"model_cards_with_readmes_sections\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"model_cards_with_readmes_sections\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"model_cards_with_readmes_sections\"\n\nMore Information needed"
]
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|
30a30d2ae61878baac354c5205ee7aa2d4a9e08d | # Dataset Card for "helpful-self-instruct-raw"
This dataset is derived from the `finetuning` subset of [Self-Instruct](https://github.com/yizhongw/self-instruct), with some light formatting to remove trailing spaces and `<|endoftext|>` tokens.
| HuggingFaceH4/helpful-self-instruct-raw | [
"license:apache-2.0",
"human-feedback",
"region:us"
]
| 2023-02-15T15:32:48+00:00 | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "demonstration", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20412870, "num_examples": 82612}], "download_size": 12532431, "dataset_size": 20412870}, "tags": ["human-feedback"]} | 2023-02-15T16:04:31+00:00 | []
| []
| TAGS
#license-apache-2.0 #human-feedback #region-us
| # Dataset Card for "helpful-self-instruct-raw"
This dataset is derived from the 'finetuning' subset of Self-Instruct, with some light formatting to remove trailing spaces and '<|endoftext|>' tokens.
| [
"# Dataset Card for \"helpful-self-instruct-raw\"\n\nThis dataset is derived from the 'finetuning' subset of Self-Instruct, with some light formatting to remove trailing spaces and '<|endoftext|>' tokens."
]
| [
"TAGS\n#license-apache-2.0 #human-feedback #region-us \n",
"# Dataset Card for \"helpful-self-instruct-raw\"\n\nThis dataset is derived from the 'finetuning' subset of Self-Instruct, with some light formatting to remove trailing spaces and '<|endoftext|>' tokens."
]
| [
19,
61
]
| [
"passage: TAGS\n#license-apache-2.0 #human-feedback #region-us \n# Dataset Card for \"helpful-self-instruct-raw\"\n\nThis dataset is derived from the 'finetuning' subset of Self-Instruct, with some light formatting to remove trailing spaces and '<|endoftext|>' tokens."
]
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|
f7cc5f972394c3e973e9844736853d9cf5e1a7ba | # Dataset Card for "GID"
## Dataset Description
- **Paper** [Land-cover classification with high-resolution remote sensing images using transferable deep models](https://www.sciencedirect.com/science/article/pii/S0034425719303414)
### Licensing Information
Public domain.
## Citation Information
[Land-cover classification with high-resolution remote sensing images using transferable deep models](https://www.sciencedirect.com/science/article/pii/S0034425719303414)
```
@article{GID2020,
title = {Land-cover classification with high-resolution remote sensing images using transferable deep models},
author = {Tong, Xin-Yi and Xia, Gui-Song and Lu, Qikai and Shen, Huanfeng and Li, Shengyang and You, Shucheng and Zhang, Liangpei},
year = 2020,
journal = {Remote Sensing of Environment},
volume = 237,
pages = 111322
}
``` | jonathan-roberts1/GID | [
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
]
| 2023-02-15T16:42:03+00:00 | {"license": "other", "task_categories": ["image-classification", "zero-shot-image-classification"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "arbor woodland", "1": "artificial grassland", "2": "dry cropland", "3": "garden plot", "4": "industrial land", "5": "irrigated land", "6": "lake", "7": "natural grassland", "8": "paddy field", "9": "pond", "10": "river", "11": "rural residential", "12": "shrub land", "13": "traffic land", "14": "urban residential"}}}}], "splits": [{"name": "train", "num_bytes": 1777210275, "num_examples": 30000}], "download_size": 1263253291, "dataset_size": 1777210275}} | 2023-03-31T14:38:31+00:00 | []
| []
| TAGS
#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us
| # Dataset Card for "GID"
## Dataset Description
- Paper Land-cover classification with high-resolution remote sensing images using transferable deep models
### Licensing Information
Public domain.
Land-cover classification with high-resolution remote sensing images using transferable deep models
| [
"# Dataset Card for \"GID\"",
"## Dataset Description\n\n- Paper Land-cover classification with high-resolution remote sensing images using transferable deep models",
"### Licensing Information\n\nPublic domain.\n\n\n\nLand-cover classification with high-resolution remote sensing images using transferable deep models"
]
| [
"TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us \n",
"# Dataset Card for \"GID\"",
"## Dataset Description\n\n- Paper Land-cover classification with high-resolution remote sensing images using transferable deep models",
"### Licensing Information\n\nPublic domain.\n\n\n\nLand-cover classification with high-resolution remote sensing images using transferable deep models"
]
| [
37,
9,
25,
28
]
| [
"passage: TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us \n# Dataset Card for \"GID\"## Dataset Description\n\n- Paper Land-cover classification with high-resolution remote sensing images using transferable deep models### Licensing Information\n\nPublic domain.\n\n\n\nLand-cover classification with high-resolution remote sensing images using transferable deep models"
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|
07773c7351ebfce04f666a9c9a165d1e0d59af36 | # Dataset Card for "CLRS"
## Dataset Description
- **Paper** [CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification](https://www.mdpi.com/1424-8220/20/4/1226/pdf)
-
### Licensing Information
For academic purposes.
## Citation Information
[CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification](https://www.mdpi.com/1424-8220/20/4/1226/pdf)
```
@article{s20041226,
title = {CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification},
author = {Li, Haifeng and Jiang, Hao and Gu, Xin and Peng, Jian and Li, Wenbo and Hong, Liang and Tao, Chao},
year = 2020,
journal = {Sensors},
volume = 20,
number = 4,
doi = {10.3390/s20041226},
issn = {1424-8220},
url = {https://www.mdpi.com/1424-8220/20/4/1226},
article-number = 1226,
pubmedid = 32102294,
}
``` | jonathan-roberts1/CLRS | [
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
]
| 2023-02-15T16:46:17+00:00 | {"license": "other", "task_categories": ["image-classification", "zero-shot-image-classification"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "airport", "1": "bare land", "2": "beach", "3": "bridge", "4": "commercial", "5": "desert", "6": "farmland", "7": "forest", "8": "golf course", "9": "highway", "10": "industrial", "11": "meadow", "12": "mountain", "13": "overpass", "14": "park", "15": "parking", "16": "playground", "17": "port", "18": "railway", "19": "railway station", "20": "residential", "21": "river", "22": "runway", "23": "stadium", "24": "storage tank"}}}}], "splits": [{"name": "train", "num_bytes": 2969926932, "num_examples": 15000}], "download_size": 2327956775, "dataset_size": 2969926932}} | 2023-03-31T14:35:22+00:00 | []
| []
| TAGS
#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us
| # Dataset Card for "CLRS"
## Dataset Description
- Paper CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification
-
### Licensing Information
For academic purposes.
CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification
| [
"# Dataset Card for \"CLRS\"",
"## Dataset Description\n\n- Paper CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification\n-",
"### Licensing Information\n\nFor academic purposes.\n\n\n\nCLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification"
]
| [
"TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us \n",
"# Dataset Card for \"CLRS\"",
"## Dataset Description\n\n- Paper CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification\n-",
"### Licensing Information\n\nFor academic purposes.\n\n\n\nCLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification"
]
| [
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9,
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28
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| [
"passage: TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us \n# Dataset Card for \"CLRS\"## Dataset Description\n\n- Paper CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification\n-### Licensing Information\n\nFor academic purposes.\n\n\n\nCLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification"
]
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|
a1657fd6bb5aab11fc2df492816f5f7f2e919025 | # Dataset Card for "streamlit-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | andfanilo/streamlit-issues | [
"region:us"
]
| 2023-02-15T16:50:20+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": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "labels", "list": [{"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "assignees", "list": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "milestone", "dtype": "null"}, {"name": "comments", "dtype": "int64"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "closed_at", "dtype": "timestamp[s]"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "null"}, {"name": "body", "dtype": "string"}, {"name": "reactions", "struct": [{"name": "url", "dtype": "string"}, {"name": "total_count", "dtype": "int64"}, {"name": "+1", "dtype": "int64"}, {"name": "-1", "dtype": "int64"}, {"name": "laugh", "dtype": "int64"}, {"name": "hooray", "dtype": "int64"}, {"name": "confused", "dtype": "int64"}, {"name": "heart", "dtype": "int64"}, {"name": "rocket", "dtype": "int64"}, {"name": "eyes", "dtype": "int64"}]}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "null"}, {"name": "state_reason", "dtype": "string"}, {"name": "draft", "dtype": "bool"}, {"name": "pull_request", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "diff_url", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}, {"name": "merged_at", "dtype": "timestamp[s]"}]}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 15843221, "num_examples": 5000}], "download_size": 3914406, "dataset_size": 15843221}} | 2023-02-15T16:50:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "streamlit-issues"
More Information needed | [
"# Dataset Card for \"streamlit-issues\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"streamlit-issues\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"streamlit-issues\"\n\nMore Information needed"
]
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|
ac05b361866a336d733c666b48bcc63c21b4c5cd | # Dataset Card for "cppe-5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | comet-team/cppe-5 | [
"region:us"
]
| 2023-02-15T17:12:20+00:00 | {"dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "Coverall", "1": "Face_Shield", "2": "Gloves", "3": "Goggles", "4": "Mask"}}}}]}], "splits": [{"name": "train", "num_bytes": 240463364.0, "num_examples": 1000}, {"name": "test", "num_bytes": 4172164.0, "num_examples": 29}], "download_size": 239989523, "dataset_size": 244635528.0}} | 2023-02-15T17:18:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cppe-5"
More Information needed | [
"# Dataset Card for \"cppe-5\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cppe-5\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cppe-5\"\n\nMore Information needed"
]
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|
8333b655cb9786ed6e68d58231ba348fc41c2e8a | # Dataset Card for "mastodon-instances"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | comet-team/mastodon-instances | [
"region:us"
]
| 2023-02-15T17:17:09+00:00 | {"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "short_description", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "uptime", "dtype": "float64"}, {"name": "up", "dtype": "bool"}, {"name": "https_score", "dtype": "int64"}, {"name": "https_rank", "dtype": "string"}, {"name": "ipv6", "dtype": "bool"}, {"name": "openRegistrations", "dtype": "bool"}, {"name": "users", "dtype": "int64"}, {"name": "statuses", "dtype": "string"}, {"name": "connections", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 816425, "num_examples": 1868}], "download_size": 536440, "dataset_size": 816425}} | 2023-02-15T17:17:15+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mastodon-instances"
More Information needed | [
"# Dataset Card for \"mastodon-instances\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mastodon-instances\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mastodon-instances\"\n\nMore Information needed"
]
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|
5ad1aa816cd428759634413f30a3b524f2ff2011 | # Dataset Card for "bmfr-finetuning-dictmed-med"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ell-hol/bmfr-finetuning-dictmed-med | [
"region:us"
]
| 2023-02-15T17:23:43+00:00 | {"dataset_info": {"features": [{"name": "bambara", "dtype": "string"}, {"name": "french", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 190972.0, "num_examples": 1539}, {"name": "test", "num_bytes": 52325.0, "num_examples": 192}], "download_size": 147976, "dataset_size": 243297.0}} | 2023-02-15T18:52:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bmfr-finetuning-dictmed-med"
More Information needed | [
"# Dataset Card for \"bmfr-finetuning-dictmed-med\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bmfr-finetuning-dictmed-med\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bmfr-finetuning-dictmed-med\"\n\nMore Information needed"
]
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|
fcf7de2af244520e06f424c6149235166a561642 |
# Dataset Card for incivility-arizona-daily-star-comments
This is a collection of more than 6000 comments on Arizona Daily Star news articles from 2011 that have been manually annotated for various forms of incivility including aspersion, namecalling, sarcasm, and vulgarity.
## Dataset Structure
Each instance in the dataset corresponds to a single comment from a single commenter.
An instance's `text` field contains the text of the comment with any quotes of other commenters removed.
The remaining fields in each instance provide binary labels for each type of incivility annotated:
`aspersion`, `hyperbole`, `lying`, `namecalling`, `noncooperation`, `offtopic`, `pejorative`, `sarcasm`, `vulgarity`, and `other_incivility`.
The dataset provides three standard splits: `train`, `validation`, and `test`.
## Dataset Creation
The original annotation effort is described in:
- Kevin Coe, Kate Kenski, Stephen A. Rains.
[Online and Uncivil? Patterns and Determinants of Incivility in Newspaper Website Comments](https://doi.org/10.1111/jcom.12104).
Journal of Communication, Volume 64, Issue 4, August 2014, Pages 658–679.
That dataset was converted to a computer-friendly form as described in section 4.2.1 of:
- Farig Sadeque.
[User behavior in social media: engagement, incivility, and depression](https://repository.arizona.edu/handle/10150/633192).
PhD thesis. The University of Arizona. 2019.
The current upload is a 2023 conversion of that form to a huggingface Dataset.
## Considerations for Using the Data
The data is intended for the study of incivility.
It should not be used to train models to generate incivility.
The human coders and their trainers were mostly [Western, educated, industrialized, rich and democratic (WEIRD)](https://www.nature.com/articles/466029a), which may have shaped how they evaluated incivility.
## Citation
```bibtex
@article{10.1111/jcom.12104,
author = {Coe, Kevin and Kenski, Kate and Rains, Stephen A.},
title = {Online and Uncivil? Patterns and Determinants of Incivility in Newspaper Website Comments},
journal = {Journal of Communication},
volume = {64},
number = {4},
pages = {658-679},
year = {2014},
month = {06},
issn = {0021-9916},
doi = {10.1111/jcom.12104},
url = {https://doi.org/10.1111/jcom.12104},
}
``` | civility-lab/incivility-arizona-daily-star-comments | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"social media",
"incivility",
"aspersion",
"hyperbole",
"lying",
"namecalling",
"noncooperation",
"pejorative",
"sarcasm",
"vulgarity",
"region:us"
]
| 2023-02-15T18:25:12+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-label-classification"], "pretty_name": "Incivility in Arizona Daily Star Comments", "tags": ["social media", "incivility", "aspersion", "hyperbole", "lying", "namecalling", "noncooperation", "pejorative", "sarcasm", "vulgarity"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "aspersion", "dtype": "int64"}, {"name": "hyperbole", "dtype": "int64"}, {"name": "lying", "dtype": "int64"}, {"name": "namecalling", "dtype": "int64"}, {"name": "noncooperation", "dtype": "int64"}, {"name": "offtopic", "dtype": "int64"}, {"name": "other_incivility", "dtype": "int64"}, {"name": "pejorative", "dtype": "int64"}, {"name": "sarcasm", "dtype": "int64"}, {"name": "vulgarity", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1568771, "num_examples": 3910}, {"name": "validation", "num_bytes": 398667, "num_examples": 976}, {"name": "test", "num_bytes": 486262, "num_examples": 1228}], "download_size": 1400753, "dataset_size": 2453700}} | 2023-02-15T23:18:17+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_ids-multi-label-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-apache-2.0 #social media #incivility #aspersion #hyperbole #lying #namecalling #noncooperation #pejorative #sarcasm #vulgarity #region-us
|
# Dataset Card for incivility-arizona-daily-star-comments
This is a collection of more than 6000 comments on Arizona Daily Star news articles from 2011 that have been manually annotated for various forms of incivility including aspersion, namecalling, sarcasm, and vulgarity.
## Dataset Structure
Each instance in the dataset corresponds to a single comment from a single commenter.
An instance's 'text' field contains the text of the comment with any quotes of other commenters removed.
The remaining fields in each instance provide binary labels for each type of incivility annotated:
'aspersion', 'hyperbole', 'lying', 'namecalling', 'noncooperation', 'offtopic', 'pejorative', 'sarcasm', 'vulgarity', and 'other_incivility'.
The dataset provides three standard splits: 'train', 'validation', and 'test'.
## Dataset Creation
The original annotation effort is described in:
- Kevin Coe, Kate Kenski, Stephen A. Rains.
Online and Uncivil? Patterns and Determinants of Incivility in Newspaper Website Comments.
Journal of Communication, Volume 64, Issue 4, August 2014, Pages 658–679.
That dataset was converted to a computer-friendly form as described in section 4.2.1 of:
- Farig Sadeque.
User behavior in social media: engagement, incivility, and depression.
PhD thesis. The University of Arizona. 2019.
The current upload is a 2023 conversion of that form to a huggingface Dataset.
## Considerations for Using the Data
The data is intended for the study of incivility.
It should not be used to train models to generate incivility.
The human coders and their trainers were mostly Western, educated, industrialized, rich and democratic (WEIRD), which may have shaped how they evaluated incivility.
| [
"# Dataset Card for incivility-arizona-daily-star-comments\n\nThis is a collection of more than 6000 comments on Arizona Daily Star news articles from 2011 that have been manually annotated for various forms of incivility including aspersion, namecalling, sarcasm, and vulgarity.",
"## Dataset Structure\n\nEach instance in the dataset corresponds to a single comment from a single commenter.\n\nAn instance's 'text' field contains the text of the comment with any quotes of other commenters removed.\nThe remaining fields in each instance provide binary labels for each type of incivility annotated:\n'aspersion', 'hyperbole', 'lying', 'namecalling', 'noncooperation', 'offtopic', 'pejorative', 'sarcasm', 'vulgarity', and 'other_incivility'.\n\nThe dataset provides three standard splits: 'train', 'validation', and 'test'.",
"## Dataset Creation\n\nThe original annotation effort is described in:\n\n- Kevin Coe, Kate Kenski, Stephen A. Rains.\n Online and Uncivil? Patterns and Determinants of Incivility in Newspaper Website Comments.\n Journal of Communication, Volume 64, Issue 4, August 2014, Pages 658–679.\n\nThat dataset was converted to a computer-friendly form as described in section 4.2.1 of:\n\n- Farig Sadeque.\n User behavior in social media: engagement, incivility, and depression.\n PhD thesis. The University of Arizona. 2019.\n\nThe current upload is a 2023 conversion of that form to a huggingface Dataset.",
"## Considerations for Using the Data\n\nThe data is intended for the study of incivility.\nIt should not be used to train models to generate incivility.\n\nThe human coders and their trainers were mostly Western, educated, industrialized, rich and democratic (WEIRD), which may have shaped how they evaluated incivility."
]
| [
"TAGS\n#task_categories-text-classification #task_ids-multi-label-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-apache-2.0 #social media #incivility #aspersion #hyperbole #lying #namecalling #noncooperation #pejorative #sarcasm #vulgarity #region-us \n",
"# Dataset Card for incivility-arizona-daily-star-comments\n\nThis is a collection of more than 6000 comments on Arizona Daily Star news articles from 2011 that have been manually annotated for various forms of incivility including aspersion, namecalling, sarcasm, and vulgarity.",
"## Dataset Structure\n\nEach instance in the dataset corresponds to a single comment from a single commenter.\n\nAn instance's 'text' field contains the text of the comment with any quotes of other commenters removed.\nThe remaining fields in each instance provide binary labels for each type of incivility annotated:\n'aspersion', 'hyperbole', 'lying', 'namecalling', 'noncooperation', 'offtopic', 'pejorative', 'sarcasm', 'vulgarity', and 'other_incivility'.\n\nThe dataset provides three standard splits: 'train', 'validation', and 'test'.",
"## Dataset Creation\n\nThe original annotation effort is described in:\n\n- Kevin Coe, Kate Kenski, Stephen A. Rains.\n Online and Uncivil? Patterns and Determinants of Incivility in Newspaper Website Comments.\n Journal of Communication, Volume 64, Issue 4, August 2014, Pages 658–679.\n\nThat dataset was converted to a computer-friendly form as described in section 4.2.1 of:\n\n- Farig Sadeque.\n User behavior in social media: engagement, incivility, and depression.\n PhD thesis. The University of Arizona. 2019.\n\nThe current upload is a 2023 conversion of that form to a huggingface Dataset.",
"## Considerations for Using the Data\n\nThe data is intended for the study of incivility.\nIt should not be used to train models to generate incivility.\n\nThe human coders and their trainers were mostly Western, educated, industrialized, rich and democratic (WEIRD), which may have shaped how they evaluated incivility."
]
| [
129,
68,
160,
138,
73
]
| [
"passage: TAGS\n#task_categories-text-classification #task_ids-multi-label-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-apache-2.0 #social media #incivility #aspersion #hyperbole #lying #namecalling #noncooperation #pejorative #sarcasm #vulgarity #region-us \n# Dataset Card for incivility-arizona-daily-star-comments\n\nThis is a collection of more than 6000 comments on Arizona Daily Star news articles from 2011 that have been manually annotated for various forms of incivility including aspersion, namecalling, sarcasm, and vulgarity.## Dataset Structure\n\nEach instance in the dataset corresponds to a single comment from a single commenter.\n\nAn instance's 'text' field contains the text of the comment with any quotes of other commenters removed.\nThe remaining fields in each instance provide binary labels for each type of incivility annotated:\n'aspersion', 'hyperbole', 'lying', 'namecalling', 'noncooperation', 'offtopic', 'pejorative', 'sarcasm', 'vulgarity', and 'other_incivility'.\n\nThe dataset provides three standard splits: 'train', 'validation', and 'test'.## Dataset Creation\n\nThe original annotation effort is described in:\n\n- Kevin Coe, Kate Kenski, Stephen A. Rains.\n Online and Uncivil? Patterns and Determinants of Incivility in Newspaper Website Comments.\n Journal of Communication, Volume 64, Issue 4, August 2014, Pages 658–679.\n\nThat dataset was converted to a computer-friendly form as described in section 4.2.1 of:\n\n- Farig Sadeque.\n User behavior in social media: engagement, incivility, and depression.\n PhD thesis. The University of Arizona. 2019.\n\nThe current upload is a 2023 conversion of that form to a huggingface Dataset."
]
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|
78a5b42b2450e91b71e4c8bb7f4972f35237524d | # Dataset Card for "resd_annotated"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Aniemore/resd_annotated | [
"task_categories:audio-classification",
"size_categories:1K<n<10K",
"language:ru",
"license:mit",
"voice",
"emotions",
"annotated",
"classification",
"doi:10.57967/hf/1272",
"region:us"
]
| 2023-02-15T20:00:40+00:00 | {"language": "ru", "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["audio-classification"], "pretty_name": "RESD", "dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "speech", "dtype": "audio"}, {"name": "text", "dtype": "string"}, {"name": "emotion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 398878916.336, "num_examples": 1116}, {"name": "test", "num_bytes": 96643276, "num_examples": 280}], "download_size": 485513605, "dataset_size": 495522192.336}, "tags": ["voice", "emotions", "annotated", "classification"]} | 2023-07-14T06:59:51+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-audio-classification #size_categories-1K<n<10K #language-Russian #license-mit #voice #emotions #annotated #classification #doi-10.57967/hf/1272 #region-us
| # Dataset Card for "resd_annotated"
More Information needed | [
"# Dataset Card for \"resd_annotated\"\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-audio-classification #size_categories-1K<n<10K #language-Russian #license-mit #voice #emotions #annotated #classification #doi-10.57967/hf/1272 #region-us \n",
"# Dataset Card for \"resd_annotated\"\n\nMore Information needed"
]
| [
65,
16
]
| [
"passage: TAGS\n#task_categories-audio-classification #size_categories-1K<n<10K #language-Russian #license-mit #voice #emotions #annotated #classification #doi-10.57967/hf/1272 #region-us \n# Dataset Card for \"resd_annotated\"\n\nMore Information needed"
]
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|
30340cd1cb5084bec7ffd7d622220676b88bc8a6 | # Dataset Card for "VALUE_wikitext103_lexical"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_wikitext103_lexical | [
"region:us"
]
| 2023-02-15T21:23:07+00:00 | {"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 1246241, "num_examples": 1871}, {"name": "train", "num_bytes": 522253807, "num_examples": 756745}, {"name": "validation", "num_bytes": 1085449, "num_examples": 1603}], "download_size": 311609435, "dataset_size": 524585497}} | 2023-02-15T21:23:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_wikitext103_lexical"
More Information needed | [
"# Dataset Card for \"VALUE_wikitext103_lexical\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_wikitext103_lexical\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext103_lexical\"\n\nMore Information needed"
]
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410184b25871c6fb918595b300f69c1ee21f8931 |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
| HuggingFaceH4/pmp-se-test-dataset | [
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-02-15T22:00:29+00:00 | {"license": "cc-by-sa-4.0", "dataset_info": {"features": [{"name": "qid", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "answers", "list": [{"name": "AnswerID", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "pm_score", "dtype": "int64"}, {"name": "selected", "dtype": "bool"}, {"name": "Author", "dtype": "string"}, {"name": "AuthorID", "dtype": "int64"}, {"name": "AuthorProfile", "dtype": "string"}]}, {"name": "date", "dtype": "string"}, {"name": "metadata", "dtype": "string"}]}} | 2023-02-16T00:06:13+00:00 | []
| []
| TAGS
#license-cc-by-sa-4.0 #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
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17,
8,
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|
9048b8414e4281b9a0c3c4d54eece3603996714c | # Dataset Card for Cocktail Recipes
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
## Dataset Description
### Dataset Summary
Cocktail Recipes Dataset for Semi-Structured Text Generation.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
```json
{"title": "Final Ward",
"ingredients": ["0.75 oz. Rye Whiskey",
"0.75 oz. Lemon Juice",
"0.75 oz. Maraschino Liqueur",
"0.75 oz. Green Chartreuse"],
"directions": ["shake on ice and strain"],
"misc":[],
"source": "Death & Co.",
"ner":["whiskey",
"chartreuse",
"maraschino liqueur"]}
```
### Data Fields
- `title` (`str`): Title of the recipe.
- `ingredients` (`list` of `str`): Ingredients.
- `directions` (`list` of `str`): Instruction steps.
- `source` (`str`): Origin of each recipe
- `ner` (`list` of `str`): NER entities.
### Data Splits
The dataset contains a single `train` split.
## Dataset Creation
[More Information Needed]
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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]
| brianarbuckle/cocktail_recipes | [
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-retrieval",
"task_categories:summarization",
"task_ids:document-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:explanation-generation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
]
| 2023-02-15T22:01:34+00:00 | {"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text2text-generation", "text-generation", "fill-mask", "text-retrieval", "summarization"], "task_ids": ["document-retrieval", "entity-linking-retrieval", "explanation-generation", "language-modeling", "masked-language-modeling"], "pretty_name": "Cocktail Recipes", "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "ingredients", "sequence": "string"}, {"name": "directions", "sequence": "string"}, {"name": "misc", "sequence": "string"}, {"name": "source", "dtype": "string"}, {"name": "ner", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 301501, "num_examples": 875}], "download_size": 96915, "dataset_size": 301501}} | 2023-02-28T04:14:39+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text2text-generation #task_categories-text-generation #task_categories-fill-mask #task_categories-text-retrieval #task_categories-summarization #task_ids-document-retrieval #task_ids-entity-linking-retrieval #task_ids-explanation-generation #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-unknown #region-us
| # Dataset Card for Cocktail Recipes
## Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
## Dataset Description
### Dataset Summary
Cocktail Recipes Dataset for Semi-Structured Text Generation.
### Supported Tasks and Leaderboards
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
### Data Fields
- 'title' ('str'): Title of the recipe.
- 'ingredients' ('list' of 'str'): Ingredients.
- 'directions' ('list' of 'str'): Instruction steps.
- 'source' ('str'): Origin of each recipe
- 'ner' ('list' of 'str'): NER entities.
### Data Splits
The dataset contains a single 'train' split.
## 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
| [
"# Dataset Card for Cocktail Recipes",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations",
"## Dataset Description",
"### Dataset Summary\n\nCocktail Recipes Dataset for Semi-Structured Text Generation.",
"### Supported Tasks and Leaderboards",
"### Languages\n\nThe dataset is in English.",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n- 'title' ('str'): Title of the recipe.\n- 'ingredients' ('list' of 'str'): Ingredients.\n- 'directions' ('list' of 'str'): Instruction steps.\n- 'source' ('str'): Origin of each recipe\n- 'ner' ('list' of 'str'): NER entities.",
"### Data Splits\n\nThe dataset contains a single 'train' split.",
"## 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"
]
| [
"TAGS\n#task_categories-text2text-generation #task_categories-text-generation #task_categories-fill-mask #task_categories-text-retrieval #task_categories-summarization #task_ids-document-retrieval #task_ids-entity-linking-retrieval #task_ids-explanation-generation #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-unknown #region-us \n",
"# Dataset Card for Cocktail Recipes",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations",
"## Dataset Description",
"### Dataset Summary\n\nCocktail Recipes Dataset for Semi-Structured Text Generation.",
"### Supported Tasks and Leaderboards",
"### Languages\n\nThe dataset is in English.",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n- 'title' ('str'): Title of the recipe.\n- 'ingredients' ('list' of 'str'): Ingredients.\n- 'directions' ('list' of 'str'): Instruction steps.\n- 'source' ('str'): Origin of each recipe\n- 'ner' ('list' of 'str'): NER entities.",
"### Data Splits\n\nThe dataset contains a single 'train' split.",
"## 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"
]
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| [
"passage: TAGS\n#task_categories-text2text-generation #task_categories-text-generation #task_categories-fill-mask #task_categories-text-retrieval #task_categories-summarization #task_ids-document-retrieval #task_ids-entity-linking-retrieval #task_ids-explanation-generation #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-n<1K #source_datasets-original #language-English #license-unknown #region-us \n# Dataset Card for Cocktail Recipes## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations## Dataset Description### Dataset Summary\n\nCocktail Recipes Dataset for Semi-Structured Text Generation.### Supported Tasks and Leaderboards### Languages\n\nThe dataset is in English.## Dataset Structure### Data Instances### Data Fields\n\n- 'title' ('str'): Title of the recipe.\n- 'ingredients' ('list' of 'str'): Ingredients.\n- 'directions' ('list' of 'str'): Instruction steps.\n- 'source' ('str'): Origin of each recipe\n- 'ner' ('list' of 'str'): NER entities.### Data Splits\n\nThe dataset contains a single 'train' split.## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?"
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|
26c456a2d394d66a0415fa87d1df5a036bec8414 |
Inital example files to test an easy way to store and manage data text and images.
Created from python scripts available at https://github.com/mediocreatmybest/gaslightingeveryone/tree/main/tools
Creation script: https://github.com/mediocreatmybest/gaslightingeveryone/blob/main/tools/images2parq.py
Extraction script: https://github.com/mediocreatmybest/gaslightingeveryone/blob/main/tools/parq2folder.py
| Mediocreatmybest/Example | [
"license:cc0-1.0",
"region:us"
]
| 2023-02-15T23:54:05+00:00 | {"license": "cc0-1.0"} | 2023-02-16T00:11:05+00:00 | []
| []
| TAGS
#license-cc0-1.0 #region-us
|
Inital example files to test an easy way to store and manage data text and images.
Created from python scripts available at URL
Creation script: URL
Extraction script: URL
| []
| [
"TAGS\n#license-cc0-1.0 #region-us \n"
]
| [
14
]
| [
"passage: TAGS\n#license-cc0-1.0 #region-us \n"
]
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|
7d2be385f615d2791da434b97293aece089c9d94 | # Dataset Card for "french-snli"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sugam11/french-snli | [
"region:us"
]
| 2023-02-16T01:16:49+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": 2296826, "num_examples": 10000}, {"name": "train", "num_bytes": 122642216, "num_examples": 550152}, {"name": "validation", "num_bytes": 2303892, "num_examples": 10000}], "download_size": 40422094, "dataset_size": 127242934}} | 2023-02-16T01:17:44+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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|
b2b12c4340dedd7dbd57052caca7007eb230f8fb | # Dataset Card for "c4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lsb/c4 | [
"region:us"
]
| 2023-02-16T01:29:19+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "timestamp", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 828588742863, "num_examples": 364868892}, {"name": "validation", "num_bytes": 825766822, "num_examples": 364608}], "download_size": 511302989842, "dataset_size": 829414509685}} | 2023-02-16T19:40:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "c4"
More Information needed | [
"# Dataset Card for \"c4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"c4\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"c4\"\n\nMore Information needed"
]
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|
277382fd035cabc0b744c1c5e49e79761ad31420 | # Dataset Card for "VALUE_wikitext103_uninflect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_wikitext103_uninflect | [
"region:us"
]
| 2023-02-16T03:31:56+00:00 | {"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 849818, "num_examples": 1118}, {"name": "train", "num_bytes": 368188365, "num_examples": 469979}, {"name": "validation", "num_bytes": 796548, "num_examples": 1053}], "download_size": 215340313, "dataset_size": 369834731}} | 2023-02-16T03:32:05+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_wikitext103_uninflect"
More Information needed | [
"# Dataset Card for \"VALUE_wikitext103_uninflect\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_wikitext103_uninflect\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_wikitext103_uninflect\"\n\nMore Information needed"
]
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|
5b7cd23dc3363409b775c77e79d855d02c68b46e | # Dataset Card for "affectnethq"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Piro17/affectnethq | [
"region:us"
]
| 2023-02-16T06:47:30+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "anger", "1": "disgust", "2": "fear", "3": "happy", "4": "neutral", "5": "sad", "6": "surprise"}}}}], "splits": [{"name": "train", "num_bytes": 5858852632.634, "num_examples": 27823}], "download_size": 0, "dataset_size": 5858852632.634}} | 2023-02-16T06:56:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "affectnethq"
More Information needed | [
"# Dataset Card for \"affectnethq\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"affectnethq\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"affectnethq\"\n\nMore Information needed"
]
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|
6a5f4275917093f9e8c5593120965ae065cbe459 |
# Dataset Card for SRSD-Feynman (Easy set with Dummy Variables)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/omron-sinicx/srsd-benchmark
- **Paper:** [Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery](https://arxiv.org/abs/2206.10540)
- **Point of Contact:** [Yoshitaka Ushiku](mailto:[email protected])
### Dataset Summary
Our SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.
We carefully reviewed the properties of each formula and its variables in [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html) to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.
This is the ***Easy set with dummy variables*** of our SRSD-Feynman datasets, which consists of the following 30 different physics formulas:
[](https://huggingface.co/datasets/yoshitomo-matsubara/srsd-feynman_easy_dummy/resolve/main/problem_table.pdf)
Dummy variables were randomly generated, and symbolic regression models should not use the dummy variables as part of their predictions.
The following datasets contain
**1 dummy variable**: I.12.1, I.12.4, I.12.5, I.18.12, I.25.13, I.47.23
**2 dummy variables**: I.14.3, I.18.16, I.43.16, II.3.24, II.8.31, II.10.9, II.13.17, II.15.5, II.27.18, III.7.38, III.12.43
**3 dummy variables**: I.14.4, I.26.2, I.27.6, I.30.5, II.2.42, II.4.23, II.15.4, II.27.16, II.34.11, II.34.29b, II.38.3, II.38.14, III.15.27
More details of these datasets are provided in [the paper and its supplementary material](https://arxiv.org/abs/2206.10540).
### Supported Tasks and Leaderboards
Symbolic Regression
## Dataset Structure
### Data Instances
Tabular data + Ground-truth equation per equation
Tabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.
Note that the number of variables (`num_variables`) varies from equation to equation.
Ground-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.
### Data Fields
For each dataset, we have
1. train split (txt file, whitespace as a delimiter)
2. val split (txt file, whitespace as a delimiter)
3. test split (txt file, whitespace as a delimiter)
4. true equation (pickle file for sympy object)
### Data Splits
- train: 8,000 samples per equation
- val: 1,000 samples per equation
- test: 1,000 samples per equation
## Dataset Creation
### Curation Rationale
We chose target equations based on [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html).
### Annotations
#### Annotation process
We significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.
First, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.
Next, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.
In cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.
Generally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.
Variables such as angles, for which a linear distribution is expected are set to be sampled uniformly.
In addition, variables that take a specific sign were set to be sampled within that range.
#### Who are the annotators?
The main annotators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
We annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.
### Discussion of Biases
Our choices of target equations are based on [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html), which are focused on a field of Physics.
### Other Known Limitations
Some variables used in our datasets indicate some numbers (counts), which should be treated as integer.
Due to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})
## Additional Information
### Dataset Curators
The main curators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
### Licensing Information
Creative Commons Attribution 4.0
### Citation Information
[[Preprint](https://arxiv.org/abs/2206.10540)]
```bibtex
@article{matsubara2022rethinking,
title={Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery},
author={Matsubara, Yoshitomo and Chiba, Naoya and Igarashi, Ryo and Ushiku, Yoshitaka},
journal={arXiv preprint arXiv:2206.10540},
year={2022}
}
```
### Contributions
Authors:
- Yoshitomo Matsubara (@yoshitomo-matsubara)
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
- Yoshitaka Ushiku (@yushiku)
| yoshitomo-matsubara/srsd-feynman_easy_dummy | [
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"license:cc-by-4.0",
"arxiv:2206.10540",
"doi:10.57967/hf/0760",
"region:us"
]
| 2023-02-16T06:56:39+00:00 | {"annotations_creators": ["expert"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended"], "task_categories": ["tabular-regression"], "task_ids": [], "pretty_name": "SRSD-Feynman (Easy w/ Dummy Variables)"} | 2023-10-11T01:07:46+00:00 | [
"2206.10540"
]
| [
"en"
]
| TAGS
#task_categories-tabular-regression #annotations_creators-expert #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended #language-English #license-cc-by-4.0 #arxiv-2206.10540 #doi-10.57967/hf/0760 #region-us
|
# Dataset Card for SRSD-Feynman (Easy set with Dummy Variables)
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Homepage:
- Repository: URL
- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
- Point of Contact: Yoshitaka Ushiku
### Dataset Summary
Our SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.
We carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.
This is the *Easy set with dummy variables* of our SRSD-Feynman datasets, which consists of the following 30 different physics formulas:
, where the last (rightmost) column indicate output of the target function for given variables.
Note that the number of variables ('num_variables') varies from equation to equation.
Ground-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.
### Data Fields
For each dataset, we have
1. train split (txt file, whitespace as a delimiter)
2. val split (txt file, whitespace as a delimiter)
3. test split (txt file, whitespace as a delimiter)
4. true equation (pickle file for sympy object)
### Data Splits
- train: 8,000 samples per equation
- val: 1,000 samples per equation
- test: 1,000 samples per equation
## Dataset Creation
### Curation Rationale
We chose target equations based on the Feynman Symbolic Regression Database.
### Annotations
#### Annotation process
We significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.
First, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.
Next, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.
In cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.
Generally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.
Variables such as angles, for which a linear distribution is expected are set to be sampled uniformly.
In addition, variables that take a specific sign were set to be sampled within that range.
#### Who are the annotators?
The main annotators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
We annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.
### Discussion of Biases
Our choices of target equations are based on the Feynman Symbolic Regression Database, which are focused on a field of Physics.
### Other Known Limitations
Some variables used in our datasets indicate some numbers (counts), which should be treated as integer.
