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e37180a26ce5c427e416b54b09dd52c1bbe16edb
|
# Dataset Card for "sorted_generate_sub_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Jing24/sorted_generate_sub_4
|
[
"region:us"
] |
2023-10-24T19:00:59+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "conf", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 42698509, "num_examples": 46640}], "download_size": 7864808, "dataset_size": 42698509}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T20:19:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sorted_generate_sub_4"
More Information needed
|
[
"# Dataset Card for \"sorted_generate_sub_4\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sorted_generate_sub_4\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sorted_generate_sub_4\"\n\nMore Information needed"
] |
635762215f5cad5201bc58c7734654204a302e88
|
# Dataset Card for "TalkBank_CA_Croatian"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_Croatian