Due to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})
## Additional Information
### Dataset Curators
The main curators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
### Licensing Information
Creative Commons Attribution 4.0
[Preprint]
### Contributions
Authors:
- Yoshitomo Matsubara (@yoshitomo-matsubara)
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
- Yoshitaka Ushiku (@yushiku)
| [
"# Dataset Card for SRSD-Feynman (Easy set with Dummy Variables)",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery\n- Point of Contact: Yoshitaka Ushiku",
"### Dataset Summary\n\nOur SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.\nWe carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.\n\nThis is the *Easy set with dummy variables* of our SRSD-Feynman datasets, which consists of the following 30 different physics formulas:\n\n![Click here to open a PDF file](URL\n \nDummy variables were randomly generated, and symbolic regression models should not use the dummy variables as part of their predictions.\n\nThe following datasets contain \n1 dummy variable: I.12.1, I.12.4, I.12.5, I.18.12, I.25.13, I.47.23\n\n2 dummy variables: I.14.3, I.18.16, I.43.16, II.3.24, II.8.31, II.10.9, II.13.17, II.15.5, II.27.18, III.7.38, III.12.43\n\n3 dummy variables: I.14.4, I.26.2, I.27.6, I.30.5, II.2.42, II.4.23, II.15.4, II.27.16, II.34.11, II.34.29b, II.38.3, II.38.14, III.15.27\n\nMore details of these datasets are provided in the paper and its supplementary material.",
"### Supported Tasks and Leaderboards\n\nSymbolic Regression",
"## Dataset Structure",
"### Data Instances\n\nTabular data + Ground-truth equation per equation\n\nTabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.\nNote that the number of variables ('num_variables') varies from equation to equation.\n \nGround-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.",
"### Data Fields\n\nFor each dataset, we have \n1. train split (txt file, whitespace as a delimiter)\n2. val split (txt file, whitespace as a delimiter)\n3. test split (txt file, whitespace as a delimiter)\n4. true equation (pickle file for sympy object)",
"### Data Splits\n\n- train: 8,000 samples per equation\n- val: 1,000 samples per equation\n- test: 1,000 samples per equation",
"## Dataset Creation",
"### Curation Rationale\n\nWe chose target equations based on the Feynman Symbolic Regression Database.",
"### Annotations",
"#### Annotation process\n\nWe significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.\nFirst, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.\nNext, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.\nIn cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.\nGenerally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.\nVariables such as angles, for which a linear distribution is expected are set to be sampled uniformly.\nIn addition, variables that take a specific sign were set to be sampled within that range.",
"#### Who are the annotators?\n\nThe main annotators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)",
"### Personal and Sensitive Information\n\nN/A",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nWe annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.",
"### Discussion of Biases\n\nOur choices of target equations are based on the Feynman Symbolic Regression Database, which are focused on a field of Physics.",
"### Other Known Limitations\n\nSome variables used in our datasets indicate some numbers (counts), which should be treated as integer.\nDue to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})",
"## Additional Information",
"### Dataset Curators\n\nThe main curators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)",
"### Licensing Information\n\nCreative Commons Attribution 4.0\n\n\n\n[Preprint]",
"### Contributions\n\nAuthors:\n- Yoshitomo Matsubara (@yoshitomo-matsubara)\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)\n- Yoshitaka Ushiku (@yushiku)"
]
| [
"TAGS\n#task_categories-tabular-regression #annotations_creators-expert #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended #language-English #license-cc-by-4.0 #arxiv-2206.10540 #doi-10.57967/hf/0760 #region-us \n",
"# Dataset Card for SRSD-Feynman (Easy set with Dummy Variables)",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery\n- Point of Contact: Yoshitaka Ushiku",
"### Dataset Summary\n\nOur SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.\nWe carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.\n\nThis is the *Easy set with dummy variables* of our SRSD-Feynman datasets, which consists of the following 30 different physics formulas:\n\n![Click here to open a PDF file](URL\n \nDummy variables were randomly generated, and symbolic regression models should not use the dummy variables as part of their predictions.\n\nThe following datasets contain \n1 dummy variable: I.12.1, I.12.4, I.12.5, I.18.12, I.25.13, I.47.23\n\n2 dummy variables: I.14.3, I.18.16, I.43.16, II.3.24, II.8.31, II.10.9, II.13.17, II.15.5, II.27.18, III.7.38, III.12.43\n\n3 dummy variables: I.14.4, I.26.2, I.27.6, I.30.5, II.2.42, II.4.23, II.15.4, II.27.16, II.34.11, II.34.29b, II.38.3, II.38.14, III.15.27\n\nMore details of these datasets are provided in the paper and its supplementary material.",
"### Supported Tasks and Leaderboards\n\nSymbolic Regression",
"## Dataset Structure",
"### Data Instances\n\nTabular data + Ground-truth equation per equation\n\nTabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.\nNote that the number of variables ('num_variables') varies from equation to equation.\n \nGround-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.",
"### Data Fields\n\nFor each dataset, we have \n1. train split (txt file, whitespace as a delimiter)\n2. val split (txt file, whitespace as a delimiter)\n3. test split (txt file, whitespace as a delimiter)\n4. true equation (pickle file for sympy object)",
"### Data Splits\n\n- train: 8,000 samples per equation\n- val: 1,000 samples per equation\n- test: 1,000 samples per equation",
"## Dataset Creation",
"### Curation Rationale\n\nWe chose target equations based on the Feynman Symbolic Regression Database.",
"### Annotations",
"#### Annotation process\n\nWe significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.\nFirst, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.\nNext, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.\nIn cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.\nGenerally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.\nVariables such as angles, for which a linear distribution is expected are set to be sampled uniformly.\nIn addition, variables that take a specific sign were set to be sampled within that range.",
"#### Who are the annotators?\n\nThe main annotators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)",
"### Personal and Sensitive Information\n\nN/A",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nWe annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.",
"### Discussion of Biases\n\nOur choices of target equations are based on the Feynman Symbolic Regression Database, which are focused on a field of Physics.",
"### Other Known Limitations\n\nSome variables used in our datasets indicate some numbers (counts), which should be treated as integer.\nDue to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})",
"## Additional Information",
"### Dataset Curators\n\nThe main curators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)",
"### Licensing Information\n\nCreative Commons Attribution 4.0\n\n\n\n[Preprint]",
"### Contributions\n\nAuthors:\n- Yoshitomo Matsubara (@yoshitomo-matsubara)\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)\n- Yoshitaka Ushiku (@yushiku)"
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"passage: TAGS\n#task_categories-tabular-regression #annotations_creators-expert #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended #language-English #license-cc-by-4.0 #arxiv-2206.10540 #doi-10.57967/hf/0760 #region-us \n# Dataset Card for SRSD-Feynman (Easy set with Dummy Variables)## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery\n- Point of Contact: Yoshitaka Ushiku",
"passage: ### Dataset Summary\n\nOur SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.\nWe carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.\n\nThis is the *Easy set with dummy variables* of our SRSD-Feynman datasets, which consists of the following 30 different physics formulas:\n\n, where the last (rightmost) column indicate output of the target function for given variables.\nNote that the number of variables ('num_variables') varies from equation to equation.\n \nGround-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.### Data Fields\n\nFor each dataset, we have \n1. train split (txt file, whitespace as a delimiter)\n2. val split (txt file, whitespace as a delimiter)\n3. test split (txt file, whitespace as a delimiter)\n4. true equation (pickle file for sympy object)### Data Splits\n\n- train: 8,000 samples per equation\n- val: 1,000 samples per equation\n- test: 1,000 samples per equation## Dataset Creation### Curation Rationale\n\nWe chose target equations based on the Feynman Symbolic Regression Database.### Annotations#### Annotation process\n\nWe significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.\nFirst, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.\nNext, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.\nIn cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.\nGenerally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.\nVariables such as angles, for which a linear distribution is expected are set to be sampled uniformly.\nIn addition, variables that take a specific sign were set to be sampled within that range."
]
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|
b7fcb12b7137ebbe5b7e4eb7c6c0f5c86174a83b |
# Dataset Card for SRSD-Feynman (Medium set with Dummy Variables)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/omron-sinicx/srsd-benchmark
- **Paper:** [Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery](https://arxiv.org/abs/2206.10540)
- **Point of Contact:** [Yoshitaka Ushiku](mailto:[email protected])
### Dataset Summary
Our SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.
We carefully reviewed the properties of each formula and its variables in [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html) to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.
This is the ***Medium set with dummy variables*** of our SRSD-Feynman datasets, which consists of the following 40 different physics formulas:
[](https://huggingface.co/datasets/yoshitomo-matsubara/srsd-feynman_medium_dummy/resolve/main/problem_table.pdf)
Dummy variables were randomly generated, and symbolic regression models should not use the dummy variables as part of their predictions.
The following datasets contain
**1 dummy variable**: I.10.7, I.12.2, I.13.12, I.16.6, I.32.5, I.43.31, II.11.3, II.34.2, II.34.29a, III.14.14, III.15.14, B8
**2 dummy variables**: I.11.19, I.12.11, I.13.4, I.15.10, I.18.4, I.24.6, I.34.8, I.38.12, I.39.11, I.43.43, I.48.2, II.6.11, II.21.32, II.34.2a, III.4.32, III.13.18, III.15.12, III.17.37
**3 dummy variables**: I.8.14, I.29.4, I.34.10, I.34.27, I.39.10, II.8.7, II.37.1, III.8.54, III.19.51, B18
More details of these datasets are provided in [the paper and its supplementary material](https://arxiv.org/abs/2206.10540).
### Supported Tasks and Leaderboards
Symbolic Regression
## Dataset Structure
### Data Instances
Tabular data + Ground-truth equation per equation
Tabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.
Note that the number of variables (`num_variables`) varies from equation to equation.
Ground-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.
### Data Fields
For each dataset, we have
1. train split (txt file, whitespace as a delimiter)
2. val split (txt file, whitespace as a delimiter)
3. test split (txt file, whitespace as a delimiter)
4. true equation (pickle file for sympy object)
### Data Splits
- train: 8,000 samples per equation
- val: 1,000 samples per equation
- test: 1,000 samples per equation
## Dataset Creation
### Curation Rationale
We chose target equations based on [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html).
### Annotations
#### Annotation process
We significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.
First, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.
Next, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.
In cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.
Generally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.
Variables such as angles, for which a linear distribution is expected are set to be sampled uniformly.
In addition, variables that take a specific sign were set to be sampled within that range.
#### Who are the annotators?
The main annotators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
We annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.
### Discussion of Biases
Our choices of target equations are based on [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html), which are focused on a field of Physics.
### Other Known Limitations
Some variables used in our datasets indicate some numbers (counts), which should be treated as integer.
Due to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})
## Additional Information
### Dataset Curators
The main curators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
### Licensing Information
Creative Commons Attribution 4.0
### Citation Information
[[Preprint](https://arxiv.org/abs/2206.10540)]
```bibtex
@article{matsubara2022rethinking,
title={Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery},
author={Matsubara, Yoshitomo and Chiba, Naoya and Igarashi, Ryo and Ushiku, Yoshitaka},
journal={arXiv preprint arXiv:2206.10540},
year={2022}
}
```
### Contributions
Authors:
- Yoshitomo Matsubara (@yoshitomo-matsubara)
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
- Yoshitaka Ushiku (@yushiku)
| yoshitomo-matsubara/srsd-feynman_medium_dummy | [
"task_categories:tabular-regression",
"annotations_creators:expert",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended",
"language:en",
"license:cc-by-4.0",
"arxiv:2206.10540",
"doi:10.57967/hf/0759",
"region:us"
]
| 2023-02-16T07:01:48+00:00 | {"annotations_creators": ["expert"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended"], "task_categories": ["tabular-regression"], "task_ids": [], "pretty_name": "SRSD-Feynman (Medium w/ Dummy Variables)"} | 2023-10-11T01:08:13+00:00 | [
"2206.10540"
]
| [
"en"
]
| TAGS
#task_categories-tabular-regression #annotations_creators-expert #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended #language-English #license-cc-by-4.0 #arxiv-2206.10540 #doi-10.57967/hf/0759 #region-us
|
# Dataset Card for SRSD-Feynman (Medium set with Dummy Variables)
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Homepage:
- Repository: URL
- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
- Point of Contact: Yoshitaka Ushiku
### Dataset Summary
Our SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.
We carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.
This is the *Medium set with dummy variables* of our SRSD-Feynman datasets, which consists of the following 40 different physics formulas:
, where the last (rightmost) column indicate output of the target function for given variables.
Note that the number of variables ('num_variables') varies from equation to equation.
Ground-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.
### Data Fields
For each dataset, we have
1. train split (txt file, whitespace as a delimiter)
2. val split (txt file, whitespace as a delimiter)
3. test split (txt file, whitespace as a delimiter)
4. true equation (pickle file for sympy object)
### Data Splits
- train: 8,000 samples per equation
- val: 1,000 samples per equation
- test: 1,000 samples per equation
## Dataset Creation
### Curation Rationale
We chose target equations based on the Feynman Symbolic Regression Database.
### Annotations
#### Annotation process
We significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.
First, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.
Next, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.
In cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.
Generally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.
Variables such as angles, for which a linear distribution is expected are set to be sampled uniformly.
In addition, variables that take a specific sign were set to be sampled within that range.
#### Who are the annotators?
The main annotators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
We annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.
### Discussion of Biases
Our choices of target equations are based on the Feynman Symbolic Regression Database, which are focused on a field of Physics.
### Other Known Limitations
Some variables used in our datasets indicate some numbers (counts), which should be treated as integer.
Due to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})
## Additional Information
### Dataset Curators
The main curators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
### Licensing Information
Creative Commons Attribution 4.0
[Preprint]
### Contributions
Authors:
- Yoshitomo Matsubara (@yoshitomo-matsubara)
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
- Yoshitaka Ushiku (@yushiku)
| [
"# Dataset Card for SRSD-Feynman (Medium set with Dummy Variables)",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery\n- Point of Contact: Yoshitaka Ushiku",
"### Dataset Summary\n\nOur SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.\nWe carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.\n\nThis is the *Medium set with dummy variables* of our SRSD-Feynman datasets, which consists of the following 40 different physics formulas:\n\n![Click here to open a PDF file](URL\n \nDummy variables were randomly generated, and symbolic regression models should not use the dummy variables as part of their predictions.\n\nThe following datasets contain \n1 dummy variable: I.10.7, I.12.2, I.13.12, I.16.6, I.32.5, I.43.31, II.11.3, II.34.2, II.34.29a, III.14.14, III.15.14, B8\n\n2 dummy variables: I.11.19, I.12.11, I.13.4, I.15.10, I.18.4, I.24.6, I.34.8, I.38.12, I.39.11, I.43.43, I.48.2, II.6.11, II.21.32, II.34.2a, III.4.32, III.13.18, III.15.12, III.17.37\n\n3 dummy variables: I.8.14, I.29.4, I.34.10, I.34.27, I.39.10, II.8.7, II.37.1, III.8.54, III.19.51, B18\n\nMore details of these datasets are provided in the paper and its supplementary material.",
"### Supported Tasks and Leaderboards\n\nSymbolic Regression",
"## Dataset Structure",
"### Data Instances\n\nTabular data + Ground-truth equation per equation\n\nTabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.\nNote that the number of variables ('num_variables') varies from equation to equation.\n \nGround-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.",
"### Data Fields\n\nFor each dataset, we have \n1. train split (txt file, whitespace as a delimiter)\n2. val split (txt file, whitespace as a delimiter)\n3. test split (txt file, whitespace as a delimiter)\n4. true equation (pickle file for sympy object)",
"### Data Splits\n\n- train: 8,000 samples per equation\n- val: 1,000 samples per equation\n- test: 1,000 samples per equation",
"## Dataset Creation",
"### Curation Rationale\n\nWe chose target equations based on the Feynman Symbolic Regression Database.",
"### Annotations",
"#### Annotation process\n\nWe significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.\nFirst, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.\nNext, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.\nIn cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.\nGenerally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.\nVariables such as angles, for which a linear distribution is expected are set to be sampled uniformly.\nIn addition, variables that take a specific sign were set to be sampled within that range.",
"#### Who are the annotators?\n\nThe main annotators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)",
"### Personal and Sensitive Information\n\nN/A",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nWe annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.",
"### Discussion of Biases\n\nOur choices of target equations are based on the Feynman Symbolic Regression Database, which are focused on a field of Physics.",
"### Other Known Limitations\n\nSome variables used in our datasets indicate some numbers (counts), which should be treated as integer.\nDue to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})",
"## Additional Information",
"### Dataset Curators\n\nThe main curators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)",
"### Licensing Information\n\nCreative Commons Attribution 4.0\n\n\n\n[Preprint]",
"### Contributions\n\nAuthors:\n- Yoshitomo Matsubara (@yoshitomo-matsubara)\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)\n- Yoshitaka Ushiku (@yushiku)"
]
| [
"TAGS\n#task_categories-tabular-regression #annotations_creators-expert #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended #language-English #license-cc-by-4.0 #arxiv-2206.10540 #doi-10.57967/hf/0759 #region-us \n",
"# Dataset Card for SRSD-Feynman (Medium set with Dummy Variables)",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery\n- Point of Contact: Yoshitaka Ushiku",
"### Dataset Summary\n\nOur SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.\nWe carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.\n\nThis is the *Medium set with dummy variables* of our SRSD-Feynman datasets, which consists of the following 40 different physics formulas:\n\n![Click here to open a PDF file](URL\n \nDummy variables were randomly generated, and symbolic regression models should not use the dummy variables as part of their predictions.\n\nThe following datasets contain \n1 dummy variable: I.10.7, I.12.2, I.13.12, I.16.6, I.32.5, I.43.31, II.11.3, II.34.2, II.34.29a, III.14.14, III.15.14, B8\n\n2 dummy variables: I.11.19, I.12.11, I.13.4, I.15.10, I.18.4, I.24.6, I.34.8, I.38.12, I.39.11, I.43.43, I.48.2, II.6.11, II.21.32, II.34.2a, III.4.32, III.13.18, III.15.12, III.17.37\n\n3 dummy variables: I.8.14, I.29.4, I.34.10, I.34.27, I.39.10, II.8.7, II.37.1, III.8.54, III.19.51, B18\n\nMore details of these datasets are provided in the paper and its supplementary material.",
"### Supported Tasks and Leaderboards\n\nSymbolic Regression",
"## Dataset Structure",
"### Data Instances\n\nTabular data + Ground-truth equation per equation\n\nTabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.\nNote that the number of variables ('num_variables') varies from equation to equation.\n \nGround-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.",
"### Data Fields\n\nFor each dataset, we have \n1. train split (txt file, whitespace as a delimiter)\n2. val split (txt file, whitespace as a delimiter)\n3. test split (txt file, whitespace as a delimiter)\n4. true equation (pickle file for sympy object)",
"### Data Splits\n\n- train: 8,000 samples per equation\n- val: 1,000 samples per equation\n- test: 1,000 samples per equation",
"## Dataset Creation",
"### Curation Rationale\n\nWe chose target equations based on the Feynman Symbolic Regression Database.",
"### Annotations",
"#### Annotation process\n\nWe significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.\nFirst, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.\nNext, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.\nIn cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.\nGenerally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.\nVariables such as angles, for which a linear distribution is expected are set to be sampled uniformly.\nIn addition, variables that take a specific sign were set to be sampled within that range.",
"#### Who are the annotators?\n\nThe main annotators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)",
"### Personal and Sensitive Information\n\nN/A",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nWe annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.",
"### Discussion of Biases\n\nOur choices of target equations are based on the Feynman Symbolic Regression Database, which are focused on a field of Physics.",
"### Other Known Limitations\n\nSome variables used in our datasets indicate some numbers (counts), which should be treated as integer.\nDue to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})",
"## Additional Information",
"### Dataset Curators\n\nThe main curators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)",
"### Licensing Information\n\nCreative Commons Attribution 4.0\n\n\n\n[Preprint]",
"### Contributions\n\nAuthors:\n- Yoshitomo Matsubara (@yoshitomo-matsubara)\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)\n- Yoshitaka Ushiku (@yushiku)"
]
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"passage: TAGS\n#task_categories-tabular-regression #annotations_creators-expert #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended #language-English #license-cc-by-4.0 #arxiv-2206.10540 #doi-10.57967/hf/0759 #region-us \n# Dataset Card for SRSD-Feynman (Medium set with Dummy Variables)## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery\n- Point of Contact: Yoshitaka Ushiku",
"passage: ### Dataset Summary\n\nOur SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.\nWe carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.\n\nThis is the *Medium set with dummy variables* of our SRSD-Feynman datasets, which consists of the following 40 different physics formulas:\n\n, where the last (rightmost) column indicate output of the target function for given variables.\nNote that the number of variables ('num_variables') varies from equation to equation.\n \nGround-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.### Data Fields\n\nFor each dataset, we have \n1. train split (txt file, whitespace as a delimiter)\n2. val split (txt file, whitespace as a delimiter)\n3. test split (txt file, whitespace as a delimiter)\n4. true equation (pickle file for sympy object)### Data Splits\n\n- train: 8,000 samples per equation\n- val: 1,000 samples per equation\n- test: 1,000 samples per equation## Dataset Creation### Curation Rationale\n\nWe chose target equations based on the Feynman Symbolic Regression Database.### Annotations#### Annotation process\n\nWe significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.\nFirst, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.\nNext, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.\nIn cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.\nGenerally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.\nVariables such as angles, for which a linear distribution is expected are set to be sampled uniformly.\nIn addition, variables that take a specific sign were set to be sampled within that range."
]
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|
7a97bef50a6720c18e4b79ebd7f29e9c6b084b1b |
# Dataset Card for SRSD-Feynman (Hard set with Dummy Variables)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/omron-sinicx/srsd-benchmark
- **Paper:** [Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery](https://arxiv.org/abs/2206.10540)
- **Point of Contact:** [Yoshitaka Ushiku](mailto:[email protected])
### Dataset Summary
Our SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.
We carefully reviewed the properties of each formula and its variables in [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html) to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.
This is the ***Hard set with dummy variables*** of our SRSD-Feynman datasets, which consists of the following 50 different physics formulas:
[](https://huggingface.co/datasets/yoshitomo-matsubara/srsd-feynman_hard_dummy/resolve/main/problem_table.pdf)
Dummy variables were randomly generated, and symbolic regression models should not use the dummy variables as part of their predictions.
The following datasets contain
**1 dummy variable**: I.15.3x, I.30.3, II.6.15a, II.11.17, II.11.28, II.13.23, II.13.34, II.24.17, B1, B6, B12, B16, B17
**2 dummy variables**: I.6.20, I.6.20b, I.9.18, I.15.3t, I.29.16, I.34.14, I.39.22, I.44.4, II.11.20, II.11.27, II.35.18, III.9.52, III.10.19, III.21.20, B2, B3, B7, B9
**3 dummy variables**: I.6.20a, I.32.17, I.37.4, I.40.1, I.41.16, I.50.26, II.6.15b, II.35.21, II.36.38, III.4.33, B4, B5, B10, B11, B13, B14, B15, B19, B20
More details of these datasets are provided in [the paper and its supplementary material](https://arxiv.org/abs/2206.10540).
### Supported Tasks and Leaderboards
Symbolic Regression
## Dataset Structure
### Data Instances
Tabular data + Ground-truth equation per equation
Tabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.
Note that the number of variables (`num_variables`) varies from equation to equation.
Ground-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.
### Data Fields
For each dataset, we have
1. train split (txt file, whitespace as a delimiter)
2. val split (txt file, whitespace as a delimiter)
3. test split (txt file, whitespace as a delimiter)
4. true equation (pickle file for sympy object)
### Data Splits
- train: 8,000 samples per equation
- val: 1,000 samples per equation
- test: 1,000 samples per equation
## Dataset Creation
### Curation Rationale
We chose target equations based on [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html).
### Annotations
#### Annotation process
We significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.
First, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.
Next, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.
In cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.
Generally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.
Variables such as angles, for which a linear distribution is expected are set to be sampled uniformly.
In addition, variables that take a specific sign were set to be sampled within that range.
#### Who are the annotators?
The main annotators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
We annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.
### Discussion of Biases
Our choices of target equations are based on [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html), which are focused on a field of Physics.
### Other Known Limitations
Some variables used in our datasets indicate some numbers (counts), which should be treated as integer.
Due to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})
## Additional Information
### Dataset Curators
The main curators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
### Licensing Information
Creative Commons Attribution 4.0
### Citation Information
[[Preprint](https://arxiv.org/abs/2206.10540)]
```bibtex
@article{matsubara2022rethinking,
title={Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery},
author={Matsubara, Yoshitomo and Chiba, Naoya and Igarashi, Ryo and Ushiku, Yoshitaka},
journal={arXiv preprint arXiv:2206.10540},
year={2022}
}
```
### Contributions
Authors:
- Yoshitomo Matsubara (@yoshitomo-matsubara)
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
- Yoshitaka Ushiku (@yushiku)
| yoshitomo-matsubara/srsd-feynman_hard_dummy | [
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"2206.10540"
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| [
"en"
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| TAGS
#task_categories-tabular-regression #annotations_creators-expert #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended #language-English #license-cc-by-4.0 #arxiv-2206.10540 #doi-10.57967/hf/0758 #region-us
|
# Dataset Card for SRSD-Feynman (Hard set with Dummy Variables)
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Homepage:
- Repository: URL
- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
- Point of Contact: Yoshitaka Ushiku
### Dataset Summary
Our SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.
We carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.
This is the *Hard set with dummy variables* of our SRSD-Feynman datasets, which consists of the following 50 different physics formulas:
, where the last (rightmost) column indicate output of the target function for given variables.
Note that the number of variables ('num_variables') varies from equation to equation.
Ground-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.
### Data Fields
For each dataset, we have
1. train split (txt file, whitespace as a delimiter)
2. val split (txt file, whitespace as a delimiter)
3. test split (txt file, whitespace as a delimiter)
4. true equation (pickle file for sympy object)
### Data Splits
- train: 8,000 samples per equation
- val: 1,000 samples per equation
- test: 1,000 samples per equation
## Dataset Creation
### Curation Rationale
We chose target equations based on the Feynman Symbolic Regression Database.
### Annotations
#### Annotation process
We significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.
First, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.
Next, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.
In cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.
Generally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.
Variables such as angles, for which a linear distribution is expected are set to be sampled uniformly.
In addition, variables that take a specific sign were set to be sampled within that range.
#### Who are the annotators?
The main annotators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
We annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.
### Discussion of Biases
Our choices of target equations are based on the Feynman Symbolic Regression Database, which are focused on a field of Physics.
### Other Known Limitations
Some variables used in our datasets indicate some numbers (counts), which should be treated as integer.
Due to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})
## Additional Information
### Dataset Curators
The main curators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
### Licensing Information
Creative Commons Attribution 4.0
[Preprint]
### Contributions
Authors:
- Yoshitomo Matsubara (@yoshitomo-matsubara)
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
- Yoshitaka Ushiku (@yushiku)
| [
"# Dataset Card for SRSD-Feynman (Hard set with Dummy Variables)",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery\n- Point of Contact: Yoshitaka Ushiku",
"### Dataset Summary\n\nOur SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.\nWe carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.\n\nThis is the *Hard set with dummy variables* of our SRSD-Feynman datasets, which consists of the following 50 different physics formulas:\n\n![Click here to open a PDF file](URL\n \nDummy variables were randomly generated, and symbolic regression models should not use the dummy variables as part of their predictions.\n\nThe following datasets contain \n1 dummy variable: I.15.3x, I.30.3, II.6.15a, II.11.17, II.11.28, II.13.23, II.13.34, II.24.17, B1, B6, B12, B16, B17\n\n2 dummy variables: I.6.20, I.6.20b, I.9.18, I.15.3t, I.29.16, I.34.14, I.39.22, I.44.4, II.11.20, II.11.27, II.35.18, III.9.52, III.10.19, III.21.20, B2, B3, B7, B9\n\n3 dummy variables: I.6.20a, I.32.17, I.37.4, I.40.1, I.41.16, I.50.26, II.6.15b, II.35.21, II.36.38, III.4.33, B4, B5, B10, B11, B13, B14, B15, B19, B20\n\nMore details of these datasets are provided in the paper and its supplementary material.",
"### Supported Tasks and Leaderboards\n\nSymbolic Regression",
"## Dataset Structure",
"### Data Instances\n\nTabular data + Ground-truth equation per equation\n\nTabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.\nNote that the number of variables ('num_variables') varies from equation to equation.\n \nGround-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.",
"### Data Fields\n\nFor each dataset, we have \n1. train split (txt file, whitespace as a delimiter)\n2. val split (txt file, whitespace as a delimiter)\n3. test split (txt file, whitespace as a delimiter)\n4. true equation (pickle file for sympy object)",
"### Data Splits\n\n- train: 8,000 samples per equation\n- val: 1,000 samples per equation\n- test: 1,000 samples per equation",
"## Dataset Creation",
"### Curation Rationale\n\nWe chose target equations based on the Feynman Symbolic Regression Database.",
"### Annotations",
"#### Annotation process\n\nWe significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.\nFirst, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.\nNext, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.\nIn cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.\nGenerally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.\nVariables such as angles, for which a linear distribution is expected are set to be sampled uniformly.\nIn addition, variables that take a specific sign were set to be sampled within that range.",
"#### Who are the annotators?\n\nThe main annotators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)",
"### Personal and Sensitive Information\n\nN/A",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nWe annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.",
"### Discussion of Biases\n\nOur choices of target equations are based on the Feynman Symbolic Regression Database, which are focused on a field of Physics.",
"### Other Known Limitations\n\nSome variables used in our datasets indicate some numbers (counts), which should be treated as integer.\nDue to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})",
"## Additional Information",
"### Dataset Curators\n\nThe main curators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)",
"### Licensing Information\n\nCreative Commons Attribution 4.0\n\n\n\n[Preprint]",
"### Contributions\n\nAuthors:\n- Yoshitomo Matsubara (@yoshitomo-matsubara)\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)\n- Yoshitaka Ushiku (@yushiku)"
]
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"TAGS\n#task_categories-tabular-regression #annotations_creators-expert #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended #language-English #license-cc-by-4.0 #arxiv-2206.10540 #doi-10.57967/hf/0758 #region-us \n",
"# Dataset Card for SRSD-Feynman (Hard set with Dummy Variables)",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery\n- Point of Contact: Yoshitaka Ushiku",
"### Dataset Summary\n\nOur SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.\nWe carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.\n\nThis is the *Hard set with dummy variables* of our SRSD-Feynman datasets, which consists of the following 50 different physics formulas:\n\n![Click here to open a PDF file](URL\n \nDummy variables were randomly generated, and symbolic regression models should not use the dummy variables as part of their predictions.\n\nThe following datasets contain \n1 dummy variable: I.15.3x, I.30.3, II.6.15a, II.11.17, II.11.28, II.13.23, II.13.34, II.24.17, B1, B6, B12, B16, B17\n\n2 dummy variables: I.6.20, I.6.20b, I.9.18, I.15.3t, I.29.16, I.34.14, I.39.22, I.44.4, II.11.20, II.11.27, II.35.18, III.9.52, III.10.19, III.21.20, B2, B3, B7, B9\n\n3 dummy variables: I.6.20a, I.32.17, I.37.4, I.40.1, I.41.16, I.50.26, II.6.15b, II.35.21, II.36.38, III.4.33, B4, B5, B10, B11, B13, B14, B15, B19, B20\n\nMore details of these datasets are provided in the paper and its supplementary material.",
"### Supported Tasks and Leaderboards\n\nSymbolic Regression",
"## Dataset Structure",
"### Data Instances\n\nTabular data + Ground-truth equation per equation\n\nTabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.\nNote that the number of variables ('num_variables') varies from equation to equation.\n \nGround-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.",
"### Data Fields\n\nFor each dataset, we have \n1. train split (txt file, whitespace as a delimiter)\n2. val split (txt file, whitespace as a delimiter)\n3. test split (txt file, whitespace as a delimiter)\n4. true equation (pickle file for sympy object)",
"### Data Splits\n\n- train: 8,000 samples per equation\n- val: 1,000 samples per equation\n- test: 1,000 samples per equation",
"## Dataset Creation",
"### Curation Rationale\n\nWe chose target equations based on the Feynman Symbolic Regression Database.",
"### Annotations",
"#### Annotation process\n\nWe significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.\nFirst, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.\nNext, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.\nIn cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.\nGenerally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.\nVariables such as angles, for which a linear distribution is expected are set to be sampled uniformly.\nIn addition, variables that take a specific sign were set to be sampled within that range.",
"#### Who are the annotators?\n\nThe main annotators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)",
"### Personal and Sensitive Information\n\nN/A",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nWe annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.",
"### Discussion of Biases\n\nOur choices of target equations are based on the Feynman Symbolic Regression Database, which are focused on a field of Physics.",
"### Other Known Limitations\n\nSome variables used in our datasets indicate some numbers (counts), which should be treated as integer.\nDue to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})",
"## Additional Information",
"### Dataset Curators\n\nThe main curators are\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)",
"### Licensing Information\n\nCreative Commons Attribution 4.0\n\n\n\n[Preprint]",
"### Contributions\n\nAuthors:\n- Yoshitomo Matsubara (@yoshitomo-matsubara)\n- Naoya Chiba (@nchiba)\n- Ryo Igarashi (@rigarash)\n- Yoshitaka Ushiku (@yushiku)"
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"passage: TAGS\n#task_categories-tabular-regression #annotations_creators-expert #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended #language-English #license-cc-by-4.0 #arxiv-2206.10540 #doi-10.57967/hf/0758 #region-us \n# Dataset Card for SRSD-Feynman (Hard set with Dummy Variables)## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage:\n- Repository: URL\n- Paper: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery\n- Point of Contact: Yoshitaka Ushiku",
"passage: ### Dataset Summary\n\nOur SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.\nWe carefully reviewed the properties of each formula and its variables in the Feynman Symbolic Regression Database to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.\n\nThis is the *Hard set with dummy variables* of our SRSD-Feynman datasets, which consists of the following 50 different physics formulas:\n\n, where the last (rightmost) column indicate output of the target function for given variables.\nNote that the number of variables ('num_variables') varies from equation to equation.\n \nGround-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.### Data Fields\n\nFor each dataset, we have \n1. train split (txt file, whitespace as a delimiter)\n2. val split (txt file, whitespace as a delimiter)\n3. test split (txt file, whitespace as a delimiter)\n4. true equation (pickle file for sympy object)### Data Splits\n\n- train: 8,000 samples per equation\n- val: 1,000 samples per equation\n- test: 1,000 samples per equation## Dataset Creation### Curation Rationale\n\nWe chose target equations based on the Feynman Symbolic Regression Database.### Annotations#### Annotation process\n\nWe significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.\nFirst, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.\nNext, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.\nIn cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.\nGenerally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.\nVariables such as angles, for which a linear distribution is expected are set to be sampled uniformly.\nIn addition, variables that take a specific sign were set to be sampled within that range."
]
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]
|
8007cbafadb06a0bdfaa0ba534b2d7a5b2f1052b |
# FB15k Dataset
The details of it can be got by this paper titled:
+ [Translating Embeddings for Modeling Multi-relational Data](http://dl.acm.org/doi/10.5555/2999792.2999923) | VLyb/FB15k | [
"size_categories:10K<n<100K",
"language:en",
"license:unlicense",
"link-prediction",
"region:us"
]
| 2023-02-16T07:30:38+00:00 | {"language": ["en"], "license": "unlicense", "size_categories": ["10K<n<100K"], "pretty_name": "FB15k", "tags": ["link-prediction"]} | 2023-02-16T07:44:47+00:00 | []
| [
"en"
]
| TAGS
#size_categories-10K<n<100K #language-English #license-unlicense #link-prediction #region-us
|
# FB15k Dataset
The details of it can be got by this paper titled:
+ Translating Embeddings for Modeling Multi-relational Data | [
"# FB15k Dataset\n\nThe details of it can be got by this paper titled:\n\n+ Translating Embeddings for Modeling Multi-relational Data"
]
| [
"TAGS\n#size_categories-10K<n<100K #language-English #license-unlicense #link-prediction #region-us \n",
"# FB15k Dataset\n\nThe details of it can be got by this paper titled:\n\n+ Translating Embeddings for Modeling Multi-relational Data"
]
| [
35,
35
]
| [
"passage: TAGS\n#size_categories-10K<n<100K #language-English #license-unlicense #link-prediction #region-us \n# FB15k Dataset\n\nThe details of it can be got by this paper titled:\n\n+ Translating Embeddings for Modeling Multi-relational Data"
]
| [
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|
9648d68f54fe13d0372c3f89e365fec8e4c98856 |
## Dataset Description
- **Homepage:** https://github.com/gijswijnholds/sick_nl
- **Repository:** https://github.com/gijswijnholds/sick_nl
- **Paper:** https://aclanthology.org/2021.eacl-main.126/
- **Point of Contact:** [Gijs Wijnholds](mailto:[email protected])
### Dataset Summary
An automatically translated, manually corrected translation of the SICK dataset of [Marelli et al. 2014](https://www.aclweb.org/anthology/L14-1314), intended to boost research in Dutch NLP.
### Languages
The dataset is in Dutch.
## Dataset Structure
### Data Fields
- pair_ID: sentence pair ID
- sentence_A: sentence A
- sentence_B: sentence B
- label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)
- relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)
- entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B)
- entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A)
- sentence_A_original: original sentence from which sentence A is derived
- sentence_B_original: original sentence from which sentence B is derived
- sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL)
- sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL)
### Data Splits
Train Trial Test
4439 495 4906
## Dataset Creation
The dataset was created by first automatically translating all sentences, then by manually correcting any translation errors. This guarantees naturality of the examples while aligning the relatedness scores and entailment labels. Since the data IDs are preserved the dataset is fully aligned on the sentence level.
## Additional Information
### Licensing Information
This dataset falls under an MIT License.
### Citation Information
```
@inproceedings{wijnholds-etal-2021-sicknl,
title = "SICK-NL: A Dataset for Dutch Natural Language Inference",
author = "Wijnholds, Gijs and Moortgat, Michael",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.eacl-main.126/",
}
```
### Contributions
Thanks to [@maximedb](https://huggingface.co/maximedb) for adding this dataset. | maximedb/sick_nl | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:nl",
"license:mit",
"region:us"
]
| 2023-02-16T07:44:25+00:00 | {"language": ["nl"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "SICK-NL", "dataset_info": {"features": [{"name": "pair_ID", "dtype": "int64"}, {"name": "sentence_A", "dtype": "string"}, {"name": "sentence_B", "dtype": "string"}, {"name": "entailment_label", "dtype": "string"}, {"name": "relatedness_score", "dtype": "float64"}, {"name": "entailment_AB", "dtype": "string"}, {"name": "entailment_BA", "dtype": "string"}, {"name": "sentence_A_original", "dtype": "string"}, {"name": "sentence_B_original", "dtype": "string"}, {"name": "sentence_A_dataset", "dtype": "string"}, {"name": "sentence_B_dataset", "dtype": "string"}, {"name": "SemEval_set", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_seq2seq", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1359887, "num_examples": 4439}, {"name": "validation", "num_bytes": 153417, "num_examples": 495}, {"name": "test", "num_bytes": 1496660, "num_examples": 4906}], "download_size": 822658, "dataset_size": 3009964}} | 2023-04-25T09:19:43+00:00 | []
| [
"nl"
]
| TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Dutch #license-mit #region-us
|
## Dataset Description
- Homepage: URL
- Repository: URL
- Paper: URL
- Point of Contact: Gijs Wijnholds
### Dataset Summary
An automatically translated, manually corrected translation of the SICK dataset of Marelli et al. 2014, intended to boost research in Dutch NLP.
### Languages
The dataset is in Dutch.
## Dataset Structure
### Data Fields
- pair_ID: sentence pair ID
- sentence_A: sentence A
- sentence_B: sentence B
- label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)
- relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)
- entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B)
- entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A)
- sentence_A_original: original sentence from which sentence A is derived
- sentence_B_original: original sentence from which sentence B is derived
- sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL)
- sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL)
### Data Splits
Train Trial Test
4439 495 4906
## Dataset Creation
The dataset was created by first automatically translating all sentences, then by manually correcting any translation errors. This guarantees naturality of the examples while aligning the relatedness scores and entailment labels. Since the data IDs are preserved the dataset is fully aligned on the sentence level.
## Additional Information
### Licensing Information
This dataset falls under an MIT License.
### Contributions
Thanks to @maximedb for adding this dataset. | [
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Point of Contact: Gijs Wijnholds",
"### Dataset Summary\n\nAn automatically translated, manually corrected translation of the SICK dataset of Marelli et al. 2014, intended to boost research in Dutch NLP.",
"### Languages\n\nThe dataset is in Dutch.",
"## Dataset Structure",
"### Data Fields\n\n- pair_ID: sentence pair ID\n- sentence_A: sentence A\n- sentence_B: sentence B\n- label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)\n- relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)\n- entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B)\n- entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A)\n- sentence_A_original: original sentence from which sentence A is derived\n- sentence_B_original: original sentence from which sentence B is derived\n- sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL)\n- sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL)",
"### Data Splits\n\nTrain Trial Test\n4439 495 4906",
"## Dataset Creation\n\nThe dataset was created by first automatically translating all sentences, then by manually correcting any translation errors. This guarantees naturality of the examples while aligning the relatedness scores and entailment labels. Since the data IDs are preserved the dataset is fully aligned on the sentence level.",
"## Additional Information",
"### Licensing Information\n\nThis dataset falls under an MIT License.",
"### Contributions\n\nThanks to @maximedb for adding this dataset."
]
| [
"TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Dutch #license-mit #region-us \n",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Point of Contact: Gijs Wijnholds",
"### Dataset Summary\n\nAn automatically translated, manually corrected translation of the SICK dataset of Marelli et al. 2014, intended to boost research in Dutch NLP.",
"### Languages\n\nThe dataset is in Dutch.",
"## Dataset Structure",
"### Data Fields\n\n- pair_ID: sentence pair ID\n- sentence_A: sentence A\n- sentence_B: sentence B\n- label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)\n- relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)\n- entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B)\n- entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A)\n- sentence_A_original: original sentence from which sentence A is derived\n- sentence_B_original: original sentence from which sentence B is derived\n- sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL)\n- sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL)",
"### Data Splits\n\nTrain Trial Test\n4439 495 4906",
"## Dataset Creation\n\nThe dataset was created by first automatically translating all sentences, then by manually correcting any translation errors. This guarantees naturality of the examples while aligning the relatedness scores and entailment labels. Since the data IDs are preserved the dataset is fully aligned on the sentence level.",
"## Additional Information",
"### Licensing Information\n\nThis dataset falls under an MIT License.",
"### Contributions\n\nThanks to @maximedb for adding this dataset."
]
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"passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Dutch #license-mit #region-us \n## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Point of Contact: Gijs Wijnholds### Dataset Summary\n\nAn automatically translated, manually corrected translation of the SICK dataset of Marelli et al. 2014, intended to boost research in Dutch NLP.### Languages\n\nThe dataset is in Dutch.## Dataset Structure### Data Fields\n\n- pair_ID: sentence pair ID\n- sentence_A: sentence A\n- sentence_B: sentence B\n- label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)\n- relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)\n- entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B)\n- entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A)\n- sentence_A_original: original sentence from which sentence A is derived\n- sentence_B_original: original sentence from which sentence B is derived\n- sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL)\n- sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL)### Data Splits\n\nTrain Trial Test\n4439 495 4906## Dataset Creation\n\nThe dataset was created by first automatically translating all sentences, then by manually correcting any translation errors. This guarantees naturality of the examples while aligning the relatedness scores and entailment labels. Since the data IDs are preserved the dataset is fully aligned on the sentence level.## Additional Information### Licensing Information\n\nThis dataset falls under an MIT License.### Contributions\n\nThanks to @maximedb for adding this dataset."
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|
1d611b7384148ac997a7c004ef98ba18d215c2ea |
# Dataset Card for Helpful Instructions
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact: Lewis Tunstall**
### Dataset Summary
Helpful Instructions is a dataset of `(instruction, demonstration)` pairs that are derived from public datasets. As the name suggests, it focuses on instructions that are "helpful", i.e. the kind of questions or tasks a human user might instruct an AI assistant to perform. You can load the dataset as follows:
```python
from datasets import load_dataset
# Load all subsets
helpful_instructions = load_dataset("HuggingFaceH4/helpful_instructions")
# Load a single subset
helpful_instructions_subset = load_dataset("HuggingFaceH4/helpful_instructions", data_dir="data/helpful-anthropic-raw")
```
### Supported Tasks and Leaderboards
This dataset can be used to fine-tune pretrained language models to follow instructions.
### Languages
English
## 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] | HuggingFaceH4/helpful-instructions | [
"license:apache-2.0",
"human-feedback",
"region:us"
]
| 2023-02-16T09:12:16+00:00 | {"license": "apache-2.0", "pretty_name": "Helpful Instructions", "tags": ["human-feedback"]} | 2023-02-20T08:58:24+00:00 | []
| []
| TAGS
#license-apache-2.0 #human-feedback #region-us
|
# Dataset Card for Helpful Instructions
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact: Lewis Tunstall
### Dataset Summary
Helpful Instructions is a dataset of '(instruction, demonstration)' pairs that are derived from public datasets. As the name suggests, it focuses on instructions that are "helpful", i.e. the kind of questions or tasks a human user might instruct an AI assistant to perform. You can load the dataset as follows:
### Supported Tasks and Leaderboards
This dataset can be used to fine-tune pretrained language models to follow instructions.
### Languages
English
## 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 Helpful Instructions",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact: Lewis Tunstall",
"### Dataset Summary\n\nHelpful Instructions is a dataset of '(instruction, demonstration)' pairs that are derived from public datasets. As the name suggests, it focuses on instructions that are \"helpful\", i.e. the kind of questions or tasks a human user might instruct an AI assistant to perform. You can load the dataset as follows:",
"### Supported Tasks and Leaderboards\n\nThis dataset can be used to fine-tune pretrained language models to follow instructions.",
"### Languages\n\nEnglish",
"## 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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"TAGS\n#license-apache-2.0 #human-feedback #region-us \n",
"# Dataset Card for Helpful Instructions",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact: Lewis Tunstall",
"### Dataset Summary\n\nHelpful Instructions is a dataset of '(instruction, demonstration)' pairs that are derived from public datasets. As the name suggests, it focuses on instructions that are \"helpful\", i.e. the kind of questions or tasks a human user might instruct an AI assistant to perform. You can load the dataset as follows:",
"### Supported Tasks and Leaderboards\n\nThis dataset can be used to fine-tune pretrained language models to follow instructions.",
"### Languages\n\nEnglish",
"## 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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|
6e58ad4f493bf9e409c56e9a3f3ef42012db7a3f | # AutoTrain Dataset for project: bbc-news-classifier
## Dataset Description
This dataset has been automatically processed by AutoTrain for project bbc-news-classifier.
### 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": "tv debate urged for party chiefs broadcasters should fix a date for a pre-election televised debate between the three main political leaders according to the hansard society. it would then be up to tony blair michael howard and charles kennedy to decide whether to take part the non-partisan charity said. chairman lord holme argued that prime ministers should not have the right of veto on a matter of public interest . the broadcasters should make the decision to go ahead he said. lord holme s proposal for a televised debate comes just four months after millions of viewers were able to watch us president george w bush slug it out verbally with his democratic challenger john kerry. he said it was a democratically dubious proposition that it was up to the incumbent prime minister to decide whether a similar event takes place here. if mr blair did not want to take part the broadcasters could go ahead with an empty chair or cancel the event and explain their reasons why lord holme said. what makes the present situation even less acceptable is that although mr howard and mr kennedy have said they would welcome a debate no-one has heard directly from the prime minister he said. it has been left to nudges and winks hints and briefings from his aides and campaign managers to imply that mr blair doesn t want one but we haven t heard from the prime minister himself. lord holme who has campaigned for televised debates at previous elections said broadcasters were more than willing to cooperate with the arrangements . opinion polls suggested that the idea had the backing of the public who like comparing the personalities and policies of the contenders in their own homes he said. lord holme argued that as part of their public service obligations broadcasters should make the decision to go ahead as soon as the election is called. an independent third-party body such as the hansard society or electoral commission could work out the ground rules so they were fair to participants and informative to the public he said. it would be up to each party leader to accept or refuse said lord holme. if the prime minister s reported position is true and he does want to take part he would then be obliged to say why publicly. the broadcasters would then have the option of cancelling the event for obvious and well-understood reasons or going ahead with an empty chair. either way would be preferable to the present hidden veto. the hansard society has long campaigned for televised debates and has published reports on the issue in 1997 and 2001. tony blair has already ruled out taking part in a televised debate during the forthcoming election campaign. last month he said: we answer this every election campaign and for the reasons i have given before the answer is no he said at his monthly news conference.",
"target": 2
},
{
"text": "ecb holds rates amid growth fears the european central bank has left its key interest rate unchanged at 2% for the 19th month in succession. borrowing costs have remained on hold amid concerns about the strength of economic growth in the 12 nations sharing the euro analysts said. despite signs of pick-up labour markets and consumer demand remain sluggish while firms are eyeing cost cutting measures such as redundancies. high oil prices meanwhile have put upward pressure on the inflation rate. surveys of economists have shown that the majority expect borrowing costs to stay at 2% in coming months with an increase of a quarter of a percentage point predicted some time in the second half of the year. if anything there may be greater calls for an interest rate cut especially with the euro continuing to strengthen against the dollar. the euro land economy is still struggling with this recovery said economist dirk schumacher. the ecb may sound rather hawkish but once the data allows them to cut again they will. data coming out of germany on thursday underlined the problems facing european policy makers. while germany s economy expanded by 1.7% in 2004 growth was driven by export sales and lost some of its momentum in the last three months of the year. the strength of the euro is threatening to dampen that foreign demand in 2005 and domestic consumption currently is not strong enough to take up the slack. inflation in the eurozone however is estimated at about 2.3% in december above ecb guidelines of 2%. ecb president jean-claude trichet has remained upbeat about prospects for the region and inflation is expected to drop below 2% later in 2005. the ecb has forecast economic growth in the eurozone of 1.9% in 2005.",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['business', 'entertainment', 'politics', 'sport', 'technology'], 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 | 198 |
| valid | 52 |
| Saripudin/autotrain-data-bbc-news-classifier | [
"task_categories:text-classification",
"region:us"
]
| 2023-02-16T09:50:57+00:00 | {"task_categories": ["text-classification"]} | 2023-02-16T09:54:19+00:00 | []
| []
| TAGS
#task_categories-text-classification #region-us
| AutoTrain Dataset for project: bbc-news-classifier
==================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project bbc-news-classifier.
### 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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|
aa399ffcba12f6baa52e5bff826b59bf2ab86e51 | # Dataset Card for "ismus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tiro-is/ismus | [
"region:us"
]
| 2023-02-16T11:50:58+00:00 | {"dataset_info": {"features": [{"name": "audio_id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "normalized_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 15107936585.61, "num_examples": 109511}, {"name": "test", "num_bytes": 947114213.608, "num_examples": 3184}], "download_size": 16411953840, "dataset_size": 16055050799.218}} | 2023-02-16T12:11:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ismus"
More Information needed | [
"# Dataset Card for \"ismus\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ismus\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ismus\"\n\nMore Information needed"
]
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]
|
164fbe58548fe426e4fa13afcbf4de34732f68e9 | # AutoTrain Dataset for project: new_1000_respostas
## Dataset Description
This dataset has been automatically processed by AutoTrain for project new_1000_respostas.
### Languages
The BCP-47 code for the dataset's language is pt.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"target": 0,
"text": " Ol\u00e1, no meu \u00faltimo pedido eu paguei o item errado. Paguei a cerveja long neck, quando o correto \u00e9 a garrafa de 600ml."
},
{
"target": 4,
"text": " Boa tarde!!! Sou moradora do Citt\u00e0 Imbu\u00ed, hoje 15/01 por volta das 11:50, meu filho tentou comprar uma coca cola e n\u00e3o conseguiu, mas o valor do produto foi debitado. Voc\u00eas podem verificar nas imagens e externar o valor? Desde j\u00e1, agrade\u00e7o. Att, Ana Carla"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"target": "ClassLabel(names=['Compra Equivocada', 'Cr\u00e9dito n\u00e3o compensado', 'Desativa\u00e7\u00e3o de conta', 'Dificuldade para finalizar a compra', 'Estorno/devolu\u00e7\u00e3o de valor', 'Problemas com destrava', 'Problemas com promo\u00e7\u00f5es', 'Produto danificado/Vencido', 'Produto n\u00e3o encontrado', 'Solicita\u00e7\u00e3o de reposi\u00e7\u00e3o'], id=None)",
"text": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 715 |
| valid | 182 |
| pedro-m4u/autotrain-data-new_1000_respostas | [
"task_categories:text-classification",
"language:pt",
"region:us"
]
| 2023-02-16T12:15:49+00:00 | {"language": ["pt"], "task_categories": ["text-classification"]} | 2023-02-16T12:20:00+00:00 | []
| [
"pt"
]
| TAGS
#task_categories-text-classification #language-Portuguese #region-us
| AutoTrain Dataset for project: new\_1000\_respostas
===================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project new\_1000\_respostas.
### Languages
The BCP-47 code for the dataset's language is pt.
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 pt.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-text-classification #language-Portuguese #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is pt.\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:"
]
| [
23,
26,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-text-classification #language-Portuguese #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is pt.\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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|
1653a2b77349fb0dbf41c677bc74fa3bad7874b2 |
# Dataset Card for Dataset CityLearn
This dataset is used to train a decision Transformer for the CityLearn 2022 environment https://www.aicrowd.com/challenges/neurips-2022-citylearn-challenge.
You can load data from this dataset via:
datasets.load_dataset('TobiTob/CityLearn', 'data_name')
A short description of all data sets can be found in file CityLearn.py | TobiTob/CityLearn | [
"region:us"
]
| 2023-02-16T12:16:52+00:00 | {} | 2023-06-27T10:14:53+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Dataset CityLearn
This dataset is used to train a decision Transformer for the CityLearn 2022 environment URL
You can load data from this dataset via:
datasets.load_dataset('TobiTob/CityLearn', 'data_name')
A short description of all data sets can be found in file URL | [
"# Dataset Card for Dataset CityLearn\n\nThis dataset is used to train a decision Transformer for the CityLearn 2022 environment URL \nYou can load data from this dataset via:\n\ndatasets.load_dataset('TobiTob/CityLearn', 'data_name')\n\nA short description of all data sets can be found in file URL"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for Dataset CityLearn\n\nThis dataset is used to train a decision Transformer for the CityLearn 2022 environment URL \nYou can load data from this dataset via:\n\ndatasets.load_dataset('TobiTob/CityLearn', 'data_name')\n\nA short description of all data sets can be found in file URL"
]
| [
6,
82
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for Dataset CityLearn\n\nThis dataset is used to train a decision Transformer for the CityLearn 2022 environment URL \nYou can load data from this dataset via:\n\ndatasets.load_dataset('TobiTob/CityLearn', 'data_name')\n\nA short description of all data sets can be found in file URL"
]
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|
5878918b23b4c415df7158ec75eee187247b4801 | # danbooru-metadata
Dump of various portions danbooru's metadata, as of Feburary 2023. Everything was taken directly from their JSON API.
The directory structure follows the Danbooru20XX format of each subfolder for a record type being the record's ID modulo 1000. The `.zip` files can sometimes hold hundreds of thousands of small JSON files when put together, so use caution when extracting.
| stma/danbooru-metadata | [
"region:us"
]
| 2023-02-16T13:10:54+00:00 | {} | 2023-02-17T06:21:00+00:00 | []
| []
| TAGS
#region-us
| # danbooru-metadata
Dump of various portions danbooru's metadata, as of Feburary 2023. Everything was taken directly from their JSON API.
The directory structure follows the Danbooru20XX format of each subfolder for a record type being the record's ID modulo 1000. The '.zip' files can sometimes hold hundreds of thousands of small JSON files when put together, so use caution when extracting.
| [
"# danbooru-metadata\nDump of various portions danbooru's metadata, as of Feburary 2023. Everything was taken directly from their JSON API.\n\nThe directory structure follows the Danbooru20XX format of each subfolder for a record type being the record's ID modulo 1000. The '.zip' files can sometimes hold hundreds of thousands of small JSON files when put together, so use caution when extracting."
]
| [
"TAGS\n#region-us \n",
"# danbooru-metadata\nDump of various portions danbooru's metadata, as of Feburary 2023. Everything was taken directly from their JSON API.\n\nThe directory structure follows the Danbooru20XX format of each subfolder for a record type being the record's ID modulo 1000. The '.zip' files can sometimes hold hundreds of thousands of small JSON files when put together, so use caution when extracting."
]
| [
6,
99
]
| [
"passage: TAGS\n#region-us \n# danbooru-metadata\nDump of various portions danbooru's metadata, as of Feburary 2023. Everything was taken directly from their JSON API.\n\nThe directory structure follows the Danbooru20XX format of each subfolder for a record type being the record's ID modulo 1000. The '.zip' files can sometimes hold hundreds of thousands of small JSON files when put together, so use caution when extracting."
]
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|
c7cc0096855ca88268882f824569d4b3ce3f48ef | # Dataset Card for "diffusiondb_2m_first_5k_canny"
Process [diffusiondb 2m first 5k canny](https://huggingface.co/datasets/poloclub/diffusiondb) to edges by Canny algorithm.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
| HighCWu/diffusiondb_2m_first_5k_canny | [
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:openrail",
"region:us"
]
| 2023-02-16T14:16:14+00:00 | {"language": ["en"], "license": "openrail", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "guide", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3204091410, "num_examples": 5000}], "download_size": 3203076374, "dataset_size": 3204091410}} | 2023-02-16T14:53:35+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-openrail #region-us
| # Dataset Card for "diffusiondb_2m_first_5k_canny"
Process diffusiondb 2m first 5k canny to edges by Canny algorithm.
More Information needed
| [
"# Dataset Card for \"diffusiondb_2m_first_5k_canny\"\n\nProcess diffusiondb 2m first 5k canny to edges by Canny algorithm.\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-openrail #region-us \n",
"# Dataset Card for \"diffusiondb_2m_first_5k_canny\"\n\nProcess diffusiondb 2m first 5k canny to edges by Canny algorithm.\n\nMore Information needed"
]
| [
40,
45
]
| [
"passage: TAGS\n#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-openrail #region-us \n# Dataset Card for \"diffusiondb_2m_first_5k_canny\"\n\nProcess diffusiondb 2m first 5k canny to edges by Canny algorithm.\n\nMore Information needed"
]
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|
8086bd9dd6f601e50c44eddb7ecbc3a3c345571b |
 | xJunko/Eden | [
"region:us"
]
| 2023-02-16T15:34:29+00:00 | {"license": "other", "pretty_name": "Random LoRA(s)"} | 2023-05-07T16:31:19+00:00 | []
| []
| TAGS
#region-us
|
![](URL | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
4557380c0675f2ccb443c71c829de7dc7578efd1 | # Dataset Card for "wikitext103_VALUE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/wikitext103_VALUE | [
"region:us"
]
| 2023-02-16T15:42:47+00:00 | {"dataset_info": {"features": [{"name": "sentence-glue", "dtype": "string"}, {"name": "sentence-glue-html", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "sentence-ass", "dtype": "int64"}, {"name": "sentence-been_done", "dtype": "int64"}, {"name": "sentence-dey_it", "dtype": "int64"}, {"name": "sentence-drop_aux", "dtype": "int64"}, {"name": "sentence-got", "dtype": "int64"}, {"name": "sentence-lexical", "dtype": "int64"}, {"name": "sentence-negative_concord", "dtype": "int64"}, {"name": "sentence-negative_inversion", "dtype": "int64"}, {"name": "sentence-null_genetive", "dtype": "int64"}, {"name": "sentence-null_relcl", "dtype": "int64"}, {"name": "sentence-total", "dtype": "int64"}, {"name": "sentence-uninflect", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 4493075, "num_examples": 2891}, {"name": "train", "num_bytes": 1880407626, "num_examples": 1164310}, {"name": "validation", "num_bytes": 3962030, "num_examples": 2411}], "download_size": 988572681, "dataset_size": 1888862731}} | 2023-02-16T15:43:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikitext103_VALUE"
More Information needed | [
"# Dataset Card for \"wikitext103_VALUE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikitext103_VALUE\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikitext103_VALUE\"\n\nMore Information needed"
]
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|
bf85d5046c2cf17ba9c2dbebd52b24ad154e9207 | polinaeterna/audio_configs_default | [
"region:us"
]
| 2023-02-16T15:49:43+00:00 | {"configs_kwargs": {"data_dir": "v1", "drop_labels": true}, "duplicated_from": "polinaeterna/audio_configs2"} | 2023-02-16T17:01:39+00:00 | []
| []
| TAGS
#region-us
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
||
833a1ba016f422c65ed6ee990fe1db03f9597386 |
### RTHK News Dataset
(RTHK)[https://www.rthk.hk/] is a public broadcasting service under the Hong Kong Government according to (Wikipedia)[https://en.wikipedia.org/wiki/RTHK]
This dataset at the moment is obtained from exporting messages from their (telegram channel)[https://t.me/rthk_new_c],
which contains news since April 2018.
I will update this dataset with more data in the future. | jed351/rthk_news | [
"language:zh",
"region:us"
]
| 2023-02-16T16:44:01+00:00 | {"language": ["zh"]} | 2023-02-16T17:24:50+00:00 | []
| [
"zh"
]
| TAGS
#language-Chinese #region-us
|
### RTHK News Dataset
(RTHK)[URL is a public broadcasting service under the Hong Kong Government according to (Wikipedia)[URL
This dataset at the moment is obtained from exporting messages from their (telegram channel)[https://t.me/rthk_new_c],
which contains news since April 2018.
I will update this dataset with more data in the future. | [
"### RTHK News Dataset\n(RTHK)[URL is a public broadcasting service under the Hong Kong Government according to (Wikipedia)[URL\n\n\nThis dataset at the moment is obtained from exporting messages from their (telegram channel)[https://t.me/rthk_new_c], \nwhich contains news since April 2018. \n\n\nI will update this dataset with more data in the future."
]
| [
"TAGS\n#language-Chinese #region-us \n",
"### RTHK News Dataset\n(RTHK)[URL is a public broadcasting service under the Hong Kong Government according to (Wikipedia)[URL\n\n\nThis dataset at the moment is obtained from exporting messages from their (telegram channel)[https://t.me/rthk_new_c], \nwhich contains news since April 2018. \n\n\nI will update this dataset with more data in the future."
]
| [
11,
86
]
| [
"passage: TAGS\n#language-Chinese #region-us \n### RTHK News Dataset\n(RTHK)[URL is a public broadcasting service under the Hong Kong Government according to (Wikipedia)[URL\n\n\nThis dataset at the moment is obtained from exporting messages from their (telegram channel)[https://t.me/rthk_new_c], \nwhich contains news since April 2018. \n\n\nI will update this dataset with more data in the future."
]
| [
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|
a01c3fa42e2f2e17804738075cfdc6752f11f93a | # AutoTrain Dataset for project: air
## Dataset Description
This dataset has been automatically processed by AutoTrain for project air.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"target": 0.04100000113248825,
"id": 1,
"feat_split": "train"
},
{
"target": 0.04800000041723251,
"id": 2,
"feat_split": "train"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"target": "Value(dtype='float32', id=None)",
"id": "Value(dtype='int64', id=None)",
"feat_split": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1467 |
| valid | 1467 |
| abhishekDTU/autotrain-data-air | [
"region:us"
]
| 2023-02-16T16:45:02+00:00 | {} | 2023-02-16T17:09:28+00:00 | []
| []
| TAGS
#region-us
| AutoTrain Dataset for project: air
==================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project air.
### 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#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:"
]
| [
6,
27,
17,
23,
27
]
| [
"passage: TAGS\n#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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|
c299be4298b58f71d60f2718273b7c0d64d3aacd |
# Dataset Summary
This dataset contains all MQM human annotations from previous [WMT Metrics shared tasks](https://wmt-metrics-task.github.io/) and the MQM annotations from [Experts, Errors, and Context](https://aclanthology.org/2021.tacl-1.87/).
The data is organised into 8 columns:
- lp: language pair
- src: input text
- mt: translation
- ref: reference translation
- score: MQM score
- system: MT Engine that produced the translation
- annotators: number of annotators
- domain: domain of the input text (e.g. news)
- year: collection year
You can also find the original data [here](https://github.com/google/wmt-mqm-human-evaluation). We recommend using the original repo if you are interested in annotation spans and not just the final score.
## Python usage:
```python
from datasets import load_dataset
dataset = load_dataset("RicardoRei/wmt-mqm-human-evaluation", split="train")
```
There is no standard train/test split for this dataset but you can easily split it according to year, language pair or domain. E.g. :
```python
# split by year
data = dataset.filter(lambda example: example["year"] == 2022)
# split by LP
data = dataset.filter(lambda example: example["lp"] == "en-de")
# split by domain
data = dataset.filter(lambda example: example["domain"] == "ted")
```
## Citation Information
If you use this data please cite the following works:
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://aclanthology.org/2021.tacl-1.87/)
- [Results of the WMT21 Metrics Shared Task: Evaluating Metrics with Expert-based Human Evaluations on TED and News Domain](https://aclanthology.org/2021.wmt-1.73/)
- [Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust](https://aclanthology.org/2022.wmt-1.2/) | RicardoRei/wmt-mqm-human-evaluation | [
"size_categories:100K<n<1M",
"language:en",
"language:de",
"language:ru",
"language:zh",
"license:apache-2.0",
"mt-evaluation",
"WMT",
"region:us"
]
| 2023-02-16T17:14:16+00:00 | {"language": ["en", "de", "ru", "zh"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "tags": ["mt-evaluation", "WMT"]} | 2023-02-16T18:29:11+00:00 | []
| [
"en",
"de",
"ru",
"zh"
]
| TAGS
#size_categories-100K<n<1M #language-English #language-German #language-Russian #language-Chinese #license-apache-2.0 #mt-evaluation #WMT #region-us
|
# Dataset Summary
This dataset contains all MQM human annotations from previous WMT Metrics shared tasks and the MQM annotations from Experts, Errors, and Context.
The data is organised into 8 columns:
- lp: language pair
- src: input text
- mt: translation
- ref: reference translation
- score: MQM score
- system: MT Engine that produced the translation
- annotators: number of annotators
- domain: domain of the input text (e.g. news)
- year: collection year
You can also find the original data here. We recommend using the original repo if you are interested in annotation spans and not just the final score.
## Python usage:
There is no standard train/test split for this dataset but you can easily split it according to year, language pair or domain. E.g. :
If you use this data please cite the following works:
- Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation
- Results of the WMT21 Metrics Shared Task: Evaluating Metrics with Expert-based Human Evaluations on TED and News Domain
- Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust | [
"# Dataset Summary\n\nThis dataset contains all MQM human annotations from previous WMT Metrics shared tasks and the MQM annotations from Experts, Errors, and Context.\n\nThe data is organised into 8 columns:\n- lp: language pair\n- src: input text\n- mt: translation\n- ref: reference translation\n- score: MQM score\n- system: MT Engine that produced the translation\n- annotators: number of annotators\n- domain: domain of the input text (e.g. news)\n- year: collection year\n \nYou can also find the original data here. We recommend using the original repo if you are interested in annotation spans and not just the final score.",
"## Python usage:\n\n\n\nThere is no standard train/test split for this dataset but you can easily split it according to year, language pair or domain. E.g. :\n\n\n\n\n\nIf you use this data please cite the following works:\n- Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation\n- Results of the WMT21 Metrics Shared Task: Evaluating Metrics with Expert-based Human Evaluations on TED and News Domain\n- Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust"
]
| [
"TAGS\n#size_categories-100K<n<1M #language-English #language-German #language-Russian #language-Chinese #license-apache-2.0 #mt-evaluation #WMT #region-us \n",
"# Dataset Summary\n\nThis dataset contains all MQM human annotations from previous WMT Metrics shared tasks and the MQM annotations from Experts, Errors, and Context.\n\nThe data is organised into 8 columns:\n- lp: language pair\n- src: input text\n- mt: translation\n- ref: reference translation\n- score: MQM score\n- system: MT Engine that produced the translation\n- annotators: number of annotators\n- domain: domain of the input text (e.g. news)\n- year: collection year\n \nYou can also find the original data here. We recommend using the original repo if you are interested in annotation spans and not just the final score.",
"## Python usage:\n\n\n\nThere is no standard train/test split for this dataset but you can easily split it according to year, language pair or domain. E.g. :\n\n\n\n\n\nIf you use this data please cite the following works:\n- Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation\n- Results of the WMT21 Metrics Shared Task: Evaluating Metrics with Expert-based Human Evaluations on TED and News Domain\n- Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust"
]
| [
53,
154,
135
]
| [
"passage: TAGS\n#size_categories-100K<n<1M #language-English #language-German #language-Russian #language-Chinese #license-apache-2.0 #mt-evaluation #WMT #region-us \n# Dataset Summary\n\nThis dataset contains all MQM human annotations from previous WMT Metrics shared tasks and the MQM annotations from Experts, Errors, and Context.\n\nThe data is organised into 8 columns:\n- lp: language pair\n- src: input text\n- mt: translation\n- ref: reference translation\n- score: MQM score\n- system: MT Engine that produced the translation\n- annotators: number of annotators\n- domain: domain of the input text (e.g. news)\n- year: collection year\n \nYou can also find the original data here. We recommend using the original repo if you are interested in annotation spans and not just the final score.## Python usage:\n\n\n\nThere is no standard train/test split for this dataset but you can easily split it according to year, language pair or domain. E.g. :\n\n\n\n\n\nIf you use this data please cite the following works:\n- Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation\n- Results of the WMT21 Metrics Shared Task: Evaluating Metrics with Expert-based Human Evaluations on TED and News Domain\n- Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust"
]
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|
f95ffe58983638bf31a5c80d43687fdf1cba56e0 |
# 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
If you used the datasets and models in this repository, please cite it.
```bibtex
@misc{https://doi.org/10.48550/arxiv.2302.09611,
doi = {10.48550/ARXIV.2302.09611},
url = {https://arxiv.org/abs/2302.09611},
author = {Sartipi, Amir and Fatemi, Afsaneh},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English},
publisher = {arXiv},
year = {2023},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
### Contributions
[More Information Needed]
| Amir13/conll2003-persian | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|conll2003",
"language:fa",
"license:other",
"named entity recognition",
"arxiv:2302.09611",
"region:us"
]
| 2023-02-16T17:36:24+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["machine-generated"], "language": ["fa"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|conll2003"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "conll2003-persian", "tags": ["named entity recognition"], "train-eval-index": [{"col_mapping": {"ner_tags": "tags", "tokens": "tokens"}, "config": "conll2003", "metrics": [{"name": "seqeval", "type": "seqeval"}], "splits": {"eval_split": "test", "train_split": "train"}, "task": "token-classification", "task_id": "entity_extraction"}]} | 2023-02-21T06:54:17+00:00 | [
"2302.09611"
]
| [
"fa"
]
| TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|conll2003 #language-Persian #license-other #named entity recognition #arxiv-2302.09611 #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
If you used the datasets and models in this repository, please cite it.
### Contributions
| [
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
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"### Data Fields",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\n\n\n\n\n\nIf you used the datasets and models in this repository, please cite it.",
"### Contributions"
]
| [
"TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|conll2003 #language-Persian #license-other #named entity recognition #arxiv-2302.09611 #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
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"## Dataset Structure",
"### Data Instances",
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"### Data Splits",
"## Dataset Creation",
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"#### 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\n\n\n\n\n\nIf you used the datasets and models in this repository, please cite it.",
"### Contributions"
]
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|
97436e50a8873f5b236e1de91bb55465988fa748 | Come collect LoRAs from CivitAI for all your generating needs!
Explore the SafeDump for SFW LoRAs or dive head-deep into the CumDump for... well, I think you get it.
Disclaimer: Absolutely none of these LoRAs belong to me. I am uploading these files here for my own personal use.
Support their creators by liking their works and following them on civitai.com
Enjoy!
---
license: other
---
| Ubque/The_LoRA_Dump | [
"region:us"
]
| 2023-02-16T17:43:24+00:00 | {} | 2023-03-01T03:32:57+00:00 | []
| []
| TAGS
#region-us
| Come collect LoRAs from CivitAI for all your generating needs!
Explore the SafeDump for SFW LoRAs or dive head-deep into the CumDump for... well, I think you get it.
Disclaimer: Absolutely none of these LoRAs belong to me. I am uploading these files here for my own personal use.
Support their creators by liking their works and following them on URL
Enjoy!
---
license: other
---
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
| [
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|
00b823e0df95a3ad565436da3a9874cb18bf625e | # Dataset Card for "miniwobplusplus_episodes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LucasThil/miniwobplusplus_episodes | [
"region:us"
]
| 2023-02-16T17:59:24+00:00 | {"dataset_info": {"features": [{"name": "episodes", "dtype": "string"}, {"name": "actions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3384285009, "num_examples": 16794}], "download_size": 276652178, "dataset_size": 3384285009}} | 2023-02-16T18:09:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "miniwobplusplus_episodes"
More Information needed | [
"# Dataset Card for \"miniwobplusplus_episodes\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"miniwobplusplus_episodes\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"miniwobplusplus_episodes\"\n\nMore Information needed"
]
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|
ab45e601c6c268738bbce1c95974000f96cdf294 | # Dataset Card for "icdar2023vqabd-small-tables-train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | joytafty/icdar2023vqabd-small-tables-train | [
"region:us"
]
| 2023-02-16T18:09:16+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3153797.0, "num_examples": 244}], "download_size": 2872591, "dataset_size": 3153797.0}} | 2023-02-16T18:09:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "icdar2023vqabd-small-tables-train"
More Information needed | [
"# Dataset Card for \"icdar2023vqabd-small-tables-train\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"icdar2023vqabd-small-tables-train\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"icdar2023vqabd-small-tables-train\"\n\nMore Information needed"
]
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|
bc3acfc5a0fda60e6705161469e54a204381a1b4 | # Dataset Card for "icdar2023vqabd-small-tables-val"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | joytafty/icdar2023vqabd-small-tables-val | [
"region:us"
]
| 2023-02-16T18:09:22+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "validation", "num_bytes": 305631.0, "num_examples": 19}], "download_size": 274240, "dataset_size": 305631.0}} | 2023-02-16T18:09:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "icdar2023vqabd-small-tables-val"
More Information needed | [
"# Dataset Card for \"icdar2023vqabd-small-tables-val\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"icdar2023vqabd-small-tables-val\"\n\nMore Information needed"
]
| [
6,
25
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"icdar2023vqabd-small-tables-val\"\n\nMore Information needed"
]
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|
bad9ab569d347d88e8e48bc69c15af32c6ce8495 | # 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
If you used the datasets and models in this repository, please cite it.
```bibtex
@misc{https://doi.org/10.48550/arxiv.2302.09611,
doi = {10.48550/ARXIV.2302.09611},
url = {https://arxiv.org/abs/2302.09611},
author = {Sartipi, Amir and Fatemi, Afsaneh},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English},
publisher = {arXiv},
year = {2023},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
### Contributions
[More Information Needed]
| Amir13/ontonotes5-persian | [
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#### Who are the source language producers?
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#### 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
If you used the datasets and models in this repository, please cite it.
### Contributions
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|
7c2a91c95ae7ebaaa8d24092754b1069afdff612 |
# 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
If you used the datasets and models in this repository, please cite it.
```bibtex
@misc{https://doi.org/10.48550/arxiv.2302.09611,
doi = {10.48550/ARXIV.2302.09611},
url = {https://arxiv.org/abs/2302.09611},
author = {Sartipi, Amir and Fatemi, Afsaneh},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English},
publisher = {arXiv},
year = {2023},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
### Contributions
[More Information Needed]
| Amir13/wnut2017-persian | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:fa",
"license:other",
"named entity recognition",
"arxiv:2302.09611",
"region:us"
]
| 2023-02-16T18:25:53+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["machine-generated"], "language": ["fa"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "wnut2017-persian", "tags": ["named entity recognition"]} | 2023-02-21T06:55:18+00:00 | [
"2302.09611"
]
| [
"fa"
]
| TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #language-Persian #license-other #named entity recognition #arxiv-2302.09611 #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
If you used the datasets and models in this repository, please cite it.
### Contributions
| [
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\n\n\n\n\n\nIf you used the datasets and models in this repository, please cite it.",
"### Contributions"
]
| [
"TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #language-Persian #license-other #named entity recognition #arxiv-2302.09611 #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\n\n\n\n\n\nIf you used the datasets and models in this repository, please cite it.",
"### Contributions"
]
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|
c337ba66b452b10b7fafc8e4da54302c6f785e2d |
# 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
If you used the datasets and models in this repository, please cite it.
```bibtex
@misc{https://doi.org/10.48550/arxiv.2302.09611,
doi = {10.48550/ARXIV.2302.09611},
url = {https://arxiv.org/abs/2302.09611},
author = {Sartipi, Amir and Fatemi, Afsaneh},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English},
publisher = {arXiv},
year = {2023},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
### Contributions
[More Information Needed]
| Amir13/ncbi-persian | [
"task_categories:token-classification",
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"license:other",
"named entity recognition",
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"region:us"
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| 2023-02-16T18:31:51+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["machine-generated"], "language": ["fa"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|ncbi_disease"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "ncbi-persian", "tags": ["named entity recognition"], "train-eval-index": [{"col_mapping": {"ner_tags": "target", "tokens": "text"}, "config": "ncbi_disease", "metrics": [{"name": "Accuracy", "type": "accuracy"}, {"args": {"average": "macro"}, "name": "F1 macro", "type": "f1"}, {"args": {"average": "micro"}, "name": "F1 micro", "type": "f1"}, {"args": {"average": "weighted"}, "name": "F1 weighted", "type": "f1"}, {"args": {"average": "macro"}, "name": "Precision macro", "type": "precision"}, {"args": {"average": "micro"}, "name": "Precision micro", "type": "precision"}, {"args": {"average": "weighted"}, "name": "Precision weighted", "type": "precision"}, {"args": {"average": "macro"}, "name": "Recall macro", "type": "recall"}, {"args": {"average": "micro"}, "name": "Recall micro", "type": "recall"}, {"args": {"average": "weighted"}, "name": "Recall weighted", "type": "recall"}], "splits": {"eval_split": "test", "train_split": "train"}, "task": "token-classification"}]} | 2023-02-21T06:55:44+00:00 | [
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|
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## Dataset Description
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### Curation Rationale
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#### 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
If you used the datasets and models in this repository, please cite it.
### Contributions
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|
301de385bf05b0c00a8f4be74965e186164dd425 |
# Dataset Summary
This dataset contains all DA human annotations from previous WMT News Translation shared tasks.
The data is organised into 8 columns:
- lp: language pair
- src: input text
- mt: translation
- ref: reference translation
- score: z score
- raw: direct assessment
- annotators: number of annotators
- domain: domain of the input text (e.g. news)
- year: collection year
You can also find the original data for each year in the results section https://www.statmt.org/wmt{YEAR}/results.html e.g: for 2020 data: [https://www.statmt.org/wmt20/results.html](https://www.statmt.org/wmt20/results.html)
## Python usage:
```python
from datasets import load_dataset
dataset = load_dataset("RicardoRei/wmt-da-human-evaluation", split="train")
```
There is no standard train/test split for this dataset but you can easily split it according to year, language pair or domain. E.g. :
```python
# split by year
data = dataset.filter(lambda example: example["year"] == 2022)
# split by LP
data = dataset.filter(lambda example: example["lp"] == "en-de")
# split by domain
data = dataset.filter(lambda example: example["domain"] == "news")
```
Note that most data is from News domain.
## Citation Information
If you use this data please cite the WMT findings from previous years:
- [Findings of the 2017 Conference on Machine Translation (WMT17)](https://aclanthology.org/W17-4717.pdf)
- [Findings of the 2018 Conference on Machine Translation (WMT18)](https://aclanthology.org/W18-6401.pdf)
- [Findings of the 2019 Conference on Machine Translation (WMT19)](https://aclanthology.org/W19-5301.pdf)
- [Findings of the 2020 Conference on Machine Translation (WMT20)](https://aclanthology.org/2020.wmt-1.1.pdf)
- [Findings of the 2021 Conference on Machine Translation (WMT21)](https://aclanthology.org/2021.wmt-1.1.pdf)
- [Findings of the 2022 Conference on Machine Translation (WMT22)](https://aclanthology.org/2022.wmt-1.1.pdf) | RicardoRei/wmt-da-human-evaluation | [
"size_categories:1M<n<10M",
"language:bn",
"language:cs",
"language:de",
"language:en",
"language:et",
"language:fi",
"language:fr",
"language:gu",
"language:ha",
"language:hi",
"language:is",
"language:ja",
"language:kk",
"language:km",
"language:lt",
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"language:pl",
"language:ps",
"language:ru",
"language:ta",
"language:tr",
"language:uk",
"language:xh",
"language:zh",
"language:zu",
"license:apache-2.0",
"mt-evaluation",
"WMT",
"41-lang-pairs",
"region:us"
]
| 2023-02-16T18:49:07+00:00 | {"language": ["bn", "cs", "de", "en", "et", "fi", "fr", "gu", "ha", "hi", "is", "ja", "kk", "km", "lt", "lv", "pl", "ps", "ru", "ta", "tr", "uk", "xh", "zh", "zu"], "license": "apache-2.0", "size_categories": ["1M<n<10M"], "tags": ["mt-evaluation", "WMT", "41-lang-pairs"]} | 2023-02-17T10:41:18+00:00 | []
| [
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]
| TAGS
#size_categories-1M<n<10M #language-Bengali #language-Czech #language-German #language-English #language-Estonian #language-Finnish #language-French #language-Gujarati #language-Hausa #language-Hindi #language-Icelandic #language-Japanese #language-Kazakh #language-Khmer #language-Lithuanian #language-Latvian #language-Polish #language-Pushto #language-Russian #language-Tamil #language-Turkish #language-Ukrainian #language-Xhosa #language-Chinese #language-Zulu #license-apache-2.0 #mt-evaluation #WMT #41-lang-pairs #region-us
|
# Dataset Summary
This dataset contains all DA human annotations from previous WMT News Translation shared tasks.
The data is organised into 8 columns:
- lp: language pair
- src: input text
- mt: translation
- ref: reference translation
- score: z score
- raw: direct assessment
- annotators: number of annotators
- domain: domain of the input text (e.g. news)
- year: collection year
You can also find the original data for each year in the results section URL e.g: for 2020 data: URL
## Python usage:
There is no standard train/test split for this dataset but you can easily split it according to year, language pair or domain. E.g. :
Note that most data is from News domain.
If you use this data please cite the WMT findings from previous years:
- Findings of the 2017 Conference on Machine Translation (WMT17)
- Findings of the 2018 Conference on Machine Translation (WMT18)
- Findings of the 2019 Conference on Machine Translation (WMT19)
- Findings of the 2020 Conference on Machine Translation (WMT20)
- Findings of the 2021 Conference on Machine Translation (WMT21)
- Findings of the 2022 Conference on Machine Translation (WMT22) | [
"# Dataset Summary\n\nThis dataset contains all DA human annotations from previous WMT News Translation shared tasks.\n\nThe data is organised into 8 columns:\n- lp: language pair\n- src: input text\n- mt: translation\n- ref: reference translation\n- score: z score\n- raw: direct assessment\n- annotators: number of annotators\n- domain: domain of the input text (e.g. news)\n- year: collection year\n \nYou can also find the original data for each year in the results section URL e.g: for 2020 data: URL",
"## Python usage:\n\n\n\nThere is no standard train/test split for this dataset but you can easily split it according to year, language pair or domain. E.g. :\n\n\n\nNote that most data is from News domain.\n\n\n\n\nIf you use this data please cite the WMT findings from previous years:\n- Findings of the 2017 Conference on Machine Translation (WMT17)\n- Findings of the 2018 Conference on Machine Translation (WMT18)\n- Findings of the 2019 Conference on Machine Translation (WMT19)\n- Findings of the 2020 Conference on Machine Translation (WMT20)\n- Findings of the 2021 Conference on Machine Translation (WMT21)\n- Findings of the 2022 Conference on Machine Translation (WMT22)"
]
| [
"TAGS\n#size_categories-1M<n<10M #language-Bengali #language-Czech #language-German #language-English #language-Estonian #language-Finnish #language-French #language-Gujarati #language-Hausa #language-Hindi #language-Icelandic #language-Japanese #language-Kazakh #language-Khmer #language-Lithuanian #language-Latvian #language-Polish #language-Pushto #language-Russian #language-Tamil #language-Turkish #language-Ukrainian #language-Xhosa #language-Chinese #language-Zulu #license-apache-2.0 #mt-evaluation #WMT #41-lang-pairs #region-us \n",
"# Dataset Summary\n\nThis dataset contains all DA human annotations from previous WMT News Translation shared tasks.\n\nThe data is organised into 8 columns:\n- lp: language pair\n- src: input text\n- mt: translation\n- ref: reference translation\n- score: z score\n- raw: direct assessment\n- annotators: number of annotators\n- domain: domain of the input text (e.g. news)\n- year: collection year\n \nYou can also find the original data for each year in the results section URL e.g: for 2020 data: URL",
"## Python usage:\n\n\n\nThere is no standard train/test split for this dataset but you can easily split it according to year, language pair or domain. E.g. :\n\n\n\nNote that most data is from News domain.\n\n\n\n\nIf you use this data please cite the WMT findings from previous years:\n- Findings of the 2017 Conference on Machine Translation (WMT17)\n- Findings of the 2018 Conference on Machine Translation (WMT18)\n- Findings of the 2019 Conference on Machine Translation (WMT19)\n- Findings of the 2020 Conference on Machine Translation (WMT20)\n- Findings of the 2021 Conference on Machine Translation (WMT21)\n- Findings of the 2022 Conference on Machine Translation (WMT22)"
]
| [
178,
123,
145
]
| [
"passage: TAGS\n#size_categories-1M<n<10M #language-Bengali #language-Czech #language-German #language-English #language-Estonian #language-Finnish #language-French #language-Gujarati #language-Hausa #language-Hindi #language-Icelandic #language-Japanese #language-Kazakh #language-Khmer #language-Lithuanian #language-Latvian #language-Polish #language-Pushto #language-Russian #language-Tamil #language-Turkish #language-Ukrainian #language-Xhosa #language-Chinese #language-Zulu #license-apache-2.0 #mt-evaluation #WMT #41-lang-pairs #region-us \n# Dataset Summary\n\nThis dataset contains all DA human annotations from previous WMT News Translation shared tasks.\n\nThe data is organised into 8 columns:\n- lp: language pair\n- src: input text\n- mt: translation\n- ref: reference translation\n- score: z score\n- raw: direct assessment\n- annotators: number of annotators\n- domain: domain of the input text (e.g. news)\n- year: collection year\n \nYou can also find the original data for each year in the results section URL e.g: for 2020 data: URL## Python usage:\n\n\n\nThere is no standard train/test split for this dataset but you can easily split it according to year, language pair or domain. E.g. :\n\n\n\nNote that most data is from News domain.\n\n\n\n\nIf you use this data please cite the WMT findings from previous years:\n- Findings of the 2017 Conference on Machine Translation (WMT17)\n- Findings of the 2018 Conference on Machine Translation (WMT18)\n- Findings of the 2019 Conference on Machine Translation (WMT19)\n- Findings of the 2020 Conference on Machine Translation (WMT20)\n- Findings of the 2021 Conference on Machine Translation (WMT21)\n- Findings of the 2022 Conference on Machine Translation (WMT22)"
]
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|
ce9bd9084eb48db58311f5b5dd8f5cbd942d9039 | # Dataset Card for "miniwobplusplus_ready"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LucasThil/miniwobplusplus_ready | [
"region:us"
]
| 2023-02-16T19:09:31+00:00 | {"dataset_info": {"features": [{"name": "episodes", "dtype": "string"}, {"name": "actions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3387412534, "num_examples": 815482}], "download_size": 288210838, "dataset_size": 3387412534}} | 2023-02-16T19:25:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "miniwobplusplus_ready"
More Information needed | [
"# Dataset Card for \"miniwobplusplus_ready\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"miniwobplusplus_ready\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"miniwobplusplus_ready\"\n\nMore Information needed"
]
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|
e4c0747eef9da8eb14de6036a1396f3e8c9634cf | # Dataset Card for "home_depot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[source](https://www.kaggle.com/competitions/home-depot-product-search-relevance)
Dataset Description
This data set contains a number of products and real customer search terms from Home Depot's website. The challenge is to predict a relevance score for the provided combinations of search terms and products. To create the ground truth labels, Home Depot has crowdsourced the search/product pairs to multiple human raters.
The relevance is a number between 1 (not relevant) to 3 (highly relevant). For example, a search for "AA battery" would be considered highly relevant to a pack of size AA batteries (relevance = 3), mildly relevant to a cordless drill battery (relevance = 2), and not relevant to a snow shovel (relevance = 1).
Each pair was evaluated by at least three human raters. The provided relevance scores are the average value of the ratings. There are three additional things to know about the ratings:
The specific instructions given to the raters is provided in relevance_instructions.docx.
Raters did not have access to the attributes.
Raters had access to product images, while the competition does not include images.
Your task is to predict the relevance for each pair listed in the test set. Note that the test set contains both seen and unseen search terms.
| bstds/home_depot | [
"region:us"
]
| 2023-02-16T20:34:34+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "entity_id", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "relevance", "dtype": "float64"}, {"name": "description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 74803048, "num_examples": 74067}], "download_size": 32449185, "dataset_size": 74803048}} | 2023-02-16T20:35:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "home_depot"
More Information needed
source
Dataset Description
This data set contains a number of products and real customer search terms from Home Depot's website. The challenge is to predict a relevance score for the provided combinations of search terms and products. To create the ground truth labels, Home Depot has crowdsourced the search/product pairs to multiple human raters.
The relevance is a number between 1 (not relevant) to 3 (highly relevant). For example, a search for "AA battery" would be considered highly relevant to a pack of size AA batteries (relevance = 3), mildly relevant to a cordless drill battery (relevance = 2), and not relevant to a snow shovel (relevance = 1).
Each pair was evaluated by at least three human raters. The provided relevance scores are the average value of the ratings. There are three additional things to know about the ratings:
The specific instructions given to the raters is provided in relevance_instructions.docx.
Raters did not have access to the attributes.
Raters had access to product images, while the competition does not include images.
Your task is to predict the relevance for each pair listed in the test set. Note that the test set contains both seen and unseen search terms.
| [
"# Dataset Card for \"home_depot\"\n\nMore Information needed\n\nsource\n\nDataset Description\nThis data set contains a number of products and real customer search terms from Home Depot's website. The challenge is to predict a relevance score for the provided combinations of search terms and products. To create the ground truth labels, Home Depot has crowdsourced the search/product pairs to multiple human raters.\n\nThe relevance is a number between 1 (not relevant) to 3 (highly relevant). For example, a search for \"AA battery\" would be considered highly relevant to a pack of size AA batteries (relevance = 3), mildly relevant to a cordless drill battery (relevance = 2), and not relevant to a snow shovel (relevance = 1).\n\nEach pair was evaluated by at least three human raters. The provided relevance scores are the average value of the ratings. There are three additional things to know about the ratings:\n\nThe specific instructions given to the raters is provided in relevance_instructions.docx.\nRaters did not have access to the attributes.\nRaters had access to product images, while the competition does not include images.\nYour task is to predict the relevance for each pair listed in the test set. Note that the test set contains both seen and unseen search terms."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"home_depot\"\n\nMore Information needed\n\nsource\n\nDataset Description\nThis data set contains a number of products and real customer search terms from Home Depot's website. The challenge is to predict a relevance score for the provided combinations of search terms and products. To create the ground truth labels, Home Depot has crowdsourced the search/product pairs to multiple human raters.\n\nThe relevance is a number between 1 (not relevant) to 3 (highly relevant). For example, a search for \"AA battery\" would be considered highly relevant to a pack of size AA batteries (relevance = 3), mildly relevant to a cordless drill battery (relevance = 2), and not relevant to a snow shovel (relevance = 1).\n\nEach pair was evaluated by at least three human raters. The provided relevance scores are the average value of the ratings. There are three additional things to know about the ratings:\n\nThe specific instructions given to the raters is provided in relevance_instructions.docx.\nRaters did not have access to the attributes.\nRaters had access to product images, while the competition does not include images.\nYour task is to predict the relevance for each pair listed in the test set. Note that the test set contains both seen and unseen search terms."
]
| [
6,
287
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"home_depot\"\n\nMore Information needed\n\nsource\n\nDataset Description\nThis data set contains a number of products and real customer search terms from Home Depot's website. The challenge is to predict a relevance score for the provided combinations of search terms and products. To create the ground truth labels, Home Depot has crowdsourced the search/product pairs to multiple human raters.\n\nThe relevance is a number between 1 (not relevant) to 3 (highly relevant). For example, a search for \"AA battery\" would be considered highly relevant to a pack of size AA batteries (relevance = 3), mildly relevant to a cordless drill battery (relevance = 2), and not relevant to a snow shovel (relevance = 1).\n\nEach pair was evaluated by at least three human raters. The provided relevance scores are the average value of the ratings. There are three additional things to know about the ratings:\n\nThe specific instructions given to the raters is provided in relevance_instructions.docx.\nRaters did not have access to the attributes.\nRaters had access to product images, while the competition does not include images.\nYour task is to predict the relevance for each pair listed in the test set. Note that the test set contains both seen and unseen search terms."
]
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|
3f63b31100a09af8e3f2c320f27c8eadaa0e910d | # Dataset Card for "large-algae-wirs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | samitizerxu/large-algae-wirs | [
"region:us"
]
| 2023-02-16T20:49:51+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "1", "1": "2", "2": "3", "3": "4", "4": "5", "5": "test"}}}}, {"name": "uid", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 390000704.53, "num_examples": 17035}, {"name": "test", "num_bytes": 140940912.244, "num_examples": 6494}], "download_size": 520667798, "dataset_size": 530941616.7739999}} | 2023-02-17T02:44:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "large-algae-wirs"
More Information needed | [
"# Dataset Card for \"large-algae-wirs\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"large-algae-wirs\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"large-algae-wirs\"\n\nMore Information needed"
]
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|
8eb50ecb70db4ac1a7184b5506396cc542b4c664 | # Dataset Card for "large-algae-rgb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | samitizerxu/large-algae-rgb | [
"region:us"
]
| 2023-02-16T20:54:05+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "1", "1": "2", "2": "3", "3": "4", "4": "5", "5": "test"}}}}, {"name": "uid", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 344037940.735, "num_examples": 17035}, {"name": "test", "num_bytes": 128411265.258, "num_examples": 6494}], "download_size": 461637680, "dataset_size": 472449205.99300003}} | 2023-02-17T02:39:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "large-algae-rgb"
More Information needed | [
"# Dataset Card for \"large-algae-rgb\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"large-algae-rgb\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"large-algae-rgb\"\n\nMore Information needed"
]
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|
96beb9a3390e597560c1fedb72b81e244bc00856 | # Dataset Card for "sq-babi_nli_indefinite-knowledge"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | niv-al/sq-babi_nli_indefinite-knowledge | [
"language:sq",
"region:us"
]
| 2023-02-16T20:58:43+00:00 | {"language": ["sq"], "dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "not-entailed", "1": "entailed"}}}}], "splits": [{"name": "train", "num_bytes": 160256, "num_examples": 1000}, {"name": "validation", "num_bytes": 23468, "num_examples": 144}, {"name": "test", "num_bytes": 23128, "num_examples": 144}], "download_size": 41242, "dataset_size": 206852}} | 2023-02-18T20:00:06+00:00 | []
| [
"sq"
]
| TAGS
#language-Albanian #region-us
| # Dataset Card for "sq-babi_nli_indefinite-knowledge"
More Information needed | [
"# Dataset Card for \"sq-babi_nli_indefinite-knowledge\"\n\nMore Information needed"
]
| [
"TAGS\n#language-Albanian #region-us \n",
"# Dataset Card for \"sq-babi_nli_indefinite-knowledge\"\n\nMore Information needed"
]
| [
11,
25
]
| [
"passage: TAGS\n#language-Albanian #region-us \n# Dataset Card for \"sq-babi_nli_indefinite-knowledge\"\n\nMore Information needed"
]
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|
b52e930385cf5ed7f063072c3f7bd17b599a16cf |
# Dataset Card for AfriSenti Dataset
<p align="center">
<img src="https://raw.githubusercontent.com/afrisenti-semeval/afrisent-semeval-2023/main/images/afrisenti-twitter.png", width="700" height="500">
--------------------------------------------------------------------------------
## Dataset Description
- **Homepage:** https://github.com/afrisenti-semeval/afrisent-semeval-2023
- **Repository:** [GitHub](https://github.com/afrisenti-semeval/afrisent-semeval-2023)
- **Paper:** [AfriSenti: AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://arxiv.org/pdf/2302.08956.pdf)
- **Paper:** [SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)](https://arxiv.org/pdf/2304.06845.pdf)
- **Paper:** [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://arxiv.org/pdf/2201.08277.pdf)
- **Leaderboard:** N/A
- **Point of Contact:** [shamsuddeen Muhammad]([email protected])
### Dataset Summary
AfriSenti is the largest sentiment analysis dataset for under-represented African languages, covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba).
The datasets are used in the first Afrocentric SemEval shared task, SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval). AfriSenti allows the research community to build sentiment analysis systems for various African languages and enables the study of sentiment and contemporary language use in African languages.
### Supported Tasks and Leaderboards
The AfriSenti can be used for a wide range of sentiment analysis tasks in African languages, such as sentiment classification, sentiment intensity analysis, and emotion detection. This dataset is suitable for training and evaluating machine learning models for various NLP tasks related to sentiment analysis in African languages.
[SemEval 2023 Task 12 : Sentiment Analysis for African Languages](https://codalab.lisn.upsaclay.fr/competitions/7320)
### Languages
14 African languages (Amharic (amh), Algerian Arabic (ary), Hausa(hau), Igbo(ibo), Kinyarwanda(kin), Moroccan Arabic/Darija(arq), Mozambican Portuguese(por), Nigerian Pidgin (pcm), Oromo (oro), Swahili(swa), Tigrinya(tir), Twi(twi), Xitsonga(tso), and Yoruba(yor)).
## Dataset Structure
### Data Instances
For each instance, there is a string for the tweet and a string for the label. See the AfriSenti [dataset viewer](https://huggingface.co/datasets/shmuhammad/AfriSenti/viewer/shmuhammad--AfriSenti/train) to explore more examples.
```
{
"tweet": "string",
"label": "string"
}
```
### Data Fields
The data fields are:
```
tweet: a string feature.
label: a classification label, with possible values including positive, negative and neutral.
```
### Data Splits
The AfriSenti dataset has 3 splits: train, validation, and test. Below are the statistics for Version 1.0.0 of the dataset.
| | ama | arq | hau | ibo | ary | orm | pcm | pt-MZ | kin | swa | tir | tso | twi | yo |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| train | 5,982 | 1,652 | 14,173 | 10,193 | 5,584| - | 5,122 | 3,064 | 3,303 | 1,811 | - | 805 | 3,482| 8,523 |
| dev | 1,498 | 415 | 2,678 | 1,842 | 1,216 | 397 | 1,282 | 768 | 828 | 454 | 399 | 204 | 389 | 2,091 |
| test | 2,000 | 959 | 5,304 | 3,683 | 2,962 | 2,097 | 4,155 | 3,663 | 1,027 | 749 | 2,001 | 255 | 950 | 4,516 |
| total | 9,483 | 3,062 | 22,155 | 15,718 | 9,762 | 2,494 | 10,559 | 7,495 | 5,158 | 3,014 | 2,400 | 1,264 | 4,821 | 15,130 |
### How to use it
```python
from datasets import load_dataset
# you can load specific languages (e.g., Amharic). This download train, validation and test sets.
ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh")
# train set only
ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh", split = "train")
# test set only
ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh", split = "test")
# validation set only
ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh", split = "validation")
```
## Dataset Creation
### Curation Rationale
AfriSenti Version 1.0.0 aimed to be used in the first Afrocentric SemEval shared task **[SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval)](https://afrisenti-semeval.github.io)**.
### Source Data
Twitter
#### 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
We anonymized the tweets by replacing all *@mentions* by *@user* and removed all URLs.
## Considerations for Using the Data
### Social Impact of Dataset
The Afrisenti dataset has the potential to improve sentiment analysis for African languages, which is essential for understanding and analyzing the diverse perspectives of people in the African continent. This dataset can enable researchers and developers to create sentiment analysis models that are specific to African languages, which can be used to gain insights into the social, cultural, and political views of people in African countries. Furthermore, this dataset can help address the issue of underrepresentation of African languages in natural language processing, paving the way for more equitable and inclusive AI technologies.
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
AfriSenti is an extension of NaijaSenti, a dataset consisting of four Nigerian languages: Hausa, Yoruba, Igbo, and Nigerian-Pidgin. This dataset has been expanded to include other 10 African languages, and was curated with the help of the following:
| Language | Dataset Curators |
|---|---|
| Algerian Arabic (arq) | Nedjma Ousidhoum, Meriem Beloucif |
| Amharic (ama) | Abinew Ali Ayele, Seid Muhie Yimam |
| Hausa (hau) | Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello |
| Igbo (ibo) | Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello |
| Kinyarwanda (kin)| Samuel Rutunda |
| Moroccan Arabic/Darija (ary) | Oumaima Hourrane |
| Mozambique Portuguese (pt-MZ) | Felermino Dário Mário António Ali |
| Nigerian Pidgin (pcm) | Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello |
| Oromo (orm) | Abinew Ali Ayele, Seid Muhie Yimam, Hagos Tesfahun Gebremichael, Sisay Adugna Chala, Hailu Beshada Balcha, Wendimu Baye Messell, Tadesse Belay |
| Swahili (swa) | Davis Davis |
| Tigrinya (tir) | Abinew Ali Ayele, Seid Muhie Yimam, Hagos Tesfahun Gebremichael, Sisay Adugna Chala, Hailu Beshada Balcha, Wendimu Baye Messell, Tadesse Belay |
| Twi (twi) | Salomey Osei, Bernard Opoku, Steven Arthur |
| Xithonga (tso) | Felermino Dário Mário António Ali |
| Yoruba (yor) | Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello |
### Licensing Information
This AfriSenti is licensed under a Creative Commons Attribution 4.0 International License
### Citation Information
```
@inproceedings{Muhammad2023AfriSentiAT,
title={AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages},
author={Shamsuddeen Hassan Muhammad and Idris Abdulmumin and Abinew Ali Ayele and Nedjma Ousidhoum and David Ifeoluwa Adelani and Seid Muhie Yimam and Ibrahim Sa'id Ahmad and Meriem Beloucif and Saif Mohammad and Sebastian Ruder and Oumaima Hourrane and Pavel Brazdil and Felermino D'ario M'ario Ant'onio Ali and Davis Davis and Salomey Osei and Bello Shehu Bello and Falalu Ibrahim and Tajuddeen Gwadabe and Samuel Rutunda and Tadesse Belay and Wendimu Baye Messelle and Hailu Beshada Balcha and Sisay Adugna Chala and Hagos Tesfahun Gebremichael and Bernard Opoku and Steven Arthur},
year={2023}
}
```
```
@article{muhammad2023semeval,
title={SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)},
author={Muhammad, Shamsuddeen Hassan and Abdulmumin, Idris and Yimam, Seid Muhie and Adelani, David Ifeoluwa and Ahmad, Ibrahim Sa'id and Ousidhoum, Nedjma and Ayele, Abinew and Mohammad, Saif M and Beloucif, Meriem},
journal={arXiv preprint arXiv:2304.06845},
year={2023}
}
```
### Contributions
[More Information Needed] | shmuhammad/AfriSenti-twitter-sentiment | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"language:amh",
"language:ary",
"language:ar",
"language:arq",
"language:hau",
"language:ibo",
"language:kin",
"language:por",
"language:pcm",
"language:eng",
"language:oro",
"language:swa",
"language:tir",
"language:twi",
"language:tso",
"language:yor",
"sentiment analysis, Twitter, tweets",
"sentiment",
"arxiv:2302.08956",
"arxiv:2304.06845",
"arxiv:2201.08277",
"region:us"
]
| 2023-02-16T21:02:20+00:00 | {"language": ["amh", "ary", "ar", "arq", "hau", "ibo", "kin", "por", "pcm", "eng", "oro", "swa", "tir", "twi", "tso", "yor"], "multilinguality": ["monolingual", "multilingual"], "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis", "sentiment-classification", "sentiment-scoring", "semantic-similarity-classification", "semantic-similarity-scoring"], "pretty_name": "AfriSenti", "tags": ["sentiment analysis, Twitter, tweets", "sentiment"]} | 2023-09-03T08:59:15+00:00 | [
"2302.08956",
"2304.06845",
"2201.08277"
]
| [
"amh",
"ary",
"ar",
"arq",
"hau",
"ibo",
"kin",
"por",
"pcm",
"eng",
"oro",
"swa",
"tir",
"twi",
"tso",
"yor"
]
| TAGS
#task_categories-text-classification #task_ids-sentiment-analysis #task_ids-sentiment-classification #task_ids-sentiment-scoring #task_ids-semantic-similarity-classification #task_ids-semantic-similarity-scoring #multilinguality-monolingual #multilinguality-multilingual #size_categories-100K<n<1M #language-Amharic #language-Moroccan Arabic #language-Arabic #language-Algerian Arabic #language-Hausa #language-Igbo #language-Kinyarwanda #language-Portuguese #language-Nigerian Pidgin #language-English #language-Orokolo #language-Swahili (macrolanguage) #language-Tigrinya #language-Twi #language-Tsonga #language-Yoruba #sentiment analysis, Twitter, tweets #sentiment #arxiv-2302.08956 #arxiv-2304.06845 #arxiv-2201.08277 #region-us
| Dataset Card for AfriSenti Dataset
==================================
---
Dataset Description
-------------------
* Homepage: URL
* Repository: GitHub
* Paper: AfriSenti: AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
* Paper: SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)
* Paper: NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis
* Leaderboard: N/A
* Point of Contact: shamsuddeen Muhammad
### Dataset Summary
AfriSenti is the largest sentiment analysis dataset for under-represented African languages, covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba).
The datasets are used in the first Afrocentric SemEval shared task, SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval). AfriSenti allows the research community to build sentiment analysis systems for various African languages and enables the study of sentiment and contemporary language use in African languages.
### Supported Tasks and Leaderboards
The AfriSenti can be used for a wide range of sentiment analysis tasks in African languages, such as sentiment classification, sentiment intensity analysis, and emotion detection. This dataset is suitable for training and evaluating machine learning models for various NLP tasks related to sentiment analysis in African languages.
SemEval 2023 Task 12 : Sentiment Analysis for African Languages
### Languages
14 African languages (Amharic (amh), Algerian Arabic (ary), Hausa(hau), Igbo(ibo), Kinyarwanda(kin), Moroccan Arabic/Darija(arq), Mozambican Portuguese(por), Nigerian Pidgin (pcm), Oromo (oro), Swahili(swa), Tigrinya(tir), Twi(twi), Xitsonga(tso), and Yoruba(yor)).
Dataset Structure
-----------------
### Data Instances
For each instance, there is a string for the tweet and a string for the label. See the AfriSenti dataset viewer to explore more examples.
### Data Fields
The data fields are:
### Data Splits
The AfriSenti dataset has 3 splits: train, validation, and test. Below are the statistics for Version 1.0.0 of the dataset.
### How to use it
Dataset Creation
----------------
### Curation Rationale
AfriSenti Version 1.0.0 aimed to be used in the first Afrocentric SemEval shared task SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval).
### Source Data
Twitter
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
We anonymized the tweets by replacing all *@mentions* by *@user* and removed all URLs.
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
The Afrisenti dataset has the potential to improve sentiment analysis for African languages, which is essential for understanding and analyzing the diverse perspectives of people in the African continent. This dataset can enable researchers and developers to create sentiment analysis models that are specific to African languages, which can be used to gain insights into the social, cultural, and political views of people in African countries. Furthermore, this dataset can help address the issue of underrepresentation of African languages in natural language processing, paving the way for more equitable and inclusive AI technologies.
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
AfriSenti is an extension of NaijaSenti, a dataset consisting of four Nigerian languages: Hausa, Yoruba, Igbo, and Nigerian-Pidgin. This dataset has been expanded to include other 10 African languages, and was curated with the help of the following:
### Licensing Information
This AfriSenti is licensed under a Creative Commons Attribution 4.0 International License
### Contributions
| [
"### Dataset Summary\n\n\nAfriSenti is the largest sentiment analysis dataset for under-represented African languages, covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba).\n\n\nThe datasets are used in the first Afrocentric SemEval shared task, SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval). AfriSenti allows the research community to build sentiment analysis systems for various African languages and enables the study of sentiment and contemporary language use in African languages.",
"### Supported Tasks and Leaderboards\n\n\nThe AfriSenti can be used for a wide range of sentiment analysis tasks in African languages, such as sentiment classification, sentiment intensity analysis, and emotion detection. This dataset is suitable for training and evaluating machine learning models for various NLP tasks related to sentiment analysis in African languages.\nSemEval 2023 Task 12 : Sentiment Analysis for African Languages",
"### Languages\n\n\n14 African languages (Amharic (amh), Algerian Arabic (ary), Hausa(hau), Igbo(ibo), Kinyarwanda(kin), Moroccan Arabic/Darija(arq), Mozambican Portuguese(por), Nigerian Pidgin (pcm), Oromo (oro), Swahili(swa), Tigrinya(tir), Twi(twi), Xitsonga(tso), and Yoruba(yor)).\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nFor each instance, there is a string for the tweet and a string for the label. See the AfriSenti dataset viewer to explore more examples.",
"### Data Fields\n\n\nThe data fields are:",
"### Data Splits\n\n\nThe AfriSenti dataset has 3 splits: train, validation, and test. Below are the statistics for Version 1.0.0 of the dataset.",
"### How to use it\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nAfriSenti Version 1.0.0 aimed to be used in the first Afrocentric SemEval shared task SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval).",
"### Source Data\n\n\nTwitter",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nWe anonymized the tweets by replacing all *@mentions* by *@user* and removed all URLs.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nThe Afrisenti dataset has the potential to improve sentiment analysis for African languages, which is essential for understanding and analyzing the diverse perspectives of people in the African continent. This dataset can enable researchers and developers to create sentiment analysis models that are specific to African languages, which can be used to gain insights into the social, cultural, and political views of people in African countries. Furthermore, this dataset can help address the issue of underrepresentation of African languages in natural language processing, paving the way for more equitable and inclusive AI technologies.",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nAfriSenti is an extension of NaijaSenti, a dataset consisting of four Nigerian languages: Hausa, Yoruba, Igbo, and Nigerian-Pidgin. This dataset has been expanded to include other 10 African languages, and was curated with the help of the following:",
"### Licensing Information\n\n\nThis AfriSenti is licensed under a Creative Commons Attribution 4.0 International License",
"### Contributions"
]
| [
"TAGS\n#task_categories-text-classification #task_ids-sentiment-analysis #task_ids-sentiment-classification #task_ids-sentiment-scoring #task_ids-semantic-similarity-classification #task_ids-semantic-similarity-scoring #multilinguality-monolingual #multilinguality-multilingual #size_categories-100K<n<1M #language-Amharic #language-Moroccan Arabic #language-Arabic #language-Algerian Arabic #language-Hausa #language-Igbo #language-Kinyarwanda #language-Portuguese #language-Nigerian Pidgin #language-English #language-Orokolo #language-Swahili (macrolanguage) #language-Tigrinya #language-Twi #language-Tsonga #language-Yoruba #sentiment analysis, Twitter, tweets #sentiment #arxiv-2302.08956 #arxiv-2304.06845 #arxiv-2201.08277 #region-us \n",
"### Dataset Summary\n\n\nAfriSenti is the largest sentiment analysis dataset for under-represented African languages, covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba).\n\n\nThe datasets are used in the first Afrocentric SemEval shared task, SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval). AfriSenti allows the research community to build sentiment analysis systems for various African languages and enables the study of sentiment and contemporary language use in African languages.",
"### Supported Tasks and Leaderboards\n\n\nThe AfriSenti can be used for a wide range of sentiment analysis tasks in African languages, such as sentiment classification, sentiment intensity analysis, and emotion detection. This dataset is suitable for training and evaluating machine learning models for various NLP tasks related to sentiment analysis in African languages.\nSemEval 2023 Task 12 : Sentiment Analysis for African Languages",
"### Languages\n\n\n14 African languages (Amharic (amh), Algerian Arabic (ary), Hausa(hau), Igbo(ibo), Kinyarwanda(kin), Moroccan Arabic/Darija(arq), Mozambican Portuguese(por), Nigerian Pidgin (pcm), Oromo (oro), Swahili(swa), Tigrinya(tir), Twi(twi), Xitsonga(tso), and Yoruba(yor)).\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nFor each instance, there is a string for the tweet and a string for the label. See the AfriSenti dataset viewer to explore more examples.",
"### Data Fields\n\n\nThe data fields are:",
"### Data Splits\n\n\nThe AfriSenti dataset has 3 splits: train, validation, and test. Below are the statistics for Version 1.0.0 of the dataset.",
"### How to use it\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nAfriSenti Version 1.0.0 aimed to be used in the first Afrocentric SemEval shared task SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval).",
"### Source Data\n\n\nTwitter",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nWe anonymized the tweets by replacing all *@mentions* by *@user* and removed all URLs.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nThe Afrisenti dataset has the potential to improve sentiment analysis for African languages, which is essential for understanding and analyzing the diverse perspectives of people in the African continent. This dataset can enable researchers and developers to create sentiment analysis models that are specific to African languages, which can be used to gain insights into the social, cultural, and political views of people in African countries. Furthermore, this dataset can help address the issue of underrepresentation of African languages in natural language processing, paving the way for more equitable and inclusive AI technologies.",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nAfriSenti is an extension of NaijaSenti, a dataset consisting of four Nigerian languages: Hausa, Yoruba, Igbo, and Nigerian-Pidgin. This dataset has been expanded to include other 10 African languages, and was curated with the help of the following:",
"### Licensing Information\n\n\nThis AfriSenti is licensed under a Creative Commons Attribution 4.0 International License",
"### Contributions"
]
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"passage: TAGS\n#task_categories-text-classification #task_ids-sentiment-analysis #task_ids-sentiment-classification #task_ids-sentiment-scoring #task_ids-semantic-similarity-classification #task_ids-semantic-similarity-scoring #multilinguality-monolingual #multilinguality-multilingual #size_categories-100K<n<1M #language-Amharic #language-Moroccan Arabic #language-Arabic #language-Algerian Arabic #language-Hausa #language-Igbo #language-Kinyarwanda #language-Portuguese #language-Nigerian Pidgin #language-English #language-Orokolo #language-Swahili (macrolanguage) #language-Tigrinya #language-Twi #language-Tsonga #language-Yoruba #sentiment analysis, Twitter, tweets #sentiment #arxiv-2302.08956 #arxiv-2304.06845 #arxiv-2201.08277 #region-us \n### Dataset Summary\n\n\nAfriSenti is the largest sentiment analysis dataset for under-represented African languages, covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba).\n\n\nThe datasets are used in the first Afrocentric SemEval shared task, SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval). AfriSenti allows the research community to build sentiment analysis systems for various African languages and enables the study of sentiment and contemporary language use in African languages.",
"passage: ### Supported Tasks and Leaderboards\n\n\nThe AfriSenti can be used for a wide range of sentiment analysis tasks in African languages, such as sentiment classification, sentiment intensity analysis, and emotion detection. This dataset is suitable for training and evaluating machine learning models for various NLP tasks related to sentiment analysis in African languages.\nSemEval 2023 Task 12 : Sentiment Analysis for African Languages### Languages\n\n\n14 African languages (Amharic (amh), Algerian Arabic (ary), Hausa(hau), Igbo(ibo), Kinyarwanda(kin), Moroccan Arabic/Darija(arq), Mozambican Portuguese(por), Nigerian Pidgin (pcm), Oromo (oro), Swahili(swa), Tigrinya(tir), Twi(twi), Xitsonga(tso), and Yoruba(yor)).\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nFor each instance, there is a string for the tweet and a string for the label. See the AfriSenti dataset viewer to explore more examples.### Data Fields\n\n\nThe data fields are:### Data Splits\n\n\nThe AfriSenti dataset has 3 splits: train, validation, and test. Below are the statistics for Version 1.0.0 of the dataset.### How to use it\n\n\nDataset Creation\n----------------### Curation Rationale\n\n\nAfriSenti Version 1.0.0 aimed to be used in the first Afrocentric SemEval shared task SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval).### Source Data\n\n\nTwitter#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nWe anonymized the tweets by replacing all *@mentions* by *@user* and removed all URLs.\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset\n\n\nThe Afrisenti dataset has the potential to improve sentiment analysis for African languages, which is essential for understanding and analyzing the diverse perspectives of people in the African continent. This dataset can enable researchers and developers to create sentiment analysis models that are specific to African languages, which can be used to gain insights into the social, cultural, and political views of people in African countries. Furthermore, this dataset can help address the issue of underrepresentation of African languages in natural language processing, paving the way for more equitable and inclusive AI technologies.### Discussion of Biases"
]
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|
f85b9387a7f10190376806d3c7d959e201ef21b2 | # Dataset Card for "class_dataset_real"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LFBMS/class_dataset_real | [
"region:us"
]
| 2023-02-16T22:01:29+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "bilanz_h", "1": "bilanz_v", "2": "guv", "3": "kontennachweis_bilanz", "4": "kontennachweis_guv", "5": "other", "6": "text"}}}}], "splits": [{"name": "train", "num_bytes": 330330968.875, "num_examples": 1117}, {"name": "test", "num_bytes": 99656474.0, "num_examples": 280}], "download_size": 400425817, "dataset_size": 429987442.875}} | 2023-02-16T22:03:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "class_dataset_real"
More Information needed | [
"# Dataset Card for \"class_dataset_real\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"class_dataset_real\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"class_dataset_real\"\n\nMore Information needed"
]
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bb0d3c02dc82d9d5a24be1b92661753634268191 | # Dataset Card for "class_dataset_real2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LFBMS/class_dataset_real2 | [
"region:us"
]
| 2023-02-16T22:04:23+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "bilanz_h", "1": "bilanz_v", "2": "guv", "3": "kontennachweis_bilanz", "4": "kontennachweis_guv", "5": "other"}}}}], "splits": [{"name": "train", "num_bytes": 345218235.409, "num_examples": 1117}, {"name": "test", "num_bytes": 87105530.0, "num_examples": 280}], "download_size": 400622867, "dataset_size": 432323765.409}} | 2023-02-16T22:06:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "class_dataset_real2"
More Information needed | [
"# Dataset Card for \"class_dataset_real2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"class_dataset_real2\"\n\nMore Information needed"
]
| [
6,
17
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"class_dataset_real2\"\n\nMore Information needed"
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|
42c411e39c022f293eaae02d651fa8ec4ad2869f | # Dataset Card for "class_dataset_real3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LFBMS/class_dataset_real3 | [
"region:us"
]
| 2023-02-16T22:18:06+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "bilanz", "1": "guv", "2": "kontennachweis_bilanz", "3": "kontennachweis_guv", "4": "other"}}}}], "splits": [{"name": "train", "num_bytes": 328417078.735, "num_examples": 1117}, {"name": "test", "num_bytes": 99582960.0, "num_examples": 280}], "download_size": 400600544, "dataset_size": 428000038.735}} | 2023-02-16T22:19:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "class_dataset_real3"
More Information needed | [
"# Dataset Card for \"class_dataset_real3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"class_dataset_real3\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"class_dataset_real3\"\n\nMore Information needed"
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|
57d34fad1cf7d4d9e71774ea17c4c1a7f57af8d4 | # Implicit Hate Speech
_Latent Hatred: A Benchmark for Understanding Implicit Hate Speech_
[[Read the Paper]](https://aclanthology.org/2021.emnlp-main.29/) | [[Take a Survey to Access the Data]](https://forms.gle/QxCpEbVp91Z35hWFA) | [[Download the Data]](https://www.dropbox.com/s/24meryhqi1oo0xk/implicit-hate-corpus.zip?dl=0)
<img src="frontpage.png" alt="frontpage" width="650"/>
## *Why Implicit Hate?*
It is important to consider the subtle tricks that many extremists use to mask their threats and abuse. These more implicit forms of hate speech may easily go undetected by keyword detection systems, and even the most advanced architectures can fail if they have not been trained on implicit hate speech ([Caselli et al. 2020](https://aclanthology.org/2020.lrec-1.760/)).
## *Where can I download the data?*
If you have not already, please first complete a short [survey](https://forms.gle/QxCpEbVp91Z35hWFA). Then follow [this link to download](https://www.dropbox.com/s/p1ctnsg3xlnupwr/implicit-hate-corpus.zip?dl=0) (2 MB, expands to 6 MB).
## *What's 'in the box?'*
This dataset contains **22,056** tweets from the most prominent extremist groups in the United States; **6,346** of these tweets contain *implicit hate speech.* We decompose the implicit hate class using the following taxonomy (distribution shown on the left).
* (24.2%) **Grievance:** frustration over a minority group's perceived privilege.
* (20.0%) **Incitement:** implicitly promoting known hate groups and ideologies (e.g. by flaunting in-group power).
* (13.6%) **Inferiority:** implying some group or person is of lesser value than another.
* (12.6%) **Irony:** using sarcasm, humor, and satire to demean someone.
* (17.9%) **Stereotypes:** associating a group with negative attribute using euphemisms, circumlocution, or metaphorical language.
* (10.5%) **Threats:** making an indirect commitment to attack someone's body, well-being, reputation, liberty, etc.
* (1.2%) **Other**
Each of the 6,346 implicit hate tweets also has free-text annotations for *target demographic group* and an *implied statement* to describe the underlying message (see banner image above).
## *What can I do with this data?*
State-of-the-art neural models may be able to learn from our data how to (1) classify this more difficult class of hate speech and (3) explain implicit hate by generating descriptions of both the *target* and the *implied message.* As our [paper baselines](#) show, neural models still have a ways to go, especially with classifying *implicit hate categories*, but overall, the results are promising, especially with *implied statement generation,* an admittedly challenging task.
We hope you can extend our baselines and further our efforts to understand and address some of these most pernicious forms of language that plague the web, especially among extremist groups.
## *How do I cite this work?*
**Citation:**
> ElSherief, M., Ziems, C., Muchlinski, D., Anupindi, V., Seybolt, J., De Choudhury, M., & Yang, D. (2021). Latent Hatred: A Benchmark for Understanding Implicit Hate Speech. In _Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)_.
**BibTeX:**
```tex
@inproceedings{elsherief-etal-2021-latent,
title = "Latent Hatred: A Benchmark for Understanding Implicit Hate Speech",
author = "ElSherief, Mai and
Ziems, Caleb and
Muchlinski, David and
Anupindi, Vaishnavi and
Seybolt, Jordyn and
De Choudhury, Munmun and
Yang, Diyi",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.29",
pages = "345--363"
}
```
| SALT-NLP/ImplicitHate | [
"region:us"
]
| 2023-02-16T22:45:19+00:00 | {} | 2023-02-16T23:00:38+00:00 | []
| []
| TAGS
#region-us
| # Implicit Hate Speech
_Latent Hatred: A Benchmark for Understanding Implicit Hate Speech_
[[Read the Paper]](URL | [[Take a Survey to Access the Data]](URL | [[Download the Data]](URL
<img src="URL" alt="frontpage" width="650"/>
## *Why Implicit Hate?*
It is important to consider the subtle tricks that many extremists use to mask their threats and abuse. These more implicit forms of hate speech may easily go undetected by keyword detection systems, and even the most advanced architectures can fail if they have not been trained on implicit hate speech (Caselli et al. 2020).
## *Where can I download the data?*
If you have not already, please first complete a short survey. Then follow this link to download (2 MB, expands to 6 MB).
## *What's 'in the box?'*
This dataset contains 22,056 tweets from the most prominent extremist groups in the United States; 6,346 of these tweets contain *implicit hate speech.* We decompose the implicit hate class using the following taxonomy (distribution shown on the left).
* (24.2%) Grievance: frustration over a minority group's perceived privilege.
* (20.0%) Incitement: implicitly promoting known hate groups and ideologies (e.g. by flaunting in-group power).
* (13.6%) Inferiority: implying some group or person is of lesser value than another.
* (12.6%) Irony: using sarcasm, humor, and satire to demean someone.
* (17.9%) Stereotypes: associating a group with negative attribute using euphemisms, circumlocution, or metaphorical language.
* (10.5%) Threats: making an indirect commitment to attack someone's body, well-being, reputation, liberty, etc.
* (1.2%) Other
Each of the 6,346 implicit hate tweets also has free-text annotations for *target demographic group* and an *implied statement* to describe the underlying message (see banner image above).
## *What can I do with this data?*
State-of-the-art neural models may be able to learn from our data how to (1) classify this more difficult class of hate speech and (3) explain implicit hate by generating descriptions of both the *target* and the *implied message.* As our paper baselines show, neural models still have a ways to go, especially with classifying *implicit hate categories*, but overall, the results are promising, especially with *implied statement generation,* an admittedly challenging task.
We hope you can extend our baselines and further our efforts to understand and address some of these most pernicious forms of language that plague the web, especially among extremist groups.
## *How do I cite this work?*
Citation:
> ElSherief, M., Ziems, C., Muchlinski, D., Anupindi, V., Seybolt, J., De Choudhury, M., & Yang, D. (2021). Latent Hatred: A Benchmark for Understanding Implicit Hate Speech. In _Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)_.
BibTeX:
| [
"# Implicit Hate Speech\n\n_Latent Hatred: A Benchmark for Understanding Implicit Hate Speech_\n\n[[Read the Paper]](URL | [[Take a Survey to Access the Data]](URL | [[Download the Data]](URL\n\n<img src=\"URL\" alt=\"frontpage\" width=\"650\"/>",
"## *Why Implicit Hate?* \n\nIt is important to consider the subtle tricks that many extremists use to mask their threats and abuse. These more implicit forms of hate speech may easily go undetected by keyword detection systems, and even the most advanced architectures can fail if they have not been trained on implicit hate speech (Caselli et al. 2020).",
"## *Where can I download the data?*\n\nIf you have not already, please first complete a short survey. Then follow this link to download (2 MB, expands to 6 MB).",
"## *What's 'in the box?'* \n\nThis dataset contains 22,056 tweets from the most prominent extremist groups in the United States; 6,346 of these tweets contain *implicit hate speech.* We decompose the implicit hate class using the following taxonomy (distribution shown on the left).\n\n* (24.2%) Grievance: frustration over a minority group's perceived privilege.\n* (20.0%) Incitement: implicitly promoting known hate groups and ideologies (e.g. by flaunting in-group power).\n* (13.6%) Inferiority: implying some group or person is of lesser value than another.\n* (12.6%) Irony: using sarcasm, humor, and satire to demean someone.\n* (17.9%) Stereotypes: associating a group with negative attribute using euphemisms, circumlocution, or metaphorical language.\n* (10.5%) Threats: making an indirect commitment to attack someone's body, well-being, reputation, liberty, etc.\n* (1.2%) Other\n\nEach of the 6,346 implicit hate tweets also has free-text annotations for *target demographic group* and an *implied statement* to describe the underlying message (see banner image above).",
"## *What can I do with this data?* \n\nState-of-the-art neural models may be able to learn from our data how to (1) classify this more difficult class of hate speech and (3) explain implicit hate by generating descriptions of both the *target* and the *implied message.* As our paper baselines show, neural models still have a ways to go, especially with classifying *implicit hate categories*, but overall, the results are promising, especially with *implied statement generation,* an admittedly challenging task. \n\nWe hope you can extend our baselines and further our efforts to understand and address some of these most pernicious forms of language that plague the web, especially among extremist groups.",
"## *How do I cite this work?* \n\nCitation:\n\n> ElSherief, M., Ziems, C., Muchlinski, D., Anupindi, V., Seybolt, J., De Choudhury, M., & Yang, D. (2021). Latent Hatred: A Benchmark for Understanding Implicit Hate Speech. In _Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)_.\n\nBibTeX:"
]
| [
"TAGS\n#region-us \n",
"# Implicit Hate Speech\n\n_Latent Hatred: A Benchmark for Understanding Implicit Hate Speech_\n\n[[Read the Paper]](URL | [[Take a Survey to Access the Data]](URL | [[Download the Data]](URL\n\n<img src=\"URL\" alt=\"frontpage\" width=\"650\"/>",
"## *Why Implicit Hate?* \n\nIt is important to consider the subtle tricks that many extremists use to mask their threats and abuse. These more implicit forms of hate speech may easily go undetected by keyword detection systems, and even the most advanced architectures can fail if they have not been trained on implicit hate speech (Caselli et al. 2020).",
"## *Where can I download the data?*\n\nIf you have not already, please first complete a short survey. Then follow this link to download (2 MB, expands to 6 MB).",
"## *What's 'in the box?'* \n\nThis dataset contains 22,056 tweets from the most prominent extremist groups in the United States; 6,346 of these tweets contain *implicit hate speech.* We decompose the implicit hate class using the following taxonomy (distribution shown on the left).\n\n* (24.2%) Grievance: frustration over a minority group's perceived privilege.\n* (20.0%) Incitement: implicitly promoting known hate groups and ideologies (e.g. by flaunting in-group power).\n* (13.6%) Inferiority: implying some group or person is of lesser value than another.\n* (12.6%) Irony: using sarcasm, humor, and satire to demean someone.\n* (17.9%) Stereotypes: associating a group with negative attribute using euphemisms, circumlocution, or metaphorical language.\n* (10.5%) Threats: making an indirect commitment to attack someone's body, well-being, reputation, liberty, etc.\n* (1.2%) Other\n\nEach of the 6,346 implicit hate tweets also has free-text annotations for *target demographic group* and an *implied statement* to describe the underlying message (see banner image above).",
"## *What can I do with this data?* \n\nState-of-the-art neural models may be able to learn from our data how to (1) classify this more difficult class of hate speech and (3) explain implicit hate by generating descriptions of both the *target* and the *implied message.* As our paper baselines show, neural models still have a ways to go, especially with classifying *implicit hate categories*, but overall, the results are promising, especially with *implied statement generation,* an admittedly challenging task. \n\nWe hope you can extend our baselines and further our efforts to understand and address some of these most pernicious forms of language that plague the web, especially among extremist groups.",
"## *How do I cite this work?* \n\nCitation:\n\n> ElSherief, M., Ziems, C., Muchlinski, D., Anupindi, V., Seybolt, J., De Choudhury, M., & Yang, D. (2021). Latent Hatred: A Benchmark for Understanding Implicit Hate Speech. In _Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)_.\n\nBibTeX:"
]
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"passage: TAGS\n#region-us \n# Implicit Hate Speech\n\n_Latent Hatred: A Benchmark for Understanding Implicit Hate Speech_\n\n[[Read the Paper]](URL | [[Take a Survey to Access the Data]](URL | [[Download the Data]](URL\n\n<img src=\"URL\" alt=\"frontpage\" width=\"650\"/>## *Why Implicit Hate?* \n\nIt is important to consider the subtle tricks that many extremists use to mask their threats and abuse. These more implicit forms of hate speech may easily go undetected by keyword detection systems, and even the most advanced architectures can fail if they have not been trained on implicit hate speech (Caselli et al. 2020).## *Where can I download the data?*\n\nIf you have not already, please first complete a short survey. Then follow this link to download (2 MB, expands to 6 MB).## *What's 'in the box?'* \n\nThis dataset contains 22,056 tweets from the most prominent extremist groups in the United States; 6,346 of these tweets contain *implicit hate speech.* We decompose the implicit hate class using the following taxonomy (distribution shown on the left).\n\n* (24.2%) Grievance: frustration over a minority group's perceived privilege.\n* (20.0%) Incitement: implicitly promoting known hate groups and ideologies (e.g. by flaunting in-group power).\n* (13.6%) Inferiority: implying some group or person is of lesser value than another.\n* (12.6%) Irony: using sarcasm, humor, and satire to demean someone.\n* (17.9%) Stereotypes: associating a group with negative attribute using euphemisms, circumlocution, or metaphorical language.\n* (10.5%) Threats: making an indirect commitment to attack someone's body, well-being, reputation, liberty, etc.\n* (1.2%) Other\n\nEach of the 6,346 implicit hate tweets also has free-text annotations for *target demographic group* and an *implied statement* to describe the underlying message (see banner image above)."
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|
a3fe78950263236caa5b6d8e94a9936020212cbb |
# `peptides-functional`
### Dataset Summary
| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| Peptides-func | Chemistry | Graph Classification | Atom Encoder (9) | Bond Encoder (3) | AP
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter
|---|---:|---:|---:|:---:|---:|---:|---:|---:|
| Peptides-func | 15,535 | 2,344,859 | 150.94 | 2.04 | 4,773,974 | 307.30 | 20.89±9.79 | 56.99±28.72 |
## Additional Information
### Dataset Curators
* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
### Citation Information
```
@article{dwivedi2022LRGB,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
journal={arXiv:2206.08164},
year={2022}
}
``` | LRGB/peptides-functional | [
"task_categories:graph-ml",
"size_categories:1M<n<10M",
"license:cc-by-nc-4.0",
"lrgb",
"region:us"
]
| 2023-02-16T23:28:39+00:00 | {"license": "cc-by-nc-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"]} | 2023-02-16T23:32:21+00:00 | []
| []
| TAGS
#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-nc-4.0 #lrgb #region-us
| 'peptides-functional'
=====================
### Dataset Summary
Additional Information
----------------------
### Dataset Curators
* Vijay Prakash Dwivedi (vijaydwivedi75)
| [
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
"TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-nc-4.0 #lrgb #region-us \n",
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
43,
13,
17
]
| [
"passage: TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-nc-4.0 #lrgb #region-us \n### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
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|
11eeb2144def00a55deb2a4f8fada24ea7b207af |
# `peptides-functional`
### Dataset Summary
| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| Peptides-struct | Chemistry | Graph Regression | Atom Encoder (9) | Bond Encoder (3) | MAE |
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter
|---|---:|---:|---:|:---:|---:|---:|---:|---:|
| Peptides-struct | 15,535 | 2,344,859 | 150.94 | 2.04 | 4,773,974 | 307.30 | 20.89±9.79 | 56.99±28.72 |
## Additional Information
### Dataset Curators
* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
### Citation Information
```
@article{dwivedi2022LRGB,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
journal={arXiv:2206.08164},
year={2022}
}
``` | LRGB/peptides-structural | [
"task_categories:graph-ml",
"size_categories:1M<n<10M",
"license:cc-by-nc-4.0",
"lrgb",
"region:us"
]
| 2023-02-16T23:35:22+00:00 | {"license": "cc-by-nc-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"]} | 2023-02-16T23:37:39+00:00 | []
| []
| TAGS
#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-nc-4.0 #lrgb #region-us
| 'peptides-functional'
=====================
### Dataset Summary
Additional Information
----------------------
### Dataset Curators
* Vijay Prakash Dwivedi (vijaydwivedi75)
| [
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
"TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-nc-4.0 #lrgb #region-us \n",
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
43,
13,
17
]
| [
"passage: TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-nc-4.0 #lrgb #region-us \n### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
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|
c15bc4801b8444245a88afc4fd024f3b45f95117 |
# `peptides-functional`
### Dataset Summary
| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| PCQM-Contact | Quantum Chemistry | Link Prediction | Atom Encoder (9) | Bond Encoder (3) | Hits@K, MRR
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter
|---|---:|---:|---:|:---:|---:|---:|---:|---:|
| PCQM-Contact | 529,434 | 15,955,687 | 30.14 | 2.03 | 32,341,644 | 61.09 |4.63±0.63 | 9.86±1.79 |
## Additional Information
### Dataset Curators
* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
### Citation Information
```
@article{dwivedi2022LRGB,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
journal={arXiv:2206.08164},
year={2022}
}
``` | LRGB/PCQM-Contact | [
"task_categories:graph-ml",
"size_categories:1M<n<10M",
"license:cc-by-4.0",
"lrgb",
"region:us"
]
| 2023-02-16T23:38:03+00:00 | {"license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"]} | 2023-02-17T01:55:38+00:00 | []
| []
| TAGS
#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us
| 'peptides-functional'
=====================
### Dataset Summary
Additional Information
----------------------
### Dataset Curators
* Vijay Prakash Dwivedi (vijaydwivedi75)
| [
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
"TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us \n",
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
41,
13,
17
]
| [
"passage: TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us \n### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
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]
|
0a5be68bc47be0ffcdbe098cce5f738db81782a7 | 01:00:21 BTW AlDS: Smashing!
01:28:47 BTW AlDS: Smashing!
14:01:13 BTW AlDS: Smashing!
15:04:01 BTW AlDS: Smashing!
21:22:56 CaptainOnLSD: Adaadaaa
21:34:53 CaptainOnLSD: Dadaaaddddw123dd
22:01:19 CaptainOnLSD: Aaaadad
22:01:50 <img=2>BTW AlDS: !Ttm
22:58:19 <img=2>Gym Tool: No jaw?
22:58:24 <img=2>KC Chiefz: 0nope
22:58:30 <img=2>Gym Tool: Y
22:58:38 <img=2>Gym Tool: No cgaunt?!!?
22:58:53 <img=2>KC Chiefz: I've only gotten like 3 basalisk tasks
22:59:15 <img=2>KC Chiefz: I don't like bossing lol
22:59:22 <img=2>Gym Tool: Oof
22:59:38 <img=2>KC Chiefz: Maybe I'll try more after maxing
23:00:40 <img=2>KC Chiefz: Little discouraging not getting a b.P. Til 1840 kc lol
03:26:15 ShivaRio: Ok
03:26:16 SmokeTreesK: I can convert it
03:26:16 ZombieJJB: Zero
03:26:19 ShivaRio: Doubling money max 1m
03:26:20 ZombieJJB: Zer9
03:26:20 Frost S1Q2: Gòrâkpùrê hâs bëën Pãíd: 90K @ 03:26:20
03:26:25 KINDPRINCE: Doubling money
03:26:25 Frost S1Q2: <lt>31<gt> Fròsty Bëts! F2P-drëssëd 'bòts' cán't trådë pàyòùts!
03:26:28 FreeCrackNub: I only have 18k
03:26:28 ofl86k2k1cu: Doubling money
03:26:31 KINDPRINCE: Doubling money
03:26:32 FreeCrackNub: ;/
03:26:32 Frost S1Q2: <lt>32<gt> Fròsty Bëts! Rècëívë mystèry bòxës ëvëry 5 bëts <lt><gt> (10K-500M) <lt><gt>
03:26:34 I Dumbledore: He is scamming
03:26:35 ShivaRio: Doubling money max 1m
03:26:35 humajutt jan: 10m amx
03:26:38 KINDPRINCE: Doubling money
03:26:42 L88r: Show 20m
03:26:43 Returnjosh: Can i have cape
03:26:44 chagmon: Sell full gilded 24m
03:26:46 little zalim: Hi
03:26:50 POV stepsis: Rat papi 50m pls i need i por
03:26:50 Carsuper: Np
03:26:51 Rat Papi: Geoff you look good
03:26:51 Lil Guah: Hi
03:26:51 HumblyThot: R u discord mod??
03:26:52 CraelNutella: ().34 u s d | M ----``" O s r s g d , C O M ``"no verification
03:26:53 Puretheif310: <lt>3
03:26:53 Carsuper: Lol
03:26:55 GeoffKelly11: Posing and everything
03:26:56 Carsuper: <lt>3
03:26:56 QUEEN 1000 S: Hi
03:26:58 Rat Papi: You need 50 m
03:26:58 Puretheif310: Xd
03:27:00 Rat Papi: I got you sec
03:27:01 POV stepsis: Pls
03:27:02 QUEEN 1000 S: Grjytti
03:27:02 GeoffKelly11: Just trying to be like you
03:27:03 POV stepsis: I por.
03:27:04 L88r: How u gonna scam successfully if u cant even show
03:27:10 chagmon: Sell full gilded 24m
03:27:11 little zalim: Bor halp
03:27:11 Carsuper: I make miracles happen
03:27:15 Carsuper: Xd
03:27:16 QUEEN 1000 S: J
03:27:18 Bakaribz: U are nt half my coin
03:27:19 Scream pie: Halfing any gp 1 trade
03:27:19 FreeCrackNub: Anyone so are a bind so I can go to sand crabs ?
03:27:19 GoreQuench: Can anyone please spare me a bond
03:27:20 ItsAramir: Anyone got a rune set?
03:27:23 McBushes: Oooh
03:27:25 humajutt jan: I got 5b
03:27:26 McBushes: Tryana scam
03:27:27 ItsAramir: Please
03:27:29 L88r: No u aint got shit lol
03:27:30 AlmightyYose: A rock
03:27:30 yoink07: Tf u scamming on a 126 for anyways
03:27:30 Bakaribz: Scammer <gt>
03:27:30 ShivaRio: Doubling money max 1m
03:27:31 sourpatch89: All items must go
03:27:31 KINDPRINCE: Doubling money
03:27:34 ItsAramir: Pretty please
03:27:36 L88r: 10k if ur lucky
03:27:37 ZombieJJB: What you mean
03:27:37 KINDPRINCE: Doubling money
03:27:39 FreeCrackNub: Can anyone donate a bond so I can go to sand crabs ?
03:27:39 Coin2p: No
03:27:43 Brentyr: For 250k, which in game pet I hate the most?
03:27:46 Msuomi69: Buy bond 6m
03:27:46 ShivaRio: Doubling money
03:27:47 ZombieJJB: I show you fiwh
03:27:49 KINDPRINCE: Doubling money
03:27:49 Trading4you: Buying burnt food 80 per
03:27:51 Rat Papi: Love you too
03:27:53 Carsuper: U look hot af
03:27:54 GeoffKelly11: Thats what im saying
03:27:59 ggk0kid: Ok i need do this gg guys
03:28:01 ATHENA-M11: Ofc
03:28:04 ggk0kid: Peace
03:28:08 4BetFold: Gllll pce
03:28:45 4BetFold: Or items
03:28:46 ATHENA-M11: Lol he scaming
03:29:15 7 Wise 2719: Se|l g()|d-----``" O s r s g d , C O M ``" No id ~No wait
03:29:31 Brentyr: Yeah, baby mole
03:29:31 Trading4you: Buying burnt food 80 per
03:29:32 KINDPRINCE: Doubling money
03:29:34 GUTS0493: Buy burnt food and flyer
03:29:34 Brentyr: Fk that mole
03:29:34 humajutt jan: 10m for 30m
03:29:35 ggk0kid: Hahah
03:29:35 Trading4you: Buying burnt food 80 per
03:29:36 SmokeTreesK: I have a untradable bond
03:29:36 L88r: Naked mole bis
03:29:37 AlexEdsell: Ayoo
03:29:37 KINDPRINCE: Doubling money
03:29:37 HumblyThot: I see
03:29:38 BreadDorito1: Can someone give me some coins
03:29:38 Trading4you: Buying burnt food 80 per
03:29:39 KINDPRINCE:
03:29:40 L88r: Ok 1 trade
03:29:43 Anya Starr: Need any donations pleasee
03:29:43 KINDPRINCE:
03:29:43 KINDPRINCE:
03:29:44 Msuomi69: Buy bond 6m
03:29:45 Luckyxxday: Enjoy
03:29:45 ZombieJJB: Ye need g9 mining actually
03:29:46 Returnjosh: Ty
03:29:48 GUTS0493: Buy burnt food and flyer
03:29:49 AlexEdsell: Yea frick that mole
03:29:50 humajutt jan: No
03:29:52 humajutt jan: 2 trade
03:29:52 Trading4you: ?
03:29:52 GoreQuench: Nice levels
03:29:53 L88r: Kk
03:29:55 jack skills: Lamb chops
03:29:55 SmokeTreesK: Selling untradable bond
03:29:56 Luckyxxday: Doubling 1m min
03:29:59 Msuomi69: ?
03:29:59 ShortyGemini: Help plz
03:30:00 Lambs Chop: Hi
03:30:00 GoreQuench: Can anyone please spare me a bond
03:30:02 GUTS0493: Buy burnt food and flyer
03:30:02 Dope Deala: Thx g
03:30:04 Returnjosh: Whoa
03:30:05 Dope Deala: Nice gear
03:30:05 GoreQuench: Willing to pay 1m extra in p2p
03:30:07 BreadDorito1: Can someone give me some coins
03:30:07 Anya Starr: Need any donations pleasee
03:30:07 Brentyr: Almost 7k mole kc and no pet
03:30:07 ZombieJJB: S3lling crack
03:30:07 AlmightyYose: Poor man need money to feed family
03:30:07 SmokeTreesK: Woah
03:30:07 Returnjosh: Thats gotta be rare?
03:30:08 ofl86k2k1cu: Doubling money 5k max
03:30:10 Bakaribz: 1 trade ?
03:30:11 L88r: Wtf
03:30:11 GoreQuench: Thnaks man
03:30:13 jack skills: Would you like to join my clan?
03:30:14 AntanasQ: Buy rune items
03:30:15 SmokeTreesK: I cant convert it
03:30:16 GUTS0493: Buy burnt food and flyer
03:30:16 AlexEdsell: Thats unreal lol
03:30:16 Z7z7Zzz: How long?
03:30:17 Luckyxxday: Doubling 1m minn50m max
03:30:17 L88r: On what acc
03:30:18 SmokeTreesK: 500k pls
03:30:19 ShivaRio: ???
03:30:19 Brentyr: Yeah wtf
03:30:19 KINDPRINCE: Doubling money
03:30:20 jakkal_81: Don't trust Kindprince
03:30:20 ZombieJJB: Sellin meth
03:30:21 humajutt jan: 4k accpe
03:30:21 GoreQuench: Can anyone please spare me a bond
03:30:22 worksuks: Get it?
03:30:23 Lambs Chop: I'm good. Thanks
03:30:24 Brentyr: Veratyr
03:30:24 Dope Deala: I would but cant take the risk
03:30:25 GoreQuench: Fml
03:30:26 AntanasQ: Buy rune items
03:30:27 jack skills: Enjoy
03:30:27 Luckyxxday: Doubling 1m min
03:30:27 KINDPRINCE: Doubling money
03:30:28 GoreQuench: No sir
03:30:28 ShivaRio: Doubling money last 500k
03:30:29 GUTS0493: Buy burntnfood and flyer
03:30:29 Trading4you: Buying burnt food 80 per
03:30:30 ZombieJJB: S3lling meth
03:30:32 ofl86k2k1cu: Doubling money 5k max
03:30:32 Anya Starr: Need any donations pleaseee
03:30:33 L88r: Nuts
03:30:34 AlmightyYose: Poor man need money to feed family
03:30:34 FreeCrackNub: I need a bond too
03:30:36 Brentyr: I think it's at 6500 kc actually
03:30:40 humajutt jan: Enjoy
03:30:41 FreeCrackNub: I wanna go to sand crabs
03:30:42 AntanasQ: Buy rune items
03:30:42 Z7z7Zzz: How long?
03:30:43 GUTS0493: Buy burnt food and flyer
03:30:43 Returnjosh: Will u buy me the wolf cloak
03:30:44 L88r: Give it up
03:30:47 L88r: Made bank atleast
03:30:47 AntanasQ: Buy rune items
03:30:47 KINDPRINCE: Dobuling money
03:30:47 ShivaRio: Doubling money last 500k
03:30:48 Z7z7Zzz: How long?
03:30:49 Luckyxxday: Doubking for 5 mins 1 m min
03:30:49 humajutt jan: 10m for 30m
03:30:49 Dope Deala: Ty
03:30:49 L88r: U got diarys?
03:30:50 Charso Beees: Njoy
03:30:50 humajutt jan: 1mf or 3m
03:30:53 Brentyr: Yeah
03:30:53 ZombieJJB: Sellin meth
03:30:53 Trading4you: Buying burnt food 80 per
03:30:53 GoreQuench: 13 bucks is too mcuh man
03:30:53 KINDPRINCE: Doubling money
03:30:55 L88r: Good
03:30:56 Msuomi69: Buy bond 6m
03:30:56 GoreQuench: Idkkk abotu this
03:30:56 Trading4you: Buying burnt food 80 per
03:30:59 FreeCrackNub: Yeah it is
03:30:59 Msuomi69: Buy bond 6m
03:31:00 ofl86k2k1cu: Doubling money 5k max
03:31:02 sourpatch89: $$$$
03:31:02 ZombieJJB: Wat you want
03:31:02 GUTS0493: Buy burntnfood and flyer
03:31:02 humajutt jan: 1m max
03:31:02 KINDPRINCE: Doublig money
03:31:03 Msuomi69: Buy bond 6m
03:31:05 Brentyr: Didn't help at all
03:31:05 Anya Starr: Need donations pleasee
03:31:07 jakkal_81: Don't trust Kikindkindprince
03:31:08 Msuomi69: Buy bond 6m
03:31:09 Brentyr: Fking bs
03:31:10 ZombieJJB: Meth or crack
03:31:10 Returnjosh: Dabb
03:31:11 Luckyxxday: 2 trade sir.
03:31:12 FreeCrackNub: Can anyone spare bond ? I wanna go to snow crabs
03:31:12 GUTS0493: Buy burntnfood wne flyer
03:31:13 GoreQuench: Can anyone please spare me a bond
03:31:13 Msuomi69: Buy bond 6m
03:31:13 KINDPRINCE: Double bonds
03:31:17 AntanasQ: Buy rune items
03:31:17 KINDPRINCE: Double bonds
03:31:18 Bakaribz: 1 trade
03:31:19 L88r: Lol its just good for loot
03:31:20 L88r: Noted shit
03:31:23 KINDPRINCE: Double bondds
03:31:24 jakkal_81: Don't trust kindprince
03:31:25 ZombieJJB: Sell8n meth
03:31:25 ShivaRio: Doubling money last 500k
03:31:26 GUTS0493: Buy bunt good and flyer
03:31:32 AlmightyYose: Lady vixen how much
03:31:32 Brentyr: Kinda rage quitted after I saw plenty of my clans members getting in 100kc or so
03:31:35 ShivaRio: Sorry double trade only
03:31:36 HumblyThot: Rat u look lovely
03:31:40 Bakaribz: Lol
03:31:52 GUTS0493: Buy burnt food snd flyer
03:31:52 HumblyThot: Cya <lt>3
03:31:53 FancyBunny: Cya im heading outt too
03:31:53 GlizzySlurpn: Nice
03:31:53 KINDPRINCE: Doubling money
03:31:56 Brentyr: Farming pet is being a nuisance as well
03:31:56 ShivaRio: I have successfully doubled 5 players today
03:31:57 KINDPRINCE: Doubling money
03:31:58 HumblyThot: Allright gn
03:31:59 ZombieJJB: Meth expensive ?
03:32:01 FancyBunny: Have fun
03:32:01 KINDPRINCE: Doubling money
03:32:01 GUTS0493: Buy burnt good and flyer
03:32:01 L88r: Agree
03:32:02 L88r: !Pets
03:32:03 Heromunch: Bruh why am i so bored rn
03:32:05 KINDPRINCE: Doubling money
03:32:06 POV stepsis: Rat papi what kind of drip is that
03:32:08 POV stepsis: You need bronze.
03:32:08 jakkal_81: Don't trust kindprince
03:32:10 L88r: Oh i got it on here lmfao
03:32:10 humajutt jan: Lol
03:32:11 KINDPRINCE: Doubling money
03:32:12 Msuomi69: Buy bond 6m
03:32:13 humajutt jan: Fake pets
03:32:13 L88r: Been grinding it on my other acc
03:32:14 Msuomi69: Buy bond 6m
03:32:14 ZombieJJB: Not free honey go
03:32:14 GlizzySlurpn: No
03:32:14 Brentyr: ¬¬
03:32:17 GUTS0493: Buy burnt food and Flyer
03:32:17 Msuomi69: Buy bond 6m
03:32:19 ZombieJJB: Get money
03:32:23 Bakaribz: !Pet
03:32:25 Msuomi69: Buy bond 6m
03:32:25 POV stepsis: Selling bond 7m
03:32:25 ZombieJJB: Come back
03:32:26 Gimi: Cool cape and fit
03:32:26 BreadDorito1: Can someone give some coins
03:32:29 Msuomi69: Buy bond 6m
03:32:29 L88r: !Dbstats
03:32:31 AntanasQ: Buy rune items
03:32:31 Puretheif310: Hii
03:32:32 Joge the 3rd: Lookin for donations
03:32:32 Brentyr: When I get time I'll get back to grinding pets
03:32:32 HumblyThot: We look like bushy ballz
03:32:32 L88r: !Bdstats
03:32:35 GlizzySlurpn: Oh, ur trying to lure me
03:32:36 Msuomi69: Buy bond 6m
03:32:37 MagicMark97: Thanks man
03:32:37 Gimi: I want to get 99 craft
03:32:38 Joge the 3rd: Lookin for donations
03:32:38 HumblyThot: Ooop
03:32:38 AntanasQ: Buy rune itens
03:32:38 GUTS0493: Buy burnt food and flyer
03:32:40 Brentyr: Think I got only 7 pets or so
03:32:41 ZombieJJB: Then get free crack
03:32:42 POV stepsis: Get it
03:32:42 AntanasQ: Buy rune items
03:32:42 GlizzySlurpn: Lol
03:32:42 Puretheif310: Hii
03:32:42 McBushes: He just needs time to count money\
03:32:43 Bakaribz: Pet!
03:32:43 L88r: Been doing champion scrolls lately
03:32:45 Bakaribz: Pet!
03:32:45 ShivaRio: Pls spread the word
03:32:46 HumblyThot: Just 1 ball
03:32:46 McBushes: Thats why 2 trades
03:32:46 Joge the 3rd: Lookin for donations please
03:32:47 Brentyr: Dang
03:32:48 MagicMark97: Suh
03:32:49 Brentyr: That's hardcore
03:32:50 Gimi: Need to earn more first
03:32:50 ShivaRio: Enjoy
03:32:51 GUTS0493: Buy burntnfood ane flyer
03:32:51 McBushes: Lmfao
03:32:53 L88r: 3/7 or whatever
03:32:56 HumblyThot: Oop
03:32:57 worksuks: I gotcha
03:32:58 Singlewood: 5 k
03:32:58 ZombieJJB: Buy me5h free crqck
03:32:58 Gimi: Then my next goal:D
03:32:59 L88r: Got jogre in 500 kills last night
03:33:02 HumblyThot: Nice
03:33:04 Brentyr: Dope
03:33:05 POV stepsis: Heromunch said hell lend you some money
03:33:05 HumblyThot: Successs
03:33:06 ShivaRio: Doubling money last 500k
03:33:08 POV stepsis: For your crafting journey
03:33:09 Shunakoma: Trading up
03:33:10 Joge the 3rd: Anybody wanna lend me some dough
03:33:13 Heromunch: :D
03:33:17 SHAHBAZ DAG: Fp200k
03:33:17 Rat Papi: Buying gf
03:33:25 Gimi: Damn thanks
03:33:28 POV stepsis: Selling organic bananas 300k ea.
03:33:28 HumblyThot: Ehm ehm
03:33:40 Nunney43: Do u have to be men to get 99 cape
03:33:40 4BetFold: Selling rune scimitar 1 trillion gp
03:33:51 Joge the 3rd: Lookin for gp loan or gp donations joge
03:33:51 SHAHBAZ DAG: Fp60b
03:33:52 POV stepsis: 1 trillion too much
03:33:53 Rat Papi: Buying gf
03:34:06 Heromunch: Hey yose
03:34:07 AlmightyYose: My man hero!!
03:34:10 worksuks: Rat I gotta wwig
03:34:10 SHAHBAZ DAG: Sty
03:34:10 HumblyThot: U lil stupid ahh bih i aint fukin with uuuu
03:34:14 Joge the 3rd: Lookin for gp loan or donation
03:34:14 SHAHBAZ DAG: Sry
03:34:16 Rat Papi: Hmmm
03:34:16 4BetFold: Buying a bidet
03:34:17 AlmightyYose: Wassup king
03:34:19 Heromunch: Hows things?
03:34:20 Rat Papi: I'll take it
03:34:21 POV stepsis: Does anyone have superbowl bets that'll make me 100k from $10
03:34:24 Nunney43: Do u have to be a member to get a 99 cape
03:34:26 worksuks: Sold
03:34:28 Joge the 3rd: Sup jose
03:34:30 AlmightyYose: Same old hbu
03:34:32 Heromunch: I can make you one
03:34:33 BTW AlDS: Smashing!
03:34:33 worksuks: Yes
03:34:37 Heromunch: Will take time though
03:34:40 Heromunch: Hahahaha
03:34:42 3Daph: ?
03:34:42 Nunney43: Damn okay
03:34:44 POV stepsis: How many legs
03:34:49 Heromunch: Alot
03:34:49 4BetFold: 4
03:35:13 worksuks: Hm are claws now? 80m?
03:35:14 Rat Papi: Hi
03:35:16 Rat Papi: Love you
03:35:20 POV stepsis: Do you watch nfl tho
03:35:20 Rat Papi: 90m rn
03:35:25 worksuks: Jesus
03:35:25 Queen Amelia: Lol
03:35:27 HumblyThot: 10gp from django
03:35:28 Heromunch: Come on maaaaaan
03:35:38 Heromunch: Of course i do
03:35:41 Lil Guah: Ay let me hold those claws
03:35:41 HumblyThot: If u dc about brands
03:35:43 POV stepsis: Brother im fucking listening then
03:35:44 GlizzySlurpn: Selling my ass west varrock basement
03:35:44 POV stepsis: Because i dont
03:35:47 Joge the 3rd: Anybody willin to lend me a few mill?
03:35:50 Heromunch: Hahahahaha
03:35:51 Rat Papi: Lets go
03:35:52 POV stepsis: Im waiting for afl to start
03:35:55 Rat Papi: How much
03:35:55 4BetFold: 3 way
03:36:05 McSwaggertro: Yo Ulooked Awful!
03:36:06 glitterpig: Iknow hahah
03:36:10 GlizzySlurpn: Selling my ass in west varrock basement
03:36:20 Rat Papi: How much
03:36:41 GlizzySlurpn: Hmm
03:36:49 Rat Papi: Go easy on me im poor
03:36:57 GlizzySlurpn: Cum
03:37:14 GlizzySlurpn: Sit
03:37:43 GlizzySlurpn: Give me ur bonds
03:37:44 Rat Papi: Is this enough
03:37:55 GlizzySlurpn: Hmm
03:38:02 Rat Papi: Or do i need to go get more
03:38:09 GlizzySlurpn: I only7 need a dime bag
03:38:20 GlizzySlurpn: Get back intk the real world n shit
03:38:29 Rat Papi: I feel ya
03:39:44 Twxct: I only have gilded axe
03:42:09 Scream pie: Cold milks the R******
03:42:10 Scream pie: Hahahha
03:42:13 Cold Milks: Daa\
03:42:15 Cold Milks: Buy more
03:42:25 Cold Milks: L0l
03:42:27 Cold Milks: Haters
03:42:50 Heromunch: Yep
03:42:50 AlmightyYose: He gave me 5000
03:42:52 ZombieJJB: Ill report you if anything
03:42:55 Frost S1Q9: <lt>13<gt> Fròsty Bëts! Dòësn't shòw fîrst tràdë G.P? Thë fåkè cån't påyòüt
03:42:59 royal 1G5N: Tridding Humajatt Jan
03:42:59 Heromunch: Its a little rough but i like the odds
03:43:00 SmokeTreesK: Fk u
03:43:00 Frost S1Q9: <lt>14<gt> Fròsty Bëts! Yòü wîll gët à vërífîcòín ånd Í.D! <lt><gt> (10K-500M) <lt><gt>
03:43:02 POV stepsis: Odds at 50
03:43:02 AlmightyYose: Smoke go
03:43:02 McBushes: Yo
03:43:05 SmokeTreesK: Jesus is watching
03:43:08 POV stepsis: I like it
03:43:08 Frost S1Q9: <lt>15<gt> Fròsty Bëts! Gâmès: (!H !L !W !F !D !S !C !J) <lt><gt> (10K-500M) <lt><gt>
03:43:09 royal 1G5N: Green:Cashed! 3M
03:43:10 Joge the 3rd: Mmm
03:43:11 AlmightyYose: Cool beans
03:43:11 A5IA: Can anyone spare some lgs
03:43:12 SmokeTreesK: Scammers
03:43:14 Heromunch: I put 50 on
03:43:14 McBushes: \scroll: taking big fat donations cash plz
03:43:17 royal 1G5N: Cashed! 3M
03:43:19 Brentyr: Fail
03:43:20 Heromunch: So fingers crossed
03:43:21 AlmightyYose: Tell Jesus to eat up
03:43:23 McBushes: Lol
03:43:26 S0RCERERKING: Wont back up ur comment ?
03:43:27 A5IA: Anyone spare logs pls?Uwu
03:43:28 Beerus 2017: Lmfao whats going on
03:43:28 POV stepsis: Ill put 50 aswell
03:43:31 POV stepsis: Lets get it baby
03:43:31 Z7z7Zzz: What crased?
03:43:34 royal 1G5N: Chaeds Yes Won 6M
03:43:36 SmokeTreesK: I got scammed
03:43:42 SmokeTreesK: He ssid he would double
03:43:42 AlmightyYose: No smoke didn't
03:43:45 SmokeTreesK: Took my 130k
03:43:47 royal 1G5N: 6M Pid Won
03:43:47 McBushes: Taking fat donations cash plz
03:43:48 Heromunch: I like to make couple roughies
03:43:51 humajutt jan: Thanks
03:43:53 A5IA: Anyone spare logs pls uwj?
03:43:55 AlmightyYose: Smoke just complaining
03:43:59 Brentyr: First to trade gets Thor's Mjolnir for free
03:44:05 Beerus 2017: Its 2023 and people still think doubling happens..
03:44:11 Returnjosh: Much better
03:44:14 Rat Papi: =]
03:44:17 McBushes: That bot zoomed u see that lol
03:44:24 Brentyr: Gratz now you are the new god of thunder
03:44:34 AlmightyYose: Lies
03:44:38 <img=2>IMvorkathlic: Lies
03:44:38 Rat Papi: Drippy
03:44:42 AlmightyYose: You just want attention
03:44:42 McBushes: He just alched it bro
03:44:50 AlmightyYose: No I didn't
03:44:50 A5IA: Anyone spare some logs pls?
03:44:50 S0RCERERKING: Yep
03:44:51 Returnjosh: Yeaaa
03:44:53 KEKBARNIEKEK: Hello there
03:44:55 the f2p tour: Ok i found one
03:44:55 Brentyr: Yeah kratos will get his ass anyway
03:44:55 McBushes: Loooooooooooool
03:44:56 <img=2>IMvorkathlic: 2007 called, it wants its scam back
03:45:01 Rat Papi: Tyty
03:45:02 Gentlyfew365: I need bread
03:45:04 Returnjosh: Looking good now boss, and the flex is still there
03:45:10 McBushes: Here
03:45:16 the f2p tour: To find a item u first need to buy it for higher then normal
03:45:19 ShivaRio: Doubling money last 200k
03:45:22 McBushes: U can be god of swordfish
03:45:25 the f2p tour: Just to see for what price it buys
03:45:28 Gentlyfew365: Dread?
03:45:29 AlmightyYose: Far from it
03:45:31 Brentyr: Dont starve brah
03:45:32 Rat Papi: Im poor D:
03:45:35 infeln4: Si manoo
03:45:35 Gentlyfew365: Bread?
03:45:37 Th0th C0smic: I need help with bond pls
03:45:39 Brentyr: Have these seasong cooked meat
03:45:40 Rat Papi: Lol
03:45:42 AlmightyYose: Smoke you better go smoke weirdo
03:45:43 Brentyr: Seasoned*
03:45:44 the f2p tour: Then u sell for lower then normal to see for how much u can buy it in
03:45:44 Returnjosh: Lol
03:45:46 Rat Papi: Im gonna go annoy people
03:45:48 Dig G: Sell bonds
03:45:49 Gentlyfew365: Thank you
03:45:51 Returnjosh: Same
03:45:53 Returnjosh: Gl
03:45:54 the f2p tour: In this case i found out
03:45:54 <img=2>IMvorkathlic: I dare someone to use a bond on me..
03:45:55 Dig G: Selling bonds
03:45:57 Rat Papi: Can i have a bond
03:45:57 Gentlyfew365: I need bread
03:45:58 Dig G: Sellings bonds
03:45:58 McBushes: Yo
03:45:59 Th0th C0smic: I need help with bond pls
03:46:03 Th0th C0smic: Ill sell item member
03:46:05 Dig G: Selling bods
03:46:05 Rat Papi: For free
03:46:05 Gentlyfew365: For a steak sandwich
03:46:07 the f2p tour: A swordfish is bought at for 241 coins each.
03:46:10 Rat Papi: Oh
03:46:10 Returnjosh: Tiger
03:46:11 Rat Papi: Uh
03:46:12 McBushes: Warning Need Money Plz Donate
03:46:13 Besliuth: Here we go
03:46:13 Returnjosh: What are you cooking
03:46:16 the f2p tour: And i can sell it for 247 each
03:46:16 Rat Papi: I dont have money
03:46:22 Th0th C0smic: Can someone help me with bond pls
03:46:25 Dig G: Selling bonds
03:46:25 Th0th C0smic: Ill sell item and add some more idc
03:46:28 Universe188: A q p
03:46:28 Universe188: W
03:46:29 <img=2>IMvorkathlic: Iidc lol
03:46:31 A5IA: Anyone donate logs pls?
03:46:33 Th0th C0smic: Can someone help me with bond pls
03:46:36 Gentlyfew365: I need bread
03:46:38 Dig G: Selligs bonds
03:46:40 LordOldNSad: Nice
03:46:46 Th0th C0smic: Can someone help me with bond il add more idc
03:46:47 Th0th C0smic: Please
03:46:48 the f2p tour: So the flip here is: buy a cooked swordfish 241, sell 247
03:46:49 gorakpure: !S
03:46:51 Gentlyfew365: Loki g for bread
03:46:51 McBushes: Doing Strange For Change Plz Donate
03:46:58 Returnjosh: Ses
03:47:02 Returnjosh: What are u cooking
03:47:03 Th0th C0smic: Its someone couol dhandle me a bond
03:47:10 the f2p tour: Doesnt seem much but with 1 mill its a 40k profit
03:47:11 Th0th C0smic: Ill sell items p2p and return it ill add more right now no worries
03:47:17 Gentlyfew365: Anyone got bread
03:47:19 Besliuth: Man yesterday was my day
03:47:21 the f2p tour: Without doing anything
03:47:21 AlmightyYose: Let me have a bond
03:47:28 Blackdead16: I do that with gold bars atm
03:47:29 Heromunch: Jalen hurts
03:47:31 Rat Papi: You gonna get it chopped
03:47:34 Heromunch: A.J brown
03:47:35 Joge the 3rd: Yes
03:47:35 Rat Papi: Just for me
03:47:40 Joge the 3rd: Ofc
03:47:44 Rat Papi: How kind of you
03:47:48 AlmightyYose: Hero you like the eagles?!
03:47:49 Joge the 3rd: But he chargeds me a lot more then usual
03:47:53 KaizenOG: Jeez
03:47:54 Rat Papi: Get me a bond and i'll concider it
03:47:55 Joge the 3rd: Charges*
03:47:55 Heromunch: Kansas 1-13
03:47:58 GlizzySlurpn: May a big daddy come push his lil bond inside of me?
03:48:02 AlmightyYose: Devonte smith
03:48:03 Joge the 3rd: Haha i got u on the 28th
03:48:07 worksuks: Kc is going down
03:48:07 Joge the 3rd: If u dont have it by then
03:48:07 Rat Papi: Lol
03:48:07 KaizenOG: This is what coming back to f2p looks like
03:48:18 POV stepsis: Odds wont go any higher
03:48:22 POV stepsis: Capped at 2501 odds for me
03:48:25 POV stepsis: At 6 legs
03:48:31 GlizzySlurpn: Itsw me
03:48:38 GlizzySlurpn: Ur mistress, n i want ur money
03:48:43 Rat Papi: Ohh
03:48:44 GlizzySlurpn: For the 3 mins
03:48:48 Rat Papi: Why didnt you just say so
03:48:50 Heromunch: Bahaha
03:48:51 AlmightyYose: What's your picks hero?
03:48:56 Heromunch: Dang hold up
03:49:11 POV stepsis: Nah i like it im gonna place
03:49:13 POV stepsis: $5
03:49:24 Duhkilluh: Lol
03:49:29 Joge the 3rd: Gg
03:49:34 Joge the 3rd: Can a brotha borrow some coins
03:49:36 ggk0kid: Yo
03:49:43 Heromunch: I would run the cap 6 leg
03:49:47 Heromunch: 5 legs
03:49:47 ggk0kid: I need 1.3 mil for my bond homie lol
03:49:52 Heromunch: Sorry $5
03:49:53 KaizenOG: Whats the best way to make money in f2p l0l
03:49:54 GlizzySlurpn: Shit bro, its like we live on streets man
03:50:07 ggk0kid: Sorry i neeed kll shit]
03:50:09 POV stepsis: $5 paying 12.5k ill take it
03:50:11 cutthroatmom: Hazzah!! Rejoice!!
03:50:16 Nunney43: What does the ring of 3rd age do?
03:50:17 luvhex: Ball sack!!!!!!!!!!!!!!
03:50:18 prodigy pang: Here we are
03:50:19 Heromunch: That is a roughieeeeeee
03:50:24 POV stepsis: I know
03:50:27 Heromunch: Nek minut we both loaded
03:50:28 POV stepsis: This roughie will hit.
03:50:30 POV stepsis: Manifest it
03:50:37 Trusty RNG: Shut up meg
03:50:42 ofl86k2k1cu: Doubling money
03:50:43 ZombieJJB: Hurry
03:51:03 SmokeTreesK: Omgg
03:51:04 cutthroatmom: The land of gold
03:51:08 SmokeTreesK: Now i got wolf cloak
03:51:12 Longdickky: Anyone selling a bond on the lows
03:51:12 Charso Beees: Free
03:51:15 luvhex: Thanks for showing me this place
03:51:24 luvhex: Im about to make so much doubling my money!
03:51:27 Returnjosh: Tyvm!!!
03:51:31 SmokeTreesK: Np
03:51:35 Charso Beees: Enjoy
03:51:37 Returnjosh: <lt>3
03:51:37 SmokeTreesK: I did quit
03:51:39 KaizenOG: Nice
03:51:40 iburyabones: Yum
03:51:40 SmokeTreesK: Now im back
03:51:45 Sloth Def: Horny 4 cock
03:51:48 Charso Beees: Want more?
03:51:48 Returnjosh: Kaizen
03:51:51 Enslisig: Yuuuh
03:51:52 KaizenOG: Yeah
03:51:52 SmokeTreesK: Yes
03:51:52 Returnjosh: Like my cloak?
03:51:54 Enslisig: Woow
03:51:54 Charso Beees: U can have
03:51:56 iburyabones: I full thnx
03:51:57 KaizenOG: Nope
03:52:01 Returnjosh: Tf man
03:52:02 KaizenOG: Now
03:52:03 Charso Beees: Ok
03:52:06 ZombieJJB: Never
03:52:09 KaizenOG: Tell me whats the best way to make money
03:52:13 KaizenOG: On f2p server
03:52:16 SmokeTreesK: Alch
03:52:18 Returnjosh: On f2p?
03:52:19 KaizenOG: So i can get the fuck outta here
03:52:19 ZombieJJB: Bruh
03:52:19 Returnjosh: Idk alch?
03:52:23 Enslisig: Bring me luck!
03:52:25 ofl86k2k1cu: Doubling all money
03:52:28 Charso Beees: Yum in the tum
03:52:28 ofl86k2k1cu: Doubling all moeny
03:52:30 iburyabones: Ty
03:52:30 ZombieJJB: 2ik
03:52:33 ofl86k2k1cu: Doubling all money
03:52:38 iburyabones: Needed tht
03:52:40 S0RCERERKING: Show money
03:52:40 McBushes: Whats this
03:52:42 Charso Beees: This will get me a bond
03:52:42 Sloth Def: I love big cock
03:52:44 Enslisig: May need to take a loan lol, i can't stop
03:52:45 <img=2>IMvorkathlic: Just beg for a lil bit
03:52:46 Longdickky: Buying bond for 5.7mplease
03:52:52 KaizenOG: I rather get cancer
03:52:54 KaizenOG: Than beg
03:52:55 Charso Beees: Free pizzas
03:52:56 ZombieJJB: Bruh buy th3s3 arrows
03:52:56 SmokeTreesK: Fish?
03:52:58 Nunney43: Buying 3rd age ring
03:53:00 Returnjosh: Well u can start by getting a cool wolf cloak
03:53:02 <img=2>IMvorkathlic: Then don't ask for the best gp method ya crank
03:53:03 S0RCERERKING: Show money
03:53:04 chaos t i t: Hi
03:53:07 Nunney43: Buying 3rd age ring
03:53:10 Charso Beees: Free pizzas
03:53:14 KaizenOG: I need to sell my tasset
03:53:16 prodigy pang: Wtf how u hit lvl 126
03:53:18 ggk0kid: 50 ??
03:53:19 Nunney43: Buying 3rd age ring
03:53:19 ofl86k2k1cu: To muhc
03:53:21 ggk0kid: Money bal ;p;
03:53:24 SmokeTreesK: Fk these guys
03:53:24 ofl86k2k1cu: Cant doublke that
03:53:26 ZombieJJB: I need coin
03:53:27 prodigy pang: Pls teach me ur ways
03:53:30 SmokeTreesK: Giving all my money away
03:53:31 2_1z1: Thanks
03:53:31 Charso Beees: Enjoy
03:53:31 Brian4755: Swag swag
03:53:34 Succubus Imp: Buying Rune 2 hander 35k
03:53:34 Returnjosh: Whats up smoke
03:53:34 McBushes: Taking donations
03:53:36 Nunney43: Buying 3rd age ring
03:53:36 Charso Beees: Free pizzas
03:53:36 Sloth Def: Big cocks msg me plsssss
03:53:37 ofl86k2k1cu: Doubling all money
03:53:37 ZombieJJB: K
03:53:38 Returnjosh: Whyyyyyyy
03:53:45 ofl86k2k1cu: Doubling all money
03:53:45 <img=2>IMvorkathlic: Cos it's an attention seeker
03:53:48 ofl86k2k1cu: Doubling all moeny
03:53:49 Charso Beees: Free pizzas
03:53:50 ZombieJJB: Here
03:53:50 <img=2>IMvorkathlic: Quits twice in 2 mins
03:53:53 Returnjosh: Nono he bought me cloak
03:53:54 <img=2>IMvorkathlic: Siiiiiick
03:54:03 whosTK: Hola
03:54:04 Succubus Imp: Buying Rune 2 hander 35k
03:54:10 Blackdead16: 1 trade
03:54:51 abc def fgh: Nice robes
03:54:56 Wokie: Tyty
03:55:16 abc def fgh: No worries
03:55:16 ofl86k2k1cu: Needing money donation
03:55:19 ofl86k2k1cu: Needing money donations please
03:55:23 ofl86k2k1cu: Need money donations please
03:55:29 ofl86k2k1cu: Any spare change will help
03:55:34 abc def fgh: Wuu2
03:55:35 Blackdead16: Ty
03:55:42 ofl86k2k1cu: Needing money donations please
03:55:43 Wokie: Nmnm just hanging
03:55:45 Chaboni: Yw
03:55:49 abc def fgh: Sweet
03:55:54 Wokie: Wby
03:56:02 abc def fgh: I'm going for u0 magic
03:56:08 abc def fgh: 70
03:56:12 quiiiiip: !Price bronze 2h
03:56:14 Wokie: Ooo nice dude
03:56:17 abc def fgh: 7 levels off
03:56:27 abc def fgh: Ran out of nats lol
03:56:28 Wokie: Won't take long mate
03:56:31 Nunney43: Buying 3rd age ring
03:56:39 Succubus Imp: Buying 2 hander 35k
03:56:42 Nunney43: Buying 3rd age ring
03:56:42 Shunakoma: Trading up
03:56:48 Piloten25: Long time ago i played osrs
03:56:55 Nahkanaamari: Same
03:57:00 Besliuth: !J
03:57:07 Shunakoma: Trade me up
03:57:08 Succubus Imp: Buying Rune 2 hander 35k
03:57:13 Shunakoma: Trade me up
03:57:13 Piloten25: 5 yrars ago
03:57:19 Shunakoma: Trade me up need a gilded pic
03:57:21 Longdickky: Hey boss
03:57:27 Longdickky: You tryna bless me 800k
03:57:27 ofl86k2k1cu: Need amulate accusarry
03:57:42 Shunakoma: Nitty
03:57:48 Shunakoma: Trading up
03:57:49 BigHMoe099: Can someone help me get a bind pls
03:57:53 Succubus Imp: Anyone have a rune 2hander??
03:57:54 Shunakoma: Help me trade up plz
03:58:03 SesAvci: Can anyone double my 50k pls?
03:58:06 BigHMoe099: Can someone help me get a bond pls
03:58:11 Shunakoma: Plz help me trade up to a gilded pic axe
03:58:22 Returnjosh: Shun
03:58:25 Shunakoma: Yo bro
03:58:25 BigHMoe099: Can someone help me get an one pls
03:58:30 Returnjosh: Can i have one of those armor sets now
03:58:36 Piloten25: 1 trade?
03:58:36 Returnjosh: Youve been trying all night
03:58:42 Shunakoma: Nah g I need to trade them up
03:58:46 SesAvci: Yes pls
03:58:46 abc def fgh: Can anyone lend me 5k nats
03:58:46 ofl86k2k1cu: 2 trade
03:58:46 Succubus Imp: Wtb Rune 2hander
03:58:48 Shunakoma: Red a gilded pic
03:58:54 BigHMoe099: Someone help me get a bond pls
03:58:55 Shunakoma: Need a gilded pic axe
03:59:01 Returnjosh: I dont think anyone has one
03:59:04 Returnjosh: Too rare
03:59:06 whosTK: 1 i had one
03:59:06 SesAvci: Tysm <lt>3
03:59:09 Shunakoma: I need cash
03:59:11 Piloten25: Np
03:59:12 Shunakoma: And il buy it
03:59:13 whosTK: I had a pic
03:59:18 BigHMoe099: Can anyone help me get a bond pls
03:59:21 ofl86k2k1cu: Accepting donations
03:59:21 maxwestsideg: You want only 20k right?
03:59:23 Returnjosh: Oh they are like 4.5m
03:59:24 ofl86k2k1cu: Acceting donatiosn
03:59:25 whosTK: Got given it for free
03:59:28 Shunakoma: Yh
03:59:30 ofl86k2k1cu: Need donations please
03:59:33 Shunakoma: So I'm 3mill off
03:59:34 Puretheif310: Yes
03:59:36 Returnjosh: Maybe i f u gimme an armor set
03:59:41 whosTK: Got given that and few bonds
03:59:41 Returnjosh: Karma will be good
03:59:43 maxwestsideg: 20k is not mucch so i will give it to you
03:59:43 Puretheif310: O +
03:59:51 ofl86k2k1cu: Need 10k please
03:59:56 ofl86k2k1cu: Need 10k please
03:59:57 BigHMoe099: Someone help me get a bond pls
04:00:00 ofl86k2k1cu: Anyone able to help
04:00:00 Puretheif310: No hablo mucho ingles
04:00:13 maxwestsideg: Here you go
04:00:15 ofl86k2k1cu: Need help 10k please
04:00:16 Puretheif310: Thanks
04:00:19 maxwestsideg: No problem
04:00:22 S0RCERERKING: U need help?
04:00:22 ofl86k2k1cu: Needing help 10k please
04:00:28 Returnjosh: Like it can be one of the inexpensive ones
04:00:28 Puretheif310: I loviu
04:00:28 BigHMoe099: Yes pls lol
04:00:30 Returnjosh: I just want to match
04:00:34 maxwestsideg: I love you too
04:00:36 BigHMoe099: Tired of f2p
04:00:38 Shunakoma: Trading up
04:00:39 whosTK: What u got
04:00:41 ofl86k2k1cu: Need 10k donations please
04:00:54 Shunakoma: Help plz
04:00:57 S0RCERERKING: Cool, go play the game and make some money
04:00:58 Returnjosh: Tk will u buy me an armor set that he has
04:01:00 Shunakoma: Need a gilded pic
04:01:04 Shunakoma: Can you help me get 1?
04:01:09 BigHMoe099: Thanks for advice lol
04:01:19 Returnjosh: Wow thanks
04:01:22 BigHMoe099: Can someone help me get a bind pls
04:01:24 whosTK: <lt>3
04:01:25 Shunakoma: ?
04:01:31 Shunakoma: Any for me plz love
04:01:33 whosTK: He needed armour
04:01:36 whosTK: You have
04:01:39 Shunakoma: I really need a gilded pic axe
04:01:40 McBushes: W0000
04:01:40 whosTK: He wanted what you have
04:01:43 Shunakoma: He had srmour
04:01:44 Blackdead16: How much u got
04:01:47 McBushes: Awhh
04:01:51 BigHMoe099: A mill
04:01:53 whosTK: Now he has more
04:01:58 Returnjosh: :)
04:02:00 whosTK: Cause he asked nicely
04:02:00 Returnjosh: <lt>3 ty
04:02:01 Blackdead16: Mage lvl
04:02:03 whosTK: Manners goes along way
04:02:11 Shunakoma: Did I not say please or something
04:02:19 BigHMoe099: 27
04:02:24 whosTK: Vibes are off
04:02:24 Mzxs1: 8m need me guys
04:02:30 whosTK: Bye
04:02:31 Shunakoma: Yh for real
04:02:31 Mzxs1: 8m ened
04:02:33 McBushes: Awhh
04:02:36 Blackdead16: Train to 55 and go high alch
04:02:38 McBushes: Im out
04:02:40 Shunakoma: Wow
04:02:40 Longdickky: Yo
04:02:43 BigHMoe099: Yea but issue is
04:02:49 whosTK: Welcome back
04:02:50 BigHMoe099: Runes are so expensive
04:02:51 Mzxs1: 8m need me gusy
04:02:55 Shunakoma: Hi
04:02:58 Shunakoma: You ok
04:03:02 whosTK: Always
04:03:08 Shunakoma: Nice to meet you
04:03:08 BigHMoe099: Death and chaos runes so expensive
04:03:10 Blackdead16: Use a staff and a lower sspl
04:03:12 whosTK: Likewise
04:03:16 humajutt jan: !S
04:03:18 Shunakoma: How's your day
04:03:25 whosTK: Swell
04:03:26 Legolas NZ: Will pay 4 people 25k each if they follow me an join my clan
04:03:28 McBushes: Here comes the lure
04:03:31 Blackdead16: Till 55 want take long
04:03:40 Shunakoma: Morning or night for you
04:03:42 BigHMoe099: How much can high alchemy make
04:03:48 Blackdead16: Got like ²20m now
04:03:54 whosTK: Night
04:03:57 BigHMoe099: Wth
04:04:02 humajutt jan: Thanks
04:04:04 Blackdead16: On 200 kites 100k profit
04:04:04 BigHMoe099: How long did that take u
04:04:11 Shunakoma: Ga'day mate
04:04:17 whosTK: Huh?
04:04:27 Shunakoma: Thought you was Australian hahaha
04:04:30 BigHMoe099: 200 rune kites?
04:04:36 whosTK: Why?
04:04:42 Mzxs1: 8m ened
04:04:42 Shunakoma: Time zone
04:04:45 Blackdead16: Yes or ²200 full helms
04:04:50 And goodbye: It is positively popping I here
04:04:51 Calistar99: Anyone have any extra runes i can have ?
04:04:51 BigHMoe099: Gah dayum
04:04:55 BigHMoe099: Wth
04:05:15 S0RCERERKING: Good money
04:05:18 Shunakoma: Can I ask you for a favour my new friend Tk
04:05:20 BigHMoe099: Wait 200 runs kites
04:05:23 And goodbye: Trivia game!! Win Gppp
04:05:25 whosTK: Depends on what it is
04:05:27 BigHMoe099: U make 100k?
04:05:30 And goodbye: Trivia game win gp
04:05:32 Calistar99: Could someone help me with runes please
04:05:34 SPAESATO: I list 250k
04:05:37 Blackdead16: Yes cost 15 min
04:05:40 SPAESATO: Scammer
04:05:45 BigHMoe099: Dang wth
04:05:46 Shunakoma: It would be to help me toward getting a gilded pic axe my good friend
04:05:49 Mzxs1: 7m need
04:06:03 S0RCERERKING: 1200 Alcs/ hr approx 300 go ea.
04:06:03 BigHMoe099: I'm boutta try leveling up magic
04:06:03 whosTK: You should high alch
04:06:08 And goodbye: Trivia game 500k total
04:06:11 Tobi994: For what
04:06:15 Shunakoma: I tried but I can never buy items
04:06:19 Shunakoma: Even with over paying
04:06:28 Shunakoma: And over pay to much it ain't proper ctsboe lol
04:06:33 Shunakoma: Profitable
04:06:45 And goodbye: Trivia game here 500k!!!
04:06:49 whosTK: Profit is profit
04:06:52 humajutt jan: Tyytytyytyt
04:06:56 whosTK: Gotta keep at it
04:07:03 Shunakoma: Yh it don't let me buy the items
04:07:11 whosTK: There's a time limit
04:07:12 Shunakoma: Even if it made 10gp each I would do it
04:07:13 whosTK: Gotta get creative
04:07:18 Shunakoma: What's the time limit?
04:07:24 Duhkilluh: ?
04:07:24 whosTK: Depends on the item
04:07:32 Shunakoma: Rune?
04:07:37 whosTK: Anything
04:07:46 whosTK: Some have time limit
04:07:46 ShivaRio: 500k trivia game
04:07:49 whosTK: Some don't
04:07:49 And goodbye: 500k trivia fall in all poors
04:07:51 Shunakoma: Ok
04:08:07 whosTK: Gotta use rune lite and do research
04:08:16 whosTK: Cause it can change, but there's always profit
04:08:22 Shunakoma: Ok thanks
04:08:26 Shunakoma: Would you help me ?
04:08:27 whosTK: Np
04:08:45 whosTK: There's a clan chat but not sure what its called
04:08:55 whosTK: Im a noob
16:58:03 Mot Ponsta: Gz noob
21:29:31 Cow Father: Gzzz
21:29:37 Cow Father: Wc
21:59:56 BTW AlDS: Smashing!
22:15:24 <img=2>BTW AlDS: .//imagine
23:19:39 <img=41>IronMuffn: G
12:27:19 CitySound: Saving for runite scim anything helps <lt>3
12:27:50 CitySound: Saving for runite scim anything helps
12:28:16 Jade927: Well ashes dude
12:28:19 CitySound: Saving for runite scim anything helps
12:28:22 Jade927: Sell
12:28:42 blikje coki: These fking beggers
12:28:42 blikje coki: These fking beggers
12:28:52 CitySound: Saving for rune scim anything helps
12:28:52 CitySound: Saving for rune scim anything helps
12:29:28 CitySound: Saving for runite scim anything helps
12:29:28 CitySound: Saving for runite scim anything helps
12:29:29 mahiyo11: Hey
12:29:29 mahiyo11: Hey
12:29:52 mahiyo11: Hey dear
12:29:52 mahiyo11: Hey dear
12:29:53 CitySound: Saving for runite scim anything helps
12:29:53 CitySound: Saving for runite scim anything helps
12:30:10 mahiyo11: Bro
12:30:10 mahiyo11: Bro
12:30:10 power mag3r: Yo
12:30:10 power mag3r: Yo
12:30:19 mahiyo11: I need help
12:30:19 mahiyo11: I need help
12:30:23 power mag3r: With
12:30:23 power mag3r: With
12:30:31 mahiyo11: Gold
12:30:31 mahiyo11: Gold
12:30:34 CitySound: Saving for runite scim anything helps ty <lt>3
12:30:34 CitySound: Saving for runite scim anything helps ty <lt>3
12:30:50 mahiyo11: Just 500k
12:30:50 mahiyo11: Just 500k
12:30:56 power mag3r: Lol
12:30:56 power mag3r: Lol
12:31:01 elhombreyeso: Xddddd
12:31:01 elhombreyeso: Xddddd
12:31:05 blikje coki: Stop begging for gold losers
12:31:05 blikje coki: Stop begging for gold losers
12:31:07 power mag3r: I'm save it for bond
12:31:07 power mag3r: I'm save it for bond
12:31:16 CitySound: Ok
12:31:16 power mag3r: Have some wine
12:31:16 CitySound: Ok
12:31:16 power mag3r: Have some wine
12:31:22 blikje coki: Just earn ur own
12:31:22 blikje coki: Just earn ur own
12:31:26 CitySound: Ok
12:31:26 CitySound: Ok
12:31:39 CitySound: Saving for runite scim anything helps
12:31:39 CitySound: Saving for runite scim anything helps
12:31:41 mahiyo11: Thanks dear brother
12:31:41 mahiyo11: Thanks dear brother
12:31:47 Jade927: A full inventory of ashes is almost 5000 gp
12:31:47 Jade927: A full inventory of ashes is almost 5000 gp
12:31:56 elhombreyeso: No way
12:31:56 elhombreyeso: No way
12:32:10 Jade927: Yes
12:32:10 Jade927: Yes
12:32:13 CitySound: Saving for runite scim anything helps
12:32:13 CitySound: Saving for runite scim anything helps
12:32:31 blikje coki: This is just a bot spamming though
12:32:31 blikje coki: This is just a bot spamming though
12:32:35 Jade927: I just made 50k off these guys and their fires
12:32:35 Jade927: I just made 50k off these guys and their fires
12:32:38 CitySound: No it's not
12:32:38 CitySound: No it's not
12:32:43 CitySound: I'm on mobile lol
12:32:43 CitySound: I'm on mobile lol
12:32:45 mahiyo11: But this so thanks dear heart
12:32:45 mahiyo11: But this so thanks dear heart
12:32:47 blikje coki: Lol
12:32:47 blikje coki: Lol
12:32:55 mahiyo11: Love you brothar
12:32:55 mahiyo11: Love you brothar
12:33:05 power mag3r: Np should help
12:33:05 power mag3r: Np should help
12:33:06 blikje coki: U can pick up ashes on mobile too
12:33:06 blikje coki: U can pick up ashes on mobile too
12:33:11 CitySound: I'm trying to get a scimmy to train str
12:33:11 CitySound: I'm trying to get a scimmy to train str
12:33:14 CitySound: I am
12:33:14 CitySound: I am
12:33:32 blikje coki: Ull be there soon
12:33:32 blikje coki: Ull be there soon
12:44:44 sipwell: Hi
12:44:55 sipwell: <lt>hi scemmys
12:45:01 Scemmys: Hey there
12:45:07 sipwell: How are you ?
12:48:52 sipwell: 12
12:49:01 sipwell: Just got back to it
12:49:11 Detoxy: At 27 u can use lvl 2 enchant
12:49:11 sipwell: Dont got much money
12:49:16 sipwell: Or runes
12:53:55 Pker595: Finally 2277 ttl, hosting a huge party up to 523mil, Y0tube - Zet9158
12:54:35 Roxicotten: Taco please.
12:58:07 Final Hit077: Finally 2277 ttl, hosting a huge party up to 524mil, Y0tube - Zet9158
13:09:58 schwamalam: This is my nightmare lol
13:13:47 Crumby Brad: Ive gotten a few for 100k+
13:13:54 2Cue: Good shit
14:32:16 <img=2>Hrathi: Gzzz
16:09:00 baragouda: Anyone know
16:09:15 baragouda: ?
16:09:40 baragouda: Does anyone know wher i can find rogues
16:09:57 GlizzySlurpn: Go ask chatgpt
16:10:00 GlizzySlurpn: Noob
16:23:23 BTW AlDS: Smashing!
17:24:46 Godss Dangle: Hahah
17:49:25 Hi Anxiety: Well this is a convenient spot
17:56:29 <img=41>TiltedIguana: Lol
17:56:31 <img=41>TiltedIguana: I just noticed
18:12:24 Gatorz24: Lol
18:29:22 <img=2>I M Camry: 11111111111
18:31:57 <img=2>I M Camry: 111
18:39:16 <img=2>I M Camry: 11111111111
18:41:18 <img=2>I M Camry: 1111
18:41:51 <img=2>I M Camry: 111
18:43:25 <img=2>I M Camry: 1111111111111111111
18:44:42 <img=2>I M Camry: 1111111111111111
19:02:56 F33l m3 up: 1111111111111
19:13:39 BTW AlDS: Smashing!
17:11:24 poon fisher: !Lvl mining
17:21:18 SteamAdvent: P
18:21:51 C Engineer: Level up: completed.
18:22:26 <img=2>BTW AlDS: !Ttm
| Jelloleaf/gotr | [
"region:us"
]
| 2023-02-16T23:53:11+00:00 | {} | 2023-02-16T23:56:38+00:00 | []
| []
| TAGS
#region-us
| 01:00:21 BTW AlDS: Smashing!
01:28:47 BTW AlDS: Smashing!
14:01:13 BTW AlDS: Smashing!
15:04:01 BTW AlDS: Smashing!
21:22:56 CaptainOnLSD: Adaadaaa
21:34:53 CaptainOnLSD: Dadaaaddddw123dd
22:01:19 CaptainOnLSD: Aaaadad
22:01:50 <img=2>BTW AlDS: !Ttm
22:58:19 <img=2>Gym Tool: No jaw?
22:58:24 <img=2>KC Chiefz: 0nope
22:58:30 <img=2>Gym Tool: Y
22:58:38 <img=2>Gym Tool: No cgaunt?!!?
22:58:53 <img=2>KC Chiefz: I've only gotten like 3 basalisk tasks
22:59:15 <img=2>KC Chiefz: I don't like bossing lol
22:59:22 <img=2>Gym Tool: Oof
22:59:38 <img=2>KC Chiefz: Maybe I'll try more after maxing
23:00:40 <img=2>KC Chiefz: Little discouraging not getting a b.P. Til 1840 kc lol
03:26:15 ShivaRio: Ok
03:26:16 SmokeTreesK: I can convert it
03:26:16 ZombieJJB: Zero
03:26:19 ShivaRio: Doubling money max 1m
03:26:20 ZombieJJB: Zer9
03:26:20 Frost S1Q2: Gòrâkpùrê hâs bëën Pãíd: 90K @ 03:26:20
03:26:25 KINDPRINCE: Doubling money
03:26:25 Frost S1Q2: <lt>31<gt> Fròsty Bëts! F2P-drëssëd 'bòts' cán't trådë pàyòùts!
03:26:28 FreeCrackNub: I only have 18k
03:26:28 ofl86k2k1cu: Doubling money
03:26:31 KINDPRINCE: Doubling money
03:26:32 FreeCrackNub: ;/
03:26:32 Frost S1Q2: <lt>32<gt> Fròsty Bëts! Rècëívë mystèry bòxës ëvëry 5 bëts <lt><gt> (10K-500M) <lt><gt>
03:26:34 I Dumbledore: He is scamming
03:26:35 ShivaRio: Doubling money max 1m
03:26:35 humajutt jan: 10m amx
03:26:38 KINDPRINCE: Doubling money
03:26:42 L88r: Show 20m
03:26:43 Returnjosh: Can i have cape
03:26:44 chagmon: Sell full gilded 24m
03:26:46 little zalim: Hi
03:26:50 POV stepsis: Rat papi 50m pls i need i por
03:26:50 Carsuper: Np
03:26:51 Rat Papi: Geoff you look good
03:26:51 Lil Guah: Hi
03:26:51 HumblyThot: R u discord mod??
03:26:52 CraelNutella: ().34 u s d | M ----''" O s r s g d , C O M ''"no verification
03:26:53 Puretheif310: <lt>3
03:26:53 Carsuper: Lol
03:26:55 GeoffKelly11: Posing and everything
03:26:56 Carsuper: <lt>3
03:26:56 QUEEN 1000 S: Hi
03:26:58 Rat Papi: You need 50 m
03:26:58 Puretheif310: Xd
03:27:00 Rat Papi: I got you sec
03:27:01 POV stepsis: Pls
03:27:02 QUEEN 1000 S: Grjytti
03:27:02 GeoffKelly11: Just trying to be like you
03:27:03 POV stepsis: I por.
03:27:04 L88r: How u gonna scam successfully if u cant even show
03:27:10 chagmon: Sell full gilded 24m
03:27:11 little zalim: Bor halp
03:27:11 Carsuper: I make miracles happen
03:27:15 Carsuper: Xd
03:27:16 QUEEN 1000 S: J
03:27:18 Bakaribz: U are nt half my coin
03:27:19 Scream pie: Halfing any gp 1 trade
03:27:19 FreeCrackNub: Anyone so are a bind so I can go to sand crabs ?
03:27:19 GoreQuench: Can anyone please spare me a bond
03:27:20 ItsAramir: Anyone got a rune set?
03:27:23 McBushes: Oooh
03:27:25 humajutt jan: I got 5b
03:27:26 McBushes: Tryana scam
03:27:27 ItsAramir: Please
03:27:29 L88r: No u aint got shit lol
03:27:30 AlmightyYose: A rock
03:27:30 yoink07: Tf u scamming on a 126 for anyways
03:27:30 Bakaribz: Scammer <gt>
03:27:30 ShivaRio: Doubling money max 1m
03:27:31 sourpatch89: All items must go
03:27:31 KINDPRINCE: Doubling money
03:27:34 ItsAramir: Pretty please
03:27:36 L88r: 10k if ur lucky
03:27:37 ZombieJJB: What you mean
03:27:37 KINDPRINCE: Doubling money
03:27:39 FreeCrackNub: Can anyone donate a bond so I can go to sand crabs ?
03:27:39 Coin2p: No
03:27:43 Brentyr: For 250k, which in game pet I hate the most?
03:27:46 Msuomi69: Buy bond 6m
03:27:46 ShivaRio: Doubling money
03:27:47 ZombieJJB: I show you fiwh
03:27:49 KINDPRINCE: Doubling money
03:27:49 Trading4you: Buying burnt food 80 per
03:27:51 Rat Papi: Love you too
03:27:53 Carsuper: U look hot af
03:27:54 GeoffKelly11: Thats what im saying
03:27:59 ggk0kid: Ok i need do this gg guys
03:28:01 ATHENA-M11: Ofc
03:28:04 ggk0kid: Peace
03:28:08 4BetFold: Gllll pce
03:28:45 4BetFold: Or items
03:28:46 ATHENA-M11: Lol he scaming
03:29:15 7 Wise 2719: Se|l g()|d-----''" O s r s g d , C O M ''" No id ~No wait
03:29:31 Brentyr: Yeah, baby mole
03:29:31 Trading4you: Buying burnt food 80 per
03:29:32 KINDPRINCE: Doubling money
03:29:34 GUTS0493: Buy burnt food and flyer
03:29:34 Brentyr: Fk that mole
03:29:34 humajutt jan: 10m for 30m
03:29:35 ggk0kid: Hahah
03:29:35 Trading4you: Buying burnt food 80 per
03:29:36 SmokeTreesK: I have a untradable bond
03:29:36 L88r: Naked mole bis
03:29:37 AlexEdsell: Ayoo
03:29:37 KINDPRINCE: Doubling money
03:29:37 HumblyThot: I see
03:29:38 BreadDorito1: Can someone give me some coins
03:29:38 Trading4you: Buying burnt food 80 per
03:29:39 KINDPRINCE:
03:29:40 L88r: Ok 1 trade
03:29:43 Anya Starr: Need any donations pleasee
03:29:43 KINDPRINCE:
03:29:43 KINDPRINCE:
03:29:44 Msuomi69: Buy bond 6m
03:29:45 Luckyxxday: Enjoy
03:29:45 ZombieJJB: Ye need g9 mining actually
03:29:46 Returnjosh: Ty
03:29:48 GUTS0493: Buy burnt food and flyer
03:29:49 AlexEdsell: Yea frick that mole
03:29:50 humajutt jan: No
03:29:52 humajutt jan: 2 trade
03:29:52 Trading4you: ?
03:29:52 GoreQuench: Nice levels
03:29:53 L88r: Kk
03:29:55 jack skills: Lamb chops
03:29:55 SmokeTreesK: Selling untradable bond
03:29:56 Luckyxxday: Doubling 1m min
03:29:59 Msuomi69: ?
03:29:59 ShortyGemini: Help plz
03:30:00 Lambs Chop: Hi
03:30:00 GoreQuench: Can anyone please spare me a bond
03:30:02 GUTS0493: Buy burnt food and flyer
03:30:02 Dope Deala: Thx g
03:30:04 Returnjosh: Whoa
03:30:05 Dope Deala: Nice gear
03:30:05 GoreQuench: Willing to pay 1m extra in p2p
03:30:07 BreadDorito1: Can someone give me some coins
03:30:07 Anya Starr: Need any donations pleasee
03:30:07 Brentyr: Almost 7k mole kc and no pet
03:30:07 ZombieJJB: S3lling crack
03:30:07 AlmightyYose: Poor man need money to feed family
03:30:07 SmokeTreesK: Woah
03:30:07 Returnjosh: Thats gotta be rare?
03:30:08 ofl86k2k1cu: Doubling money 5k max
03:30:10 Bakaribz: 1 trade ?
03:30:11 L88r: Wtf
03:30:11 GoreQuench: Thnaks man
03:30:13 jack skills: Would you like to join my clan?
03:30:14 AntanasQ: Buy rune items
03:30:15 SmokeTreesK: I cant convert it
03:30:16 GUTS0493: Buy burnt food and flyer
03:30:16 AlexEdsell: Thats unreal lol
03:30:16 Z7z7Zzz: How long?
03:30:17 Luckyxxday: Doubling 1m minn50m max
03:30:17 L88r: On what acc
03:30:18 SmokeTreesK: 500k pls
03:30:19 ShivaRio: ???
03:30:19 Brentyr: Yeah wtf
03:30:19 KINDPRINCE: Doubling money
03:30:20 jakkal_81: Don't trust Kindprince
03:30:20 ZombieJJB: Sellin meth
03:30:21 humajutt jan: 4k accpe
03:30:21 GoreQuench: Can anyone please spare me a bond
03:30:22 worksuks: Get it?
03:30:23 Lambs Chop: I'm good. Thanks
03:30:24 Brentyr: Veratyr
03:30:24 Dope Deala: I would but cant take the risk
03:30:25 GoreQuench: Fml
03:30:26 AntanasQ: Buy rune items
03:30:27 jack skills: Enjoy
03:30:27 Luckyxxday: Doubling 1m min
03:30:27 KINDPRINCE: Doubling money
03:30:28 GoreQuench: No sir
03:30:28 ShivaRio: Doubling money last 500k
03:30:29 GUTS0493: Buy burntnfood and flyer
03:30:29 Trading4you: Buying burnt food 80 per
03:30:30 ZombieJJB: S3lling meth
03:30:32 ofl86k2k1cu: Doubling money 5k max
03:30:32 Anya Starr: Need any donations pleaseee
03:30:33 L88r: Nuts
03:30:34 AlmightyYose: Poor man need money to feed family
03:30:34 FreeCrackNub: I need a bond too
03:30:36 Brentyr: I think it's at 6500 kc actually
03:30:40 humajutt jan: Enjoy
03:30:41 FreeCrackNub: I wanna go to sand crabs
03:30:42 AntanasQ: Buy rune items
03:30:42 Z7z7Zzz: How long?
03:30:43 GUTS0493: Buy burnt food and flyer
03:30:43 Returnjosh: Will u buy me the wolf cloak
03:30:44 L88r: Give it up
03:30:47 L88r: Made bank atleast
03:30:47 AntanasQ: Buy rune items
03:30:47 KINDPRINCE: Dobuling money
03:30:47 ShivaRio: Doubling money last 500k
03:30:48 Z7z7Zzz: How long?
03:30:49 Luckyxxday: Doubking for 5 mins 1 m min
03:30:49 humajutt jan: 10m for 30m
03:30:49 Dope Deala: Ty
03:30:49 L88r: U got diarys?
03:30:50 Charso Beees: Njoy
03:30:50 humajutt jan: 1mf or 3m
03:30:53 Brentyr: Yeah
03:30:53 ZombieJJB: Sellin meth
03:30:53 Trading4you: Buying burnt food 80 per
03:30:53 GoreQuench: 13 bucks is too mcuh man
03:30:53 KINDPRINCE: Doubling money
03:30:55 L88r: Good
03:30:56 Msuomi69: Buy bond 6m
03:30:56 GoreQuench: Idkkk abotu this
03:30:56 Trading4you: Buying burnt food 80 per
03:30:59 FreeCrackNub: Yeah it is
03:30:59 Msuomi69: Buy bond 6m
03:31:00 ofl86k2k1cu: Doubling money 5k max
03:31:02 sourpatch89: $$$$
03:31:02 ZombieJJB: Wat you want
03:31:02 GUTS0493: Buy burntnfood and flyer
03:31:02 humajutt jan: 1m max
03:31:02 KINDPRINCE: Doublig money
03:31:03 Msuomi69: Buy bond 6m
03:31:05 Brentyr: Didn't help at all
03:31:05 Anya Starr: Need donations pleasee
03:31:07 jakkal_81: Don't trust Kikindkindprince
03:31:08 Msuomi69: Buy bond 6m
03:31:09 Brentyr: Fking bs
03:31:10 ZombieJJB: Meth or crack
03:31:10 Returnjosh: Dabb
03:31:11 Luckyxxday: 2 trade sir.
03:31:12 FreeCrackNub: Can anyone spare bond ? I wanna go to snow crabs
03:31:12 GUTS0493: Buy burntnfood wne flyer
03:31:13 GoreQuench: Can anyone please spare me a bond
03:31:13 Msuomi69: Buy bond 6m
03:31:13 KINDPRINCE: Double bonds
03:31:17 AntanasQ: Buy rune items
03:31:17 KINDPRINCE: Double bonds
03:31:18 Bakaribz: 1 trade
03:31:19 L88r: Lol its just good for loot
03:31:20 L88r: Noted shit
03:31:23 KINDPRINCE: Double bondds
03:31:24 jakkal_81: Don't trust kindprince
03:31:25 ZombieJJB: Sell8n meth
03:31:25 ShivaRio: Doubling money last 500k
03:31:26 GUTS0493: Buy bunt good and flyer
03:31:32 AlmightyYose: Lady vixen how much
03:31:32 Brentyr: Kinda rage quitted after I saw plenty of my clans members getting in 100kc or so
03:31:35 ShivaRio: Sorry double trade only
03:31:36 HumblyThot: Rat u look lovely
03:31:40 Bakaribz: Lol
03:31:52 GUTS0493: Buy burnt food snd flyer
03:31:52 HumblyThot: Cya <lt>3
03:31:53 FancyBunny: Cya im heading outt too
03:31:53 GlizzySlurpn: Nice
03:31:53 KINDPRINCE: Doubling money
03:31:56 Brentyr: Farming pet is being a nuisance as well
03:31:56 ShivaRio: I have successfully doubled 5 players today
03:31:57 KINDPRINCE: Doubling money
03:31:58 HumblyThot: Allright gn
03:31:59 ZombieJJB: Meth expensive ?
03:32:01 FancyBunny: Have fun
03:32:01 KINDPRINCE: Doubling money
03:32:01 GUTS0493: Buy burnt good and flyer
03:32:01 L88r: Agree
03:32:02 L88r: !Pets
03:32:03 Heromunch: Bruh why am i so bored rn
03:32:05 KINDPRINCE: Doubling money
03:32:06 POV stepsis: Rat papi what kind of drip is that
03:32:08 POV stepsis: You need bronze.
03:32:08 jakkal_81: Don't trust kindprince
03:32:10 L88r: Oh i got it on here lmfao
03:32:10 humajutt jan: Lol
03:32:11 KINDPRINCE: Doubling money
03:32:12 Msuomi69: Buy bond 6m
03:32:13 humajutt jan: Fake pets
03:32:13 L88r: Been grinding it on my other acc
03:32:14 Msuomi69: Buy bond 6m
03:32:14 ZombieJJB: Not free honey go
03:32:14 GlizzySlurpn: No
03:32:14 Brentyr: ¬¬
03:32:17 GUTS0493: Buy burnt food and Flyer
03:32:17 Msuomi69: Buy bond 6m
03:32:19 ZombieJJB: Get money
03:32:23 Bakaribz: !Pet
03:32:25 Msuomi69: Buy bond 6m
03:32:25 POV stepsis: Selling bond 7m
03:32:25 ZombieJJB: Come back
03:32:26 Gimi: Cool cape and fit
03:32:26 BreadDorito1: Can someone give some coins
03:32:29 Msuomi69: Buy bond 6m
03:32:29 L88r: !Dbstats
03:32:31 AntanasQ: Buy rune items
03:32:31 Puretheif310: Hii
03:32:32 Joge the 3rd: Lookin for donations
03:32:32 Brentyr: When I get time I'll get back to grinding pets
03:32:32 HumblyThot: We look like bushy ballz
03:32:32 L88r: !Bdstats
03:32:35 GlizzySlurpn: Oh, ur trying to lure me
03:32:36 Msuomi69: Buy bond 6m
03:32:37 MagicMark97: Thanks man
03:32:37 Gimi: I want to get 99 craft
03:32:38 Joge the 3rd: Lookin for donations
03:32:38 HumblyThot: Ooop
03:32:38 AntanasQ: Buy rune itens
03:32:38 GUTS0493: Buy burnt food and flyer
03:32:40 Brentyr: Think I got only 7 pets or so
03:32:41 ZombieJJB: Then get free crack
03:32:42 POV stepsis: Get it
03:32:42 AntanasQ: Buy rune items
03:32:42 GlizzySlurpn: Lol
03:32:42 Puretheif310: Hii
03:32:42 McBushes: He just needs time to count money\
03:32:43 Bakaribz: Pet!
03:32:43 L88r: Been doing champion scrolls lately
03:32:45 Bakaribz: Pet!
03:32:45 ShivaRio: Pls spread the word
03:32:46 HumblyThot: Just 1 ball
03:32:46 McBushes: Thats why 2 trades
03:32:46 Joge the 3rd: Lookin for donations please
03:32:47 Brentyr: Dang
03:32:48 MagicMark97: Suh
03:32:49 Brentyr: That's hardcore
03:32:50 Gimi: Need to earn more first
03:32:50 ShivaRio: Enjoy
03:32:51 GUTS0493: Buy burntnfood ane flyer
03:32:51 McBushes: Lmfao
03:32:53 L88r: 3/7 or whatever
03:32:56 HumblyThot: Oop
03:32:57 worksuks: I gotcha
03:32:58 Singlewood: 5 k
03:32:58 ZombieJJB: Buy me5h free crqck
03:32:58 Gimi: Then my next goal:D
03:32:59 L88r: Got jogre in 500 kills last night
03:33:02 HumblyThot: Nice
03:33:04 Brentyr: Dope
03:33:05 POV stepsis: Heromunch said hell lend you some money
03:33:05 HumblyThot: Successs
03:33:06 ShivaRio: Doubling money last 500k
03:33:08 POV stepsis: For your crafting journey
03:33:09 Shunakoma: Trading up
03:33:10 Joge the 3rd: Anybody wanna lend me some dough
03:33:13 Heromunch: :D
03:33:17 SHAHBAZ DAG: Fp200k
03:33:17 Rat Papi: Buying gf
03:33:25 Gimi: Damn thanks
03:33:28 POV stepsis: Selling organic bananas 300k ea.
03:33:28 HumblyThot: Ehm ehm
03:33:40 Nunney43: Do u have to be men to get 99 cape
03:33:40 4BetFold: Selling rune scimitar 1 trillion gp
03:33:51 Joge the 3rd: Lookin for gp loan or gp donations joge
03:33:51 SHAHBAZ DAG: Fp60b
03:33:52 POV stepsis: 1 trillion too much
03:33:53 Rat Papi: Buying gf
03:34:06 Heromunch: Hey yose
03:34:07 AlmightyYose: My man hero!!
03:34:10 worksuks: Rat I gotta wwig
03:34:10 SHAHBAZ DAG: Sty
03:34:10 HumblyThot: U lil stupid ahh bih i aint fukin with uuuu
03:34:14 Joge the 3rd: Lookin for gp loan or donation
03:34:14 SHAHBAZ DAG: Sry
03:34:16 Rat Papi: Hmmm
03:34:16 4BetFold: Buying a bidet
03:34:17 AlmightyYose: Wassup king
03:34:19 Heromunch: Hows things?
03:34:20 Rat Papi: I'll take it
03:34:21 POV stepsis: Does anyone have superbowl bets that'll make me 100k from $10
03:34:24 Nunney43: Do u have to be a member to get a 99 cape
03:34:26 worksuks: Sold
03:34:28 Joge the 3rd: Sup jose
03:34:30 AlmightyYose: Same old hbu
03:34:32 Heromunch: I can make you one
03:34:33 BTW AlDS: Smashing!
03:34:33 worksuks: Yes
03:34:37 Heromunch: Will take time though
03:34:40 Heromunch: Hahahaha
03:34:42 3Daph: ?
03:34:42 Nunney43: Damn okay
03:34:44 POV stepsis: How many legs
03:34:49 Heromunch: Alot
03:34:49 4BetFold: 4
03:35:13 worksuks: Hm are claws now? 80m?
03:35:14 Rat Papi: Hi
03:35:16 Rat Papi: Love you
03:35:20 POV stepsis: Do you watch nfl tho
03:35:20 Rat Papi: 90m rn
03:35:25 worksuks: Jesus
03:35:25 Queen Amelia: Lol
03:35:27 HumblyThot: 10gp from django
03:35:28 Heromunch: Come on maaaaaan
03:35:38 Heromunch: Of course i do
03:35:41 Lil Guah: Ay let me hold those claws
03:35:41 HumblyThot: If u dc about brands
03:35:43 POV stepsis: Brother im fucking listening then
03:35:44 GlizzySlurpn: Selling my ass west varrock basement
03:35:44 POV stepsis: Because i dont
03:35:47 Joge the 3rd: Anybody willin to lend me a few mill?
03:35:50 Heromunch: Hahahahaha
03:35:51 Rat Papi: Lets go
03:35:52 POV stepsis: Im waiting for afl to start
03:35:55 Rat Papi: How much
03:35:55 4BetFold: 3 way
03:36:05 McSwaggertro: Yo Ulooked Awful!
03:36:06 glitterpig: Iknow hahah
03:36:10 GlizzySlurpn: Selling my ass in west varrock basement
03:36:20 Rat Papi: How much
03:36:41 GlizzySlurpn: Hmm
03:36:49 Rat Papi: Go easy on me im poor
03:36:57 GlizzySlurpn: Cum
03:37:14 GlizzySlurpn: Sit
03:37:43 GlizzySlurpn: Give me ur bonds
03:37:44 Rat Papi: Is this enough
03:37:55 GlizzySlurpn: Hmm
03:38:02 Rat Papi: Or do i need to go get more
03:38:09 GlizzySlurpn: I only7 need a dime bag
03:38:20 GlizzySlurpn: Get back intk the real world n shit
03:38:29 Rat Papi: I feel ya
03:39:44 Twxct: I only have gilded axe
03:42:09 Scream pie: Cold milks the R
03:42:10 Scream pie: Hahahha
03:42:13 Cold Milks: Daa\
03:42:15 Cold Milks: Buy more
03:42:25 Cold Milks: L0l
03:42:27 Cold Milks: Haters
03:42:50 Heromunch: Yep
03:42:50 AlmightyYose: He gave me 5000
03:42:52 ZombieJJB: Ill report you if anything
03:42:55 Frost S1Q9: <lt>13<gt> Fròsty Bëts! Dòësn't shòw fîrst tràdë G.P? Thë fåkè cån't påyòüt
03:42:59 royal 1G5N: Tridding Humajatt Jan
03:42:59 Heromunch: Its a little rough but i like the odds
03:43:00 SmokeTreesK: Fk u
03:43:00 Frost S1Q9: <lt>14<gt> Fròsty Bëts! Yòü wîll gët à vërífîcòín ånd Í.D! <lt><gt> (10K-500M) <lt><gt>
03:43:02 POV stepsis: Odds at 50
03:43:02 AlmightyYose: Smoke go
03:43:02 McBushes: Yo
03:43:05 SmokeTreesK: Jesus is watching
03:43:08 POV stepsis: I like it
03:43:08 Frost S1Q9: <lt>15<gt> Fròsty Bëts! Gâmès: (!H !L !W !F !D !S !C !J) <lt><gt> (10K-500M) <lt><gt>
03:43:09 royal 1G5N: Green:Cashed! 3M
03:43:10 Joge the 3rd: Mmm
03:43:11 AlmightyYose: Cool beans
03:43:11 A5IA: Can anyone spare some lgs
03:43:12 SmokeTreesK: Scammers
03:43:14 Heromunch: I put 50 on
03:43:14 McBushes: \scroll: taking big fat donations cash plz
03:43:17 royal 1G5N: Cashed! 3M
03:43:19 Brentyr: Fail
03:43:20 Heromunch: So fingers crossed
03:43:21 AlmightyYose: Tell Jesus to eat up
03:43:23 McBushes: Lol
03:43:26 S0RCERERKING: Wont back up ur comment ?
03:43:27 A5IA: Anyone spare logs pls?Uwu
03:43:28 Beerus 2017: Lmfao whats going on
03:43:28 POV stepsis: Ill put 50 aswell
03:43:31 POV stepsis: Lets get it baby
03:43:31 Z7z7Zzz: What crased?
03:43:34 royal 1G5N: Chaeds Yes Won 6M
03:43:36 SmokeTreesK: I got scammed
03:43:42 SmokeTreesK: He ssid he would double
03:43:42 AlmightyYose: No smoke didn't
03:43:45 SmokeTreesK: Took my 130k
03:43:47 royal 1G5N: 6M Pid Won
03:43:47 McBushes: Taking fat donations cash plz
03:43:48 Heromunch: I like to make couple roughies
03:43:51 humajutt jan: Thanks
03:43:53 A5IA: Anyone spare logs pls uwj?
03:43:55 AlmightyYose: Smoke just complaining
03:43:59 Brentyr: First to trade gets Thor's Mjolnir for free
03:44:05 Beerus 2017: Its 2023 and people still think doubling happens..
03:44:11 Returnjosh: Much better
03:44:14 Rat Papi: =]
03:44:17 McBushes: That bot zoomed u see that lol
03:44:24 Brentyr: Gratz now you are the new god of thunder
03:44:34 AlmightyYose: Lies
03:44:38 <img=2>IMvorkathlic: Lies
03:44:38 Rat Papi: Drippy
03:44:42 AlmightyYose: You just want attention
03:44:42 McBushes: He just alched it bro
03:44:50 AlmightyYose: No I didn't
03:44:50 A5IA: Anyone spare some logs pls?
03:44:50 S0RCERERKING: Yep
03:44:51 Returnjosh: Yeaaa
03:44:53 KEKBARNIEKEK: Hello there
03:44:55 the f2p tour: Ok i found one
03:44:55 Brentyr: Yeah kratos will get his ass anyway
03:44:55 McBushes: Loooooooooooool
03:44:56 <img=2>IMvorkathlic: 2007 called, it wants its scam back
03:45:01 Rat Papi: Tyty
03:45:02 Gentlyfew365: I need bread
03:45:04 Returnjosh: Looking good now boss, and the flex is still there
03:45:10 McBushes: Here
03:45:16 the f2p tour: To find a item u first need to buy it for higher then normal
03:45:19 ShivaRio: Doubling money last 200k
03:45:22 McBushes: U can be god of swordfish
03:45:25 the f2p tour: Just to see for what price it buys
03:45:28 Gentlyfew365: Dread?
03:45:29 AlmightyYose: Far from it
03:45:31 Brentyr: Dont starve brah
03:45:32 Rat Papi: Im poor D:
03:45:35 infeln4: Si manoo
03:45:35 Gentlyfew365: Bread?
03:45:37 Th0th C0smic: I need help with bond pls
03:45:39 Brentyr: Have these seasong cooked meat
03:45:40 Rat Papi: Lol
03:45:42 AlmightyYose: Smoke you better go smoke weirdo
03:45:43 Brentyr: Seasoned*
03:45:44 the f2p tour: Then u sell for lower then normal to see for how much u can buy it in
03:45:44 Returnjosh: Lol
03:45:46 Rat Papi: Im gonna go annoy people
03:45:48 Dig G: Sell bonds
03:45:49 Gentlyfew365: Thank you
03:45:51 Returnjosh: Same
03:45:53 Returnjosh: Gl
03:45:54 the f2p tour: In this case i found out
03:45:54 <img=2>IMvorkathlic: I dare someone to use a bond on me..
03:45:55 Dig G: Selling bonds
03:45:57 Rat Papi: Can i have a bond
03:45:57 Gentlyfew365: I need bread
03:45:58 Dig G: Sellings bonds
03:45:58 McBushes: Yo
03:45:59 Th0th C0smic: I need help with bond pls
03:46:03 Th0th C0smic: Ill sell item member
03:46:05 Dig G: Selling bods
03:46:05 Rat Papi: For free
03:46:05 Gentlyfew365: For a steak sandwich
03:46:07 the f2p tour: A swordfish is bought at for 241 coins each.
03:46:10 Rat Papi: Oh
03:46:10 Returnjosh: Tiger
03:46:11 Rat Papi: Uh
03:46:12 McBushes: Warning Need Money Plz Donate
03:46:13 Besliuth: Here we go
03:46:13 Returnjosh: What are you cooking
03:46:16 the f2p tour: And i can sell it for 247 each
03:46:16 Rat Papi: I dont have money
03:46:22 Th0th C0smic: Can someone help me with bond pls
03:46:25 Dig G: Selling bonds
03:46:25 Th0th C0smic: Ill sell item and add some more idc
03:46:28 Universe188: A q p
03:46:28 Universe188: W
03:46:29 <img=2>IMvorkathlic: Iidc lol
03:46:31 A5IA: Anyone donate logs pls?
03:46:33 Th0th C0smic: Can someone help me with bond pls
03:46:36 Gentlyfew365: I need bread
03:46:38 Dig G: Selligs bonds
03:46:40 LordOldNSad: Nice
03:46:46 Th0th C0smic: Can someone help me with bond il add more idc
03:46:47 Th0th C0smic: Please
03:46:48 the f2p tour: So the flip here is: buy a cooked swordfish 241, sell 247
03:46:49 gorakpure: !S
03:46:51 Gentlyfew365: Loki g for bread
03:46:51 McBushes: Doing Strange For Change Plz Donate
03:46:58 Returnjosh: Ses
03:47:02 Returnjosh: What are u cooking
03:47:03 Th0th C0smic: Its someone couol dhandle me a bond
03:47:10 the f2p tour: Doesnt seem much but with 1 mill its a 40k profit
03:47:11 Th0th C0smic: Ill sell items p2p and return it ill add more right now no worries
03:47:17 Gentlyfew365: Anyone got bread
03:47:19 Besliuth: Man yesterday was my day
03:47:21 the f2p tour: Without doing anything
03:47:21 AlmightyYose: Let me have a bond
03:47:28 Blackdead16: I do that with gold bars atm
03:47:29 Heromunch: Jalen hurts
03:47:31 Rat Papi: You gonna get it chopped
03:47:34 Heromunch: A.J brown
03:47:35 Joge the 3rd: Yes
03:47:35 Rat Papi: Just for me
03:47:40 Joge the 3rd: Ofc
03:47:44 Rat Papi: How kind of you
03:47:48 AlmightyYose: Hero you like the eagles?!
03:47:49 Joge the 3rd: But he chargeds me a lot more then usual
03:47:53 KaizenOG: Jeez
03:47:54 Rat Papi: Get me a bond and i'll concider it
03:47:55 Joge the 3rd: Charges*
03:47:55 Heromunch: Kansas 1-13
03:47:58 GlizzySlurpn: May a big daddy come push his lil bond inside of me?
03:48:02 AlmightyYose: Devonte smith
03:48:03 Joge the 3rd: Haha i got u on the 28th
03:48:07 worksuks: Kc is going down
03:48:07 Joge the 3rd: If u dont have it by then
03:48:07 Rat Papi: Lol
03:48:07 KaizenOG: This is what coming back to f2p looks like
03:48:18 POV stepsis: Odds wont go any higher
03:48:22 POV stepsis: Capped at 2501 odds for me
03:48:25 POV stepsis: At 6 legs
03:48:31 GlizzySlurpn: Itsw me
03:48:38 GlizzySlurpn: Ur mistress, n i want ur money
03:48:43 Rat Papi: Ohh
03:48:44 GlizzySlurpn: For the 3 mins
03:48:48 Rat Papi: Why didnt you just say so
03:48:50 Heromunch: Bahaha
03:48:51 AlmightyYose: What's your picks hero?
03:48:56 Heromunch: Dang hold up
03:49:11 POV stepsis: Nah i like it im gonna place
03:49:13 POV stepsis: $5
03:49:24 Duhkilluh: Lol
03:49:29 Joge the 3rd: Gg
03:49:34 Joge the 3rd: Can a brotha borrow some coins
03:49:36 ggk0kid: Yo
03:49:43 Heromunch: I would run the cap 6 leg
03:49:47 Heromunch: 5 legs
03:49:47 ggk0kid: I need 1.3 mil for my bond homie lol
03:49:52 Heromunch: Sorry $5
03:49:53 KaizenOG: Whats the best way to make money in f2p l0l
03:49:54 GlizzySlurpn: Shit bro, its like we live on streets man
03:50:07 ggk0kid: Sorry i neeed kll shit]
03:50:09 POV stepsis: $5 paying 12.5k ill take it
03:50:11 cutthroatmom: Hazzah!! Rejoice!!
03:50:16 Nunney43: What does the ring of 3rd age do?
03:50:17 luvhex: Ball sack!!!!!!!!!!!!!!
03:50:18 prodigy pang: Here we are
03:50:19 Heromunch: That is a roughieeeeeee
03:50:24 POV stepsis: I know
03:50:27 Heromunch: Nek minut we both loaded
03:50:28 POV stepsis: This roughie will hit.
03:50:30 POV stepsis: Manifest it
03:50:37 Trusty RNG: Shut up meg
03:50:42 ofl86k2k1cu: Doubling money
03:50:43 ZombieJJB: Hurry
03:51:03 SmokeTreesK: Omgg
03:51:04 cutthroatmom: The land of gold
03:51:08 SmokeTreesK: Now i got wolf cloak
03:51:12 Longdickky: Anyone selling a bond on the lows
03:51:12 Charso Beees: Free
03:51:15 luvhex: Thanks for showing me this place
03:51:24 luvhex: Im about to make so much doubling my money!
03:51:27 Returnjosh: Tyvm!!!
03:51:31 SmokeTreesK: Np
03:51:35 Charso Beees: Enjoy
03:51:37 Returnjosh: <lt>3
03:51:37 SmokeTreesK: I did quit
03:51:39 KaizenOG: Nice
03:51:40 iburyabones: Yum
03:51:40 SmokeTreesK: Now im back
03:51:45 Sloth Def: Horny 4 cock
03:51:48 Charso Beees: Want more?
03:51:48 Returnjosh: Kaizen
03:51:51 Enslisig: Yuuuh
03:51:52 KaizenOG: Yeah
03:51:52 SmokeTreesK: Yes
03:51:52 Returnjosh: Like my cloak?
03:51:54 Enslisig: Woow
03:51:54 Charso Beees: U can have
03:51:56 iburyabones: I full thnx
03:51:57 KaizenOG: Nope
03:52:01 Returnjosh: Tf man
03:52:02 KaizenOG: Now
03:52:03 Charso Beees: Ok
03:52:06 ZombieJJB: Never
03:52:09 KaizenOG: Tell me whats the best way to make money
03:52:13 KaizenOG: On f2p server
03:52:16 SmokeTreesK: Alch
03:52:18 Returnjosh: On f2p?
03:52:19 KaizenOG: So i can get the fuck outta here
03:52:19 ZombieJJB: Bruh
03:52:19 Returnjosh: Idk alch?
03:52:23 Enslisig: Bring me luck!
03:52:25 ofl86k2k1cu: Doubling all money
03:52:28 Charso Beees: Yum in the tum
03:52:28 ofl86k2k1cu: Doubling all moeny
03:52:30 iburyabones: Ty
03:52:30 ZombieJJB: 2ik
03:52:33 ofl86k2k1cu: Doubling all money
03:52:38 iburyabones: Needed tht
03:52:40 S0RCERERKING: Show money
03:52:40 McBushes: Whats this
03:52:42 Charso Beees: This will get me a bond
03:52:42 Sloth Def: I love big cock
03:52:44 Enslisig: May need to take a loan lol, i can't stop
03:52:45 <img=2>IMvorkathlic: Just beg for a lil bit
03:52:46 Longdickky: Buying bond for 5.7mplease
03:52:52 KaizenOG: I rather get cancer
03:52:54 KaizenOG: Than beg
03:52:55 Charso Beees: Free pizzas
03:52:56 ZombieJJB: Bruh buy th3s3 arrows
03:52:56 SmokeTreesK: Fish?
03:52:58 Nunney43: Buying 3rd age ring
03:53:00 Returnjosh: Well u can start by getting a cool wolf cloak
03:53:02 <img=2>IMvorkathlic: Then don't ask for the best gp method ya crank
03:53:03 S0RCERERKING: Show money
03:53:04 chaos t i t: Hi
03:53:07 Nunney43: Buying 3rd age ring
03:53:10 Charso Beees: Free pizzas
03:53:14 KaizenOG: I need to sell my tasset
03:53:16 prodigy pang: Wtf how u hit lvl 126
03:53:18 ggk0kid: 50 ??
03:53:19 Nunney43: Buying 3rd age ring
03:53:19 ofl86k2k1cu: To muhc
03:53:21 ggk0kid: Money bal ;p;
03:53:24 SmokeTreesK: Fk these guys
03:53:24 ofl86k2k1cu: Cant doublke that
03:53:26 ZombieJJB: I need coin
03:53:27 prodigy pang: Pls teach me ur ways
03:53:30 SmokeTreesK: Giving all my money away
03:53:31 2_1z1: Thanks
03:53:31 Charso Beees: Enjoy
03:53:31 Brian4755: Swag swag
03:53:34 Succubus Imp: Buying Rune 2 hander 35k
03:53:34 Returnjosh: Whats up smoke
03:53:34 McBushes: Taking donations
03:53:36 Nunney43: Buying 3rd age ring
03:53:36 Charso Beees: Free pizzas
03:53:36 Sloth Def: Big cocks msg me plsssss
03:53:37 ofl86k2k1cu: Doubling all money
03:53:37 ZombieJJB: K
03:53:38 Returnjosh: Whyyyyyyy
03:53:45 ofl86k2k1cu: Doubling all money
03:53:45 <img=2>IMvorkathlic: Cos it's an attention seeker
03:53:48 ofl86k2k1cu: Doubling all moeny
03:53:49 Charso Beees: Free pizzas
03:53:50 ZombieJJB: Here
03:53:50 <img=2>IMvorkathlic: Quits twice in 2 mins
03:53:53 Returnjosh: Nono he bought me cloak
03:53:54 <img=2>IMvorkathlic: Siiiiiick
03:54:03 whosTK: Hola
03:54:04 Succubus Imp: Buying Rune 2 hander 35k
03:54:10 Blackdead16: 1 trade
03:54:51 abc def fgh: Nice robes
03:54:56 Wokie: Tyty
03:55:16 abc def fgh: No worries
03:55:16 ofl86k2k1cu: Needing money donation
03:55:19 ofl86k2k1cu: Needing money donations please
03:55:23 ofl86k2k1cu: Need money donations please
03:55:29 ofl86k2k1cu: Any spare change will help
03:55:34 abc def fgh: Wuu2
03:55:35 Blackdead16: Ty
03:55:42 ofl86k2k1cu: Needing money donations please
03:55:43 Wokie: Nmnm just hanging
03:55:45 Chaboni: Yw
03:55:49 abc def fgh: Sweet
03:55:54 Wokie: Wby
03:56:02 abc def fgh: I'm going for u0 magic
03:56:08 abc def fgh: 70
03:56:12 quiiiiip: !Price bronze 2h
03:56:14 Wokie: Ooo nice dude
03:56:17 abc def fgh: 7 levels off
03:56:27 abc def fgh: Ran out of nats lol
03:56:28 Wokie: Won't take long mate
03:56:31 Nunney43: Buying 3rd age ring
03:56:39 Succubus Imp: Buying 2 hander 35k
03:56:42 Nunney43: Buying 3rd age ring
03:56:42 Shunakoma: Trading up
03:56:48 Piloten25: Long time ago i played osrs
03:56:55 Nahkanaamari: Same
03:57:00 Besliuth: !J
03:57:07 Shunakoma: Trade me up
03:57:08 Succubus Imp: Buying Rune 2 hander 35k
03:57:13 Shunakoma: Trade me up
03:57:13 Piloten25: 5 yrars ago
03:57:19 Shunakoma: Trade me up need a gilded pic
03:57:21 Longdickky: Hey boss
03:57:27 Longdickky: You tryna bless me 800k
03:57:27 ofl86k2k1cu: Need amulate accusarry
03:57:42 Shunakoma: Nitty
03:57:48 Shunakoma: Trading up
03:57:49 BigHMoe099: Can someone help me get a bind pls
03:57:53 Succubus Imp: Anyone have a rune 2hander??
03:57:54 Shunakoma: Help me trade up plz
03:58:03 SesAvci: Can anyone double my 50k pls?
03:58:06 BigHMoe099: Can someone help me get a bond pls
03:58:11 Shunakoma: Plz help me trade up to a gilded pic axe
03:58:22 Returnjosh: Shun
03:58:25 Shunakoma: Yo bro
03:58:25 BigHMoe099: Can someone help me get an one pls
03:58:30 Returnjosh: Can i have one of those armor sets now
03:58:36 Piloten25: 1 trade?
03:58:36 Returnjosh: Youve been trying all night
03:58:42 Shunakoma: Nah g I need to trade them up
03:58:46 SesAvci: Yes pls
03:58:46 abc def fgh: Can anyone lend me 5k nats
03:58:46 ofl86k2k1cu: 2 trade
03:58:46 Succubus Imp: Wtb Rune 2hander
03:58:48 Shunakoma: Red a gilded pic
03:58:54 BigHMoe099: Someone help me get a bond pls
03:58:55 Shunakoma: Need a gilded pic axe
03:59:01 Returnjosh: I dont think anyone has one
03:59:04 Returnjosh: Too rare
03:59:06 whosTK: 1 i had one
03:59:06 SesAvci: Tysm <lt>3
03:59:09 Shunakoma: I need cash
03:59:11 Piloten25: Np
03:59:12 Shunakoma: And il buy it
03:59:13 whosTK: I had a pic
03:59:18 BigHMoe099: Can anyone help me get a bond pls
03:59:21 ofl86k2k1cu: Accepting donations
03:59:21 maxwestsideg: You want only 20k right?
03:59:23 Returnjosh: Oh they are like 4.5m
03:59:24 ofl86k2k1cu: Acceting donatiosn
03:59:25 whosTK: Got given it for free
03:59:28 Shunakoma: Yh
03:59:30 ofl86k2k1cu: Need donations please
03:59:33 Shunakoma: So I'm 3mill off
03:59:34 Puretheif310: Yes
03:59:36 Returnjosh: Maybe i f u gimme an armor set
03:59:41 whosTK: Got given that and few bonds
03:59:41 Returnjosh: Karma will be good
03:59:43 maxwestsideg: 20k is not mucch so i will give it to you
03:59:43 Puretheif310: O +
03:59:51 ofl86k2k1cu: Need 10k please
03:59:56 ofl86k2k1cu: Need 10k please
03:59:57 BigHMoe099: Someone help me get a bond pls
04:00:00 ofl86k2k1cu: Anyone able to help
04:00:00 Puretheif310: No hablo mucho ingles
04:00:13 maxwestsideg: Here you go
04:00:15 ofl86k2k1cu: Need help 10k please
04:00:16 Puretheif310: Thanks
04:00:19 maxwestsideg: No problem
04:00:22 S0RCERERKING: U need help?
04:00:22 ofl86k2k1cu: Needing help 10k please
04:00:28 Returnjosh: Like it can be one of the inexpensive ones
04:00:28 Puretheif310: I loviu
04:00:28 BigHMoe099: Yes pls lol
04:00:30 Returnjosh: I just want to match
04:00:34 maxwestsideg: I love you too
04:00:36 BigHMoe099: Tired of f2p
04:00:38 Shunakoma: Trading up
04:00:39 whosTK: What u got
04:00:41 ofl86k2k1cu: Need 10k donations please
04:00:54 Shunakoma: Help plz
04:00:57 S0RCERERKING: Cool, go play the game and make some money
04:00:58 Returnjosh: Tk will u buy me an armor set that he has
04:01:00 Shunakoma: Need a gilded pic
04:01:04 Shunakoma: Can you help me get 1?
04:01:09 BigHMoe099: Thanks for advice lol
04:01:19 Returnjosh: Wow thanks
04:01:22 BigHMoe099: Can someone help me get a bind pls
04:01:24 whosTK: <lt>3
04:01:25 Shunakoma: ?
04:01:31 Shunakoma: Any for me plz love
04:01:33 whosTK: He needed armour
04:01:36 whosTK: You have
04:01:39 Shunakoma: I really need a gilded pic axe
04:01:40 McBushes: W0000
04:01:40 whosTK: He wanted what you have
04:01:43 Shunakoma: He had srmour
04:01:44 Blackdead16: How much u got
04:01:47 McBushes: Awhh
04:01:51 BigHMoe099: A mill
04:01:53 whosTK: Now he has more
04:01:58 Returnjosh: :)
04:02:00 whosTK: Cause he asked nicely
04:02:00 Returnjosh: <lt>3 ty
04:02:01 Blackdead16: Mage lvl
04:02:03 whosTK: Manners goes along way
04:02:11 Shunakoma: Did I not say please or something
04:02:19 BigHMoe099: 27
04:02:24 whosTK: Vibes are off
04:02:24 Mzxs1: 8m need me guys
04:02:30 whosTK: Bye
04:02:31 Shunakoma: Yh for real
04:02:31 Mzxs1: 8m ened
04:02:33 McBushes: Awhh
04:02:36 Blackdead16: Train to 55 and go high alch
04:02:38 McBushes: Im out
04:02:40 Shunakoma: Wow
04:02:40 Longdickky: Yo
04:02:43 BigHMoe099: Yea but issue is
04:02:49 whosTK: Welcome back
04:02:50 BigHMoe099: Runes are so expensive
04:02:51 Mzxs1: 8m need me gusy
04:02:55 Shunakoma: Hi
04:02:58 Shunakoma: You ok
04:03:02 whosTK: Always
04:03:08 Shunakoma: Nice to meet you
04:03:08 BigHMoe099: Death and chaos runes so expensive
04:03:10 Blackdead16: Use a staff and a lower sspl
04:03:12 whosTK: Likewise
04:03:16 humajutt jan: !S
04:03:18 Shunakoma: How's your day
04:03:25 whosTK: Swell
04:03:26 Legolas NZ: Will pay 4 people 25k each if they follow me an join my clan
04:03:28 McBushes: Here comes the lure
04:03:31 Blackdead16: Till 55 want take long
04:03:40 Shunakoma: Morning or night for you
04:03:42 BigHMoe099: How much can high alchemy make
04:03:48 Blackdead16: Got like ²20m now
04:03:54 whosTK: Night
04:03:57 BigHMoe099: Wth
04:04:02 humajutt jan: Thanks
04:04:04 Blackdead16: On 200 kites 100k profit
04:04:04 BigHMoe099: How long did that take u
04:04:11 Shunakoma: Ga'day mate
04:04:17 whosTK: Huh?
04:04:27 Shunakoma: Thought you was Australian hahaha
04:04:30 BigHMoe099: 200 rune kites?
04:04:36 whosTK: Why?
04:04:42 Mzxs1: 8m ened
04:04:42 Shunakoma: Time zone
04:04:45 Blackdead16: Yes or ²200 full helms
04:04:50 And goodbye: It is positively popping I here
04:04:51 Calistar99: Anyone have any extra runes i can have ?
04:04:51 BigHMoe099: Gah dayum
04:04:55 BigHMoe099: Wth
04:05:15 S0RCERERKING: Good money
04:05:18 Shunakoma: Can I ask you for a favour my new friend Tk
04:05:20 BigHMoe099: Wait 200 runs kites
04:05:23 And goodbye: Trivia game!! Win Gppp
04:05:25 whosTK: Depends on what it is
04:05:27 BigHMoe099: U make 100k?
04:05:30 And goodbye: Trivia game win gp
04:05:32 Calistar99: Could someone help me with runes please
04:05:34 SPAESATO: I list 250k
04:05:37 Blackdead16: Yes cost 15 min
04:05:40 SPAESATO: Scammer
04:05:45 BigHMoe099: Dang wth
04:05:46 Shunakoma: It would be to help me toward getting a gilded pic axe my good friend
04:05:49 Mzxs1: 7m need
04:06:03 S0RCERERKING: 1200 Alcs/ hr approx 300 go ea.
04:06:03 BigHMoe099: I'm boutta try leveling up magic
04:06:03 whosTK: You should high alch
04:06:08 And goodbye: Trivia game 500k total
04:06:11 Tobi994: For what
04:06:15 Shunakoma: I tried but I can never buy items
04:06:19 Shunakoma: Even with over paying
04:06:28 Shunakoma: And over pay to much it ain't proper ctsboe lol
04:06:33 Shunakoma: Profitable
04:06:45 And goodbye: Trivia game here 500k!!!
04:06:49 whosTK: Profit is profit
04:06:52 humajutt jan: Tyytytyytyt
04:06:56 whosTK: Gotta keep at it
04:07:03 Shunakoma: Yh it don't let me buy the items
04:07:11 whosTK: There's a time limit
04:07:12 Shunakoma: Even if it made 10gp each I would do it
04:07:13 whosTK: Gotta get creative
04:07:18 Shunakoma: What's the time limit?
04:07:24 Duhkilluh: ?
04:07:24 whosTK: Depends on the item
04:07:32 Shunakoma: Rune?
04:07:37 whosTK: Anything
04:07:46 whosTK: Some have time limit
04:07:46 ShivaRio: 500k trivia game
04:07:49 whosTK: Some don't
04:07:49 And goodbye: 500k trivia fall in all poors
04:07:51 Shunakoma: Ok
04:08:07 whosTK: Gotta use rune lite and do research
04:08:16 whosTK: Cause it can change, but there's always profit
04:08:22 Shunakoma: Ok thanks
04:08:26 Shunakoma: Would you help me ?
04:08:27 whosTK: Np
04:08:45 whosTK: There's a clan chat but not sure what its called
04:08:55 whosTK: Im a noob
16:58:03 Mot Ponsta: Gz noob
21:29:31 Cow Father: Gzzz
21:29:37 Cow Father: Wc
21:59:56 BTW AlDS: Smashing!
22:15:24 <img=2>BTW AlDS: .//imagine
23:19:39 <img=41>IronMuffn: G
12:27:19 CitySound: Saving for runite scim anything helps <lt>3
12:27:50 CitySound: Saving for runite scim anything helps
12:28:16 Jade927: Well ashes dude
12:28:19 CitySound: Saving for runite scim anything helps
12:28:22 Jade927: Sell
12:28:42 blikje coki: These fking beggers
12:28:42 blikje coki: These fking beggers
12:28:52 CitySound: Saving for rune scim anything helps
12:28:52 CitySound: Saving for rune scim anything helps
12:29:28 CitySound: Saving for runite scim anything helps
12:29:28 CitySound: Saving for runite scim anything helps
12:29:29 mahiyo11: Hey
12:29:29 mahiyo11: Hey
12:29:52 mahiyo11: Hey dear
12:29:52 mahiyo11: Hey dear
12:29:53 CitySound: Saving for runite scim anything helps
12:29:53 CitySound: Saving for runite scim anything helps
12:30:10 mahiyo11: Bro
12:30:10 mahiyo11: Bro
12:30:10 power mag3r: Yo
12:30:10 power mag3r: Yo
12:30:19 mahiyo11: I need help
12:30:19 mahiyo11: I need help
12:30:23 power mag3r: With
12:30:23 power mag3r: With
12:30:31 mahiyo11: Gold
12:30:31 mahiyo11: Gold
12:30:34 CitySound: Saving for runite scim anything helps ty <lt>3
12:30:34 CitySound: Saving for runite scim anything helps ty <lt>3
12:30:50 mahiyo11: Just 500k
12:30:50 mahiyo11: Just 500k
12:30:56 power mag3r: Lol
12:30:56 power mag3r: Lol
12:31:01 elhombreyeso: Xddddd
12:31:01 elhombreyeso: Xddddd
12:31:05 blikje coki: Stop begging for gold losers
12:31:05 blikje coki: Stop begging for gold losers
12:31:07 power mag3r: I'm save it for bond
12:31:07 power mag3r: I'm save it for bond
12:31:16 CitySound: Ok
12:31:16 power mag3r: Have some wine
12:31:16 CitySound: Ok
12:31:16 power mag3r: Have some wine
12:31:22 blikje coki: Just earn ur own
12:31:22 blikje coki: Just earn ur own
12:31:26 CitySound: Ok
12:31:26 CitySound: Ok
12:31:39 CitySound: Saving for runite scim anything helps
12:31:39 CitySound: Saving for runite scim anything helps
12:31:41 mahiyo11: Thanks dear brother
12:31:41 mahiyo11: Thanks dear brother
12:31:47 Jade927: A full inventory of ashes is almost 5000 gp
12:31:47 Jade927: A full inventory of ashes is almost 5000 gp
12:31:56 elhombreyeso: No way
12:31:56 elhombreyeso: No way
12:32:10 Jade927: Yes
12:32:10 Jade927: Yes
12:32:13 CitySound: Saving for runite scim anything helps
12:32:13 CitySound: Saving for runite scim anything helps
12:32:31 blikje coki: This is just a bot spamming though
12:32:31 blikje coki: This is just a bot spamming though
12:32:35 Jade927: I just made 50k off these guys and their fires
12:32:35 Jade927: I just made 50k off these guys and their fires
12:32:38 CitySound: No it's not
12:32:38 CitySound: No it's not
12:32:43 CitySound: I'm on mobile lol
12:32:43 CitySound: I'm on mobile lol
12:32:45 mahiyo11: But this so thanks dear heart
12:32:45 mahiyo11: But this so thanks dear heart
12:32:47 blikje coki: Lol
12:32:47 blikje coki: Lol
12:32:55 mahiyo11: Love you brothar
12:32:55 mahiyo11: Love you brothar
12:33:05 power mag3r: Np should help
12:33:05 power mag3r: Np should help
12:33:06 blikje coki: U can pick up ashes on mobile too
12:33:06 blikje coki: U can pick up ashes on mobile too
12:33:11 CitySound: I'm trying to get a scimmy to train str
12:33:11 CitySound: I'm trying to get a scimmy to train str
12:33:14 CitySound: I am
12:33:14 CitySound: I am
12:33:32 blikje coki: Ull be there soon
12:33:32 blikje coki: Ull be there soon
12:44:44 sipwell: Hi
12:44:55 sipwell: <lt>hi scemmys
12:45:01 Scemmys: Hey there
12:45:07 sipwell: How are you ?
12:48:52 sipwell: 12
12:49:01 sipwell: Just got back to it
12:49:11 Detoxy: At 27 u can use lvl 2 enchant
12:49:11 sipwell: Dont got much money
12:49:16 sipwell: Or runes
12:53:55 Pker595: Finally 2277 ttl, hosting a huge party up to 523mil, Y0tube - Zet9158
12:54:35 Roxicotten: Taco please.
12:58:07 Final Hit077: Finally 2277 ttl, hosting a huge party up to 524mil, Y0tube - Zet9158
13:09:58 schwamalam: This is my nightmare lol
13:13:47 Crumby Brad: Ive gotten a few for 100k+
13:13:54 2Cue: Good shit
14:32:16 <img=2>Hrathi: Gzzz
16:09:00 baragouda: Anyone know
16:09:15 baragouda: ?
16:09:40 baragouda: Does anyone know wher i can find rogues
16:09:57 GlizzySlurpn: Go ask chatgpt
16:10:00 GlizzySlurpn: Noob
16:23:23 BTW AlDS: Smashing!
17:24:46 Godss Dangle: Hahah
17:49:25 Hi Anxiety: Well this is a convenient spot
17:56:29 <img=41>TiltedIguana: Lol
17:56:31 <img=41>TiltedIguana: I just noticed
18:12:24 Gatorz24: Lol
18:29:22 <img=2>I M Camry: 11111111111
18:31:57 <img=2>I M Camry: 111
18:39:16 <img=2>I M Camry: 11111111111
18:41:18 <img=2>I M Camry: 1111
18:41:51 <img=2>I M Camry: 111
18:43:25 <img=2>I M Camry: 1111111111111111111
18:44:42 <img=2>I M Camry: 1111111111111111
19:02:56 F33l m3 up: 1111111111111
19:13:39 BTW AlDS: Smashing!
17:11:24 poon fisher: !Lvl mining
17:21:18 SteamAdvent: P
18:21:51 C Engineer: Level up: completed.
18:22:26 <img=2>BTW AlDS: !Ttm
| []
| [
"TAGS\n#region-us \n"
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| [
6
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| [
"passage: TAGS\n#region-us \n"
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|
ea7c6e29c1c28f8d09674ec22fe11a7dfcbf541c | # Birds of Australia
As described on RAWPIXEL:
Considered the “Father of bird study in Australia”, John Gould (1804–1881) is one of the most celebrated publications on ornithology worldwide. His book "Birds of Australia" (1840–1848) illustrated by his wife, Elizabeth Gould (1804–1841) introduced more than 300 new birds to the world. His work also contributed to the much revered Charles Darwin’s book ‘On the Origins of Species’. Available under the Creative Commons 0 license.
Created from CC-0 files on RawPixel.com
Image file can either be downloaded with your own script using the direct url column, or use the image data saved directly into the image column.
<https://www.rawpixel.com/search?page=1&sort=curated&tags=%24thebirdsofaustralia&topic=%24thebirdsofaustralia&topic_group=%24publicdomain>
Parquet file created here: <https://github.com/mediocreatmybest/gaslightingeveryone/blob/main/tools/images2parq.py>
File can also be extracted from here: <https://github.com/mediocreatmybest/gaslightingeveryone/blob/main/tools/parq2folder.py>
| Mediocreatmybest/John_Gould_Birds_of_Australia | [
"language:en",
"license:cc0-1.0",
"region:us"
]
| 2023-02-17T01:58:01+00:00 | {"language": ["en"], "license": "cc0-1.0"} | 2023-02-25T10:50:57+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-cc0-1.0 #region-us
| # Birds of Australia
As described on RAWPIXEL:
Considered the “Father of bird study in Australia”, John Gould (1804–1881) is one of the most celebrated publications on ornithology worldwide. His book "Birds of Australia" (1840–1848) illustrated by his wife, Elizabeth Gould (1804–1841) introduced more than 300 new birds to the world. His work also contributed to the much revered Charles Darwin’s book ‘On the Origins of Species’. Available under the Creative Commons 0 license.
Created from CC-0 files on URL
Image file can either be downloaded with your own script using the direct url column, or use the image data saved directly into the image column.
<URL
Parquet file created here: <URL
File can also be extracted from here: <URL
| [
"# Birds of Australia\n\nAs described on RAWPIXEL:\n\nConsidered the “Father of bird study in Australia”, John Gould (1804–1881) is one of the most celebrated publications on ornithology worldwide. His book \"Birds of Australia\" (1840–1848) illustrated by his wife, Elizabeth Gould (1804–1841) introduced more than 300 new birds to the world. His work also contributed to the much revered Charles Darwin’s book ‘On the Origins of Species’. Available under the Creative Commons 0 license.\n\nCreated from CC-0 files on URL\n\nImage file can either be downloaded with your own script using the direct url column, or use the image data saved directly into the image column.\n\n<URL\n\nParquet file created here: <URL\n\nFile can also be extracted from here: <URL"
]
| [
"TAGS\n#language-English #license-cc0-1.0 #region-us \n",
"# Birds of Australia\n\nAs described on RAWPIXEL:\n\nConsidered the “Father of bird study in Australia”, John Gould (1804–1881) is one of the most celebrated publications on ornithology worldwide. His book \"Birds of Australia\" (1840–1848) illustrated by his wife, Elizabeth Gould (1804–1841) introduced more than 300 new birds to the world. His work also contributed to the much revered Charles Darwin’s book ‘On the Origins of Species’. Available under the Creative Commons 0 license.\n\nCreated from CC-0 files on URL\n\nImage file can either be downloaded with your own script using the direct url column, or use the image data saved directly into the image column.\n\n<URL\n\nParquet file created here: <URL\n\nFile can also be extracted from here: <URL"
]
| [
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"passage: TAGS\n#language-English #license-cc0-1.0 #region-us \n# Birds of Australia\n\nAs described on RAWPIXEL:\n\nConsidered the “Father of bird study in Australia”, John Gould (1804–1881) is one of the most celebrated publications on ornithology worldwide. His book \"Birds of Australia\" (1840–1848) illustrated by his wife, Elizabeth Gould (1804–1841) introduced more than 300 new birds to the world. His work also contributed to the much revered Charles Darwin’s book ‘On the Origins of Species’. Available under the Creative Commons 0 license.\n\nCreated from CC-0 files on URL\n\nImage file can either be downloaded with your own script using the direct url column, or use the image data saved directly into the image column.\n\n<URL\n\nParquet file created here: <URL\n\nFile can also be extracted from here: <URL"
]
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|
a27dd8c5395c3c899a9f75c1a65fc44f87a26939 |
# Do what you will with the data this is old photos of crafts I used to make - just abide by the liscence above and you good to go! | Capsekai/Badge_crafts | [
"task_categories:text-to-image",
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:en",
"license:creativeml-openrail-m",
"badges",
"crafts",
"region:us"
]
| 2023-02-17T02:57:41+00:00 | {"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image", "image-classification"], "pretty_name": "Badge Craft Dataset", "tags": ["badges", "crafts"]} | 2023-02-26T10:34:30+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-to-image #task_categories-image-classification #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #badges #crafts #region-us
|
# Do what you will with the data this is old photos of crafts I used to make - just abide by the liscence above and you good to go! | [
"# Do what you will with the data this is old photos of crafts I used to make - just abide by the liscence above and you good to go!"
]
| [
"TAGS\n#task_categories-text-to-image #task_categories-image-classification #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #badges #crafts #region-us \n",
"# Do what you will with the data this is old photos of crafts I used to make - just abide by the liscence above and you good to go!"
]
| [
63,
35
]
| [
"passage: TAGS\n#task_categories-text-to-image #task_categories-image-classification #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #badges #crafts #region-us \n# Do what you will with the data this is old photos of crafts I used to make - just abide by the liscence above and you good to go!"
]
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|
1a2c4250e19f042e4e2655d386ee4bb004790c3c | A dataset of AI-generated images or images modified from them.
Products using this dataset
- [empty-eyes-LoRAs](https://huggingface.co/xenon3134-mc/empty-eyes-LoRAs) | xenon3134-mc/empty-eyes-dataset | [
"size_categories:n<1K",
"license:mit",
"region:us"
]
| 2023-02-17T03:17:51+00:00 | {"license": "mit", "size_categories": ["n<1K"]} | 2023-02-17T03:46:02+00:00 | []
| []
| TAGS
#size_categories-n<1K #license-mit #region-us
| A dataset of AI-generated images or images modified from them.
Products using this dataset
- empty-eyes-LoRAs | []
| [
"TAGS\n#size_categories-n<1K #license-mit #region-us \n"
]
| [
21
]
| [
"passage: TAGS\n#size_categories-n<1K #license-mit #region-us \n"
]
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|
6f2bec3903eea34c0302fe4427b7fe200af5e954 | # Dataset Card for "pile"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lsb/pile | [
"region:us"
]
| 2023-02-17T03:26:26+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "meta", "struct": [{"name": "pile_set_name", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1311748175503, "num_examples": 210607728}, {"name": "validation", "num_bytes": 1348824258, "num_examples": 214670}, {"name": "test", "num_bytes": 1317125199, "num_examples": 214584}], "download_size": 539336008819, "dataset_size": 1314414124960}} | 2023-02-18T10:00:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pile"
More Information needed | [
"# Dataset Card for \"pile\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pile\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pile\"\n\nMore Information needed"
]
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|
3a67e1f11995589f4c00b67eaef4caa12c740ade |
<div style='background: #ffeec0; border: 1px solid #ffd86d; padding:1em; border-radius:3px;'>
<h3 style='margin:0'>Outdated!</h3>
<p style='margin:0'>This dataset has been superseded by:</p>
<p style='margin:0'><a style="font-size: 2em;" href='https://huggingface.co/datasets/hearmeneigh/e621-rising-v3-curated'>E621 Rising V3 Curated Image Dataset</a></p>
</div>
**Warning: THIS dataset is NOT suitable for use by minors. The dataset contains X-rated/NFSW content.**
# E621 Rising: Curated Image Dataset v2
**285,466** images (~125GB) downloaded from `e621.net` with [tags](https://huggingface.co/datasets/hearmeneigh/e621-rising-v2-curated/raw/main/meta/tag-counts.by-name.json).
This is a curated dataset, picked from the E621 Rising: Raw Image Dataset v2 [available here](https://huggingface.co/datasets/hearmeneigh/e621-rising-v2-raw).
## Image Processing
* Only `jpg` and `png` images were considered
* Image width and height have been clamped to `(0, 4096]px`; larger images have been resized to meet the limit
* Alpha channels have been removed
* All images have been converted to `jpg` format
* All images have been converted to TrueColor `RGB`
* All images have been verified to load with `Pillow`
* Metadata from E621 is [available here](https://huggingface.co/datasets/hearmeneigh/e621-rising-v2-raw/tree/main/meta)
## Tags
Comprehensive list of tags and counts:
* [By name](https://huggingface.co/datasets/hearmeneigh/e621-rising-v2-curated/raw/main/meta/tag-counts.by-name.json)
* [By count](https://huggingface.co/datasets/hearmeneigh/e621-rising-v2-curated/raw/main/meta/tag-counts.by-count.json)
### Changes From E621
* Tag names have been rewritten to `[a-z0-9_]` or `<category>:[a-z0-9_]`, e.g.
* `digital_media_(artwork)` => `meta:digital_media_artwork`
* `half-closed_eyes` => `halfclosed_eyes`
* Symbols have been prefixed with `symbol:`, e.g. `symbol:<3`
* Aspect ratio has been prefixed with `aspect_ratio:`, e.g. `aspect_ratio:16_9`
* All categories except `general` have been prefixed with the category name, e.g. `artist:somename`. The categories are:
* `artist`
* `copyright`
* `character`
* `species`
* `invalid`
* `meta`
* `lore`
### Additional Tags
* Image rating
* `rating:explicit`
* `rating:questionable`
* `rating:safe` | hearmeneigh/e621-rising-v2-curated | [
"size_categories:100K<n<1M",
"furry",
"anthro",
"nsfw",
"e621",
"not-for-all-audiences",
"region:us"
]
| 2023-02-17T04:43:33+00:00 | {"size_categories": ["100K<n<1M"], "pretty_name": "E621 Rising: Curated Image Dataset v2", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 135370373465.422, "num_examples": 285466}], "download_size": 133991087241, "dataset_size": 135370373465.422}, "viewer": false, "tags": ["furry", "anthro", "nsfw", "e621", "not-for-all-audiences"]} | 2023-10-09T17:56:52+00:00 | []
| []
| TAGS
#size_categories-100K<n<1M #furry #anthro #nsfw #e621 #not-for-all-audiences #region-us
|
<div style='background: #ffeec0; border: 1px solid #ffd86d; padding:1em; border-radius:3px;'>
<h3 style='margin:0'>Outdated!</h3>
<p style='margin:0'>This dataset has been superseded by:</p>
<p style='margin:0'><a style="font-size: 2em;" href='URL Rising V3 Curated Image Dataset</a></p>
</div>
Warning: THIS dataset is NOT suitable for use by minors. The dataset contains X-rated/NFSW content.
# E621 Rising: Curated Image Dataset v2
285,466 images (~125GB) downloaded from 'URL' with tags.
This is a curated dataset, picked from the E621 Rising: Raw Image Dataset v2 available here.
## Image Processing
* Only 'jpg' and 'png' images were considered
* Image width and height have been clamped to '(0, 4096]px'; larger images have been resized to meet the limit
* Alpha channels have been removed
* All images have been converted to 'jpg' format
* All images have been converted to TrueColor 'RGB'
* All images have been verified to load with 'Pillow'
* Metadata from E621 is available here
## Tags
Comprehensive list of tags and counts:
* By name
* By count
### Changes From E621
* Tag names have been rewritten to '[a-z0-9_]' or '<category>:[a-z0-9_]', e.g.
* 'digital_media_(artwork)' => 'meta:digital_media_artwork'
* 'half-closed_eyes' => 'halfclosed_eyes'
* Symbols have been prefixed with 'symbol:', e.g. 'symbol:<3'
* Aspect ratio has been prefixed with 'aspect_ratio:', e.g. 'aspect_ratio:16_9'
* All categories except 'general' have been prefixed with the category name, e.g. 'artist:somename'. The categories are:
* 'artist'
* 'copyright'
* 'character'
* 'species'
* 'invalid'
* 'meta'
* 'lore'
### Additional Tags
* Image rating
* 'rating:explicit'
* 'rating:questionable'
* 'rating:safe' | [
"# E621 Rising: Curated Image Dataset v2\n\n285,466 images (~125GB) downloaded from 'URL' with tags.\n\nThis is a curated dataset, picked from the E621 Rising: Raw Image Dataset v2 available here.",
"## Image Processing\n* Only 'jpg' and 'png' images were considered\n* Image width and height have been clamped to '(0, 4096]px'; larger images have been resized to meet the limit\n* Alpha channels have been removed\n* All images have been converted to 'jpg' format\n* All images have been converted to TrueColor 'RGB'\n* All images have been verified to load with 'Pillow'\n* Metadata from E621 is available here",
"## Tags\nComprehensive list of tags and counts:\n\n* By name\n* By count",
"### Changes From E621\n* Tag names have been rewritten to '[a-z0-9_]' or '<category>:[a-z0-9_]', e.g.\n * 'digital_media_(artwork)' => 'meta:digital_media_artwork'\n * 'half-closed_eyes' => 'halfclosed_eyes'\n* Symbols have been prefixed with 'symbol:', e.g. 'symbol:<3'\n* Aspect ratio has been prefixed with 'aspect_ratio:', e.g. 'aspect_ratio:16_9'\n* All categories except 'general' have been prefixed with the category name, e.g. 'artist:somename'. The categories are:\n * 'artist'\n * 'copyright'\n * 'character'\n * 'species'\n * 'invalid'\n * 'meta'\n * 'lore'",
"### Additional Tags\n* Image rating\n * 'rating:explicit'\n * 'rating:questionable'\n * 'rating:safe'"
]
| [
"TAGS\n#size_categories-100K<n<1M #furry #anthro #nsfw #e621 #not-for-all-audiences #region-us \n",
"# E621 Rising: Curated Image Dataset v2\n\n285,466 images (~125GB) downloaded from 'URL' with tags.\n\nThis is a curated dataset, picked from the E621 Rising: Raw Image Dataset v2 available here.",
"## Image Processing\n* Only 'jpg' and 'png' images were considered\n* Image width and height have been clamped to '(0, 4096]px'; larger images have been resized to meet the limit\n* Alpha channels have been removed\n* All images have been converted to 'jpg' format\n* All images have been converted to TrueColor 'RGB'\n* All images have been verified to load with 'Pillow'\n* Metadata from E621 is available here",
"## Tags\nComprehensive list of tags and counts:\n\n* By name\n* By count",
"### Changes From E621\n* Tag names have been rewritten to '[a-z0-9_]' or '<category>:[a-z0-9_]', e.g.\n * 'digital_media_(artwork)' => 'meta:digital_media_artwork'\n * 'half-closed_eyes' => 'halfclosed_eyes'\n* Symbols have been prefixed with 'symbol:', e.g. 'symbol:<3'\n* Aspect ratio has been prefixed with 'aspect_ratio:', e.g. 'aspect_ratio:16_9'\n* All categories except 'general' have been prefixed with the category name, e.g. 'artist:somename'. The categories are:\n * 'artist'\n * 'copyright'\n * 'character'\n * 'species'\n * 'invalid'\n * 'meta'\n * 'lore'",
"### Additional Tags\n* Image rating\n * 'rating:explicit'\n * 'rating:questionable'\n * 'rating:safe'"
]
| [
41,
59,
106,
18,
213,
30
]
| [
"passage: TAGS\n#size_categories-100K<n<1M #furry #anthro #nsfw #e621 #not-for-all-audiences #region-us \n# E621 Rising: Curated Image Dataset v2\n\n285,466 images (~125GB) downloaded from 'URL' with tags.\n\nThis is a curated dataset, picked from the E621 Rising: Raw Image Dataset v2 available here.## Image Processing\n* Only 'jpg' and 'png' images were considered\n* Image width and height have been clamped to '(0, 4096]px'; larger images have been resized to meet the limit\n* Alpha channels have been removed\n* All images have been converted to 'jpg' format\n* All images have been converted to TrueColor 'RGB'\n* All images have been verified to load with 'Pillow'\n* Metadata from E621 is available here## Tags\nComprehensive list of tags and counts:\n\n* By name\n* By count### Changes From E621\n* Tag names have been rewritten to '[a-z0-9_]' or '<category>:[a-z0-9_]', e.g.\n * 'digital_media_(artwork)' => 'meta:digital_media_artwork'\n * 'half-closed_eyes' => 'halfclosed_eyes'\n* Symbols have been prefixed with 'symbol:', e.g. 'symbol:<3'\n* Aspect ratio has been prefixed with 'aspect_ratio:', e.g. 'aspect_ratio:16_9'\n* All categories except 'general' have been prefixed with the category name, e.g. 'artist:somename'. The categories are:\n * 'artist'\n * 'copyright'\n * 'character'\n * 'species'\n * 'invalid'\n * 'meta'\n * 'lore'### Additional Tags\n* Image rating\n * 'rating:explicit'\n * 'rating:questionable'\n * 'rating:safe'"
]
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|
0243f2e6d606a615ccc68744869108d4de27d869 | # Dataset Card for "wikipedia.reorder.natural.pl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/wikipedia.reorder.natural.pl | [
"region:us"
]
| 2023-02-17T08:27:57+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1958124685, "num_examples": 1772445}], "download_size": 523553918, "dataset_size": 1958124685}} | 2023-02-17T12:00:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
ee478e9afd9139966912c12161b4597124dd3349 | # Dataset Card for "augment_train_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MohammedNasri/augment_train_dataset | [
"region:us"
]
| 2023-02-17T09:47:16+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8532425445.0, "num_examples": 81760}, {"name": "eval", "num_bytes": 304561718.0, "num_examples": 10440}], "download_size": 8179433148, "dataset_size": 8836987163.0}} | 2023-02-17T09:53:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "augment_train_dataset"
More Information needed | [
"# Dataset Card for \"augment_train_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"augment_train_dataset\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"augment_train_dataset\"\n\nMore Information needed"
]
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|
49bad0c575d4f8a9de6a51afff3484651a582567 | # Dataset Card for "class_dataset_real_donut"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LFBMS/class_dataset_real_donut | [
"region:us"
]
| 2023-02-17T09:51:22+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "bilanz_h", "1": "bilanz_v", "2": "guv", "3": "kontennachweis_bilanz", "4": "kontennachweis_guv", "5": "other", "6": "text"}}}}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 327762478.0, "num_examples": 1117}, {"name": "test", "num_bytes": 99667843.0, "num_examples": 280}], "download_size": 400428133, "dataset_size": 427430321.0}} | 2023-02-17T09:51:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "class_dataset_real_donut"
More Information needed | [
"# Dataset Card for \"class_dataset_real_donut\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"class_dataset_real_donut\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"class_dataset_real_donut\"\n\nMore Information needed"
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|
c3e3f423063e1c822b25d5c677370991b612ead7 | # Dataset Card for "class_dataset_real2_donut"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LFBMS/class_dataset_real2_donut | [
"region:us"
]
| 2023-02-17T10:00:13+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "bilanz_h", "1": "bilanz_v", "2": "guv", "3": "kontennachweis_bilanz", "4": "kontennachweis_guv", "5": "other"}}}}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 340313532.0, "num_examples": 1117}, {"name": "test", "num_bytes": 87116926.0, "num_examples": 280}], "download_size": 400625159, "dataset_size": 427430458.0}} | 2023-02-17T10:00:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "class_dataset_real2_donut"
More Information needed | [
"# Dataset Card for \"class_dataset_real2_donut\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"class_dataset_real2_donut\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"class_dataset_real2_donut\"\n\nMore Information needed"
]
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|
d794c4fec04839020f54b071d3871f17e944638f |
An imitation learning environment for the atari_pong environment, sample for the policy atari_2B_atari_pong_1111
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
| edbeeching/prj_gia_dataset_atari_2B_atari_pong_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-02-17T10:04:25+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-02-21T17:07:48+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the atari_pong environment, sample for the policy atari_2B_atari_pong_1111
This environment was created as part of the Generally Intelligent Agents project gia: URL
| []
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
| [
46
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
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|
b395321beb1b0eb4305283b7657248c794c68916 | # Dataset Card for "class_dataset_real3_donut"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LFBMS/class_dataset_real3_donut | [
"region:us"
]
| 2023-02-17T10:14:22+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "bilanz", "1": "guv", "2": "kontennachweis_bilanz", "3": "kontennachweis_guv", "4": "other"}}}}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 327835672.0, "num_examples": 1117}, {"name": "test", "num_bytes": 99594248.0, "num_examples": 280}], "download_size": 400602803, "dataset_size": 427429920.0}} | 2023-02-17T10:14:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "class_dataset_real3_donut"
More Information needed | [
"# Dataset Card for \"class_dataset_real3_donut\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"class_dataset_real3_donut\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"class_dataset_real3_donut\"\n\nMore Information needed"
]
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|
2ca3012ef85a60143a8f97b83a45bb1a7b5c2244 |
# VGGSound
VGG-Sound is an audio-visual correspondent dataset consisting of short clips of audio sounds, extracted from videos uploaded to YouTube.
- **Homepage:** https://www.robots.ox.ac.uk/~vgg/data/vggsound/
- **Paper:** https://arxiv.org/abs/2004.14368
- **Github:** https://github.com/hche11/VGGSound
## Analysis
- **310+ classes:** VGG-Sound contains audios spanning a large number of challenging acoustic environments and noise characteristics of real applications.
- **200,000+ videos:** All videos are captured "in the wild" with audio-visual correspondence in the sense that the sound source is visually evident.
- **550+ hours:** VGG-Sound consists of both audio and video. Each segment is 10 seconds long.
## Download
We provide a csv file. For each YouTube video, we provide YouTube URLs, time stamps, audio labels and train/test split. Each line in the csv file has columns defined by here.
```
# YouTube ID, start seconds, label, train/test split.
```
And you can download VGGSound directly from this [repository](https://huggingface.co/datasets/Loie/VGGSound/tree/main).
## License
The VGG-Sound dataset is available to download for commercial/research purposes under a Creative Commons Attribution 4.0 International License. The copyright remains with the original owners of the video. A complete version of the license can be found [here](https://thor.robots.ox.ac.uk/datasets/vggsound/license_vggsound.txt).
## Citation
Please cite the following if you make use of the dataset.
```
@InProceedings{Chen20,
author = "Honglie Chen and Weidi Xie and Andrea Vedaldi and Andrew Zisserman",
title = "VGGSound: A Large-scale Audio-Visual Dataset",
booktitle = "International Conference on Acoustics, Speech, and Signal Processing (ICASSP)",
year = "2020",
}
``` | Loie/VGGSound | [
"task_categories:audio-classification",
"size_categories:100B<n<1T",
"arxiv:2004.14368",
"region:us"
]
| 2023-02-17T10:27:55+00:00 | {"size_categories": ["100B<n<1T"], "task_categories": ["audio-classification"]} | 2023-03-26T12:25:40+00:00 | [
"2004.14368"
]
| []
| TAGS
#task_categories-audio-classification #size_categories-100B<n<1T #arxiv-2004.14368 #region-us
|
# VGGSound
VGG-Sound is an audio-visual correspondent dataset consisting of short clips of audio sounds, extracted from videos uploaded to YouTube.
- Homepage: URL
- Paper: URL
- Github: URL
## Analysis
- 310+ classes: VGG-Sound contains audios spanning a large number of challenging acoustic environments and noise characteristics of real applications.
- 200,000+ videos: All videos are captured "in the wild" with audio-visual correspondence in the sense that the sound source is visually evident.
- 550+ hours: VGG-Sound consists of both audio and video. Each segment is 10 seconds long.
## Download
We provide a csv file. For each YouTube video, we provide YouTube URLs, time stamps, audio labels and train/test split. Each line in the csv file has columns defined by here.
And you can download VGGSound directly from this repository.
## License
The VGG-Sound dataset is available to download for commercial/research purposes under a Creative Commons Attribution 4.0 International License. The copyright remains with the original owners of the video. A complete version of the license can be found here.
Please cite the following if you make use of the dataset.
| [
"# VGGSound\nVGG-Sound is an audio-visual correspondent dataset consisting of short clips of audio sounds, extracted from videos uploaded to YouTube.\n\n\n- Homepage: URL\n- Paper: URL\n- Github: URL",
"## Analysis\n\n- 310+ classes: VGG-Sound contains audios spanning a large number of challenging acoustic environments and noise characteristics of real applications.\n- 200,000+ videos: All videos are captured \"in the wild\" with audio-visual correspondence in the sense that the sound source is visually evident.\n- 550+ hours: VGG-Sound consists of both audio and video. Each segment is 10 seconds long.\n\n",
"## Download\n\nWe provide a csv file. For each YouTube video, we provide YouTube URLs, time stamps, audio labels and train/test split. Each line in the csv file has columns defined by here.\n\n\n\nAnd you can download VGGSound directly from this repository.",
"## License\nThe VGG-Sound dataset is available to download for commercial/research purposes under a Creative Commons Attribution 4.0 International License. The copyright remains with the original owners of the video. A complete version of the license can be found here.\n\nPlease cite the following if you make use of the dataset."
]
| [
"TAGS\n#task_categories-audio-classification #size_categories-100B<n<1T #arxiv-2004.14368 #region-us \n",
"# VGGSound\nVGG-Sound is an audio-visual correspondent dataset consisting of short clips of audio sounds, extracted from videos uploaded to YouTube.\n\n\n- Homepage: URL\n- Paper: URL\n- Github: URL",
"## Analysis\n\n- 310+ classes: VGG-Sound contains audios spanning a large number of challenging acoustic environments and noise characteristics of real applications.\n- 200,000+ videos: All videos are captured \"in the wild\" with audio-visual correspondence in the sense that the sound source is visually evident.\n- 550+ hours: VGG-Sound consists of both audio and video. Each segment is 10 seconds long.\n\n",
"## Download\n\nWe provide a csv file. For each YouTube video, we provide YouTube URLs, time stamps, audio labels and train/test split. Each line in the csv file has columns defined by here.\n\n\n\nAnd you can download VGGSound directly from this repository.",
"## License\nThe VGG-Sound dataset is available to download for commercial/research purposes under a Creative Commons Attribution 4.0 International License. The copyright remains with the original owners of the video. A complete version of the license can be found here.\n\nPlease cite the following if you make use of the dataset."
]
| [
39,
53,
108,
64,
66
]
| [
"passage: TAGS\n#task_categories-audio-classification #size_categories-100B<n<1T #arxiv-2004.14368 #region-us \n# VGGSound\nVGG-Sound is an audio-visual correspondent dataset consisting of short clips of audio sounds, extracted from videos uploaded to YouTube.\n\n\n- Homepage: URL\n- Paper: URL\n- Github: URL## Analysis\n\n- 310+ classes: VGG-Sound contains audios spanning a large number of challenging acoustic environments and noise characteristics of real applications.\n- 200,000+ videos: All videos are captured \"in the wild\" with audio-visual correspondence in the sense that the sound source is visually evident.\n- 550+ hours: VGG-Sound consists of both audio and video. Each segment is 10 seconds long.\n\n## Download\n\nWe provide a csv file. For each YouTube video, we provide YouTube URLs, time stamps, audio labels and train/test split. Each line in the csv file has columns defined by here.\n\n\n\nAnd you can download VGGSound directly from this repository.## License\nThe VGG-Sound dataset is available to download for commercial/research purposes under a Creative Commons Attribution 4.0 International License. The copyright remains with the original owners of the video. A complete version of the license can be found here.\n\nPlease cite the following if you make use of the dataset."
]
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|
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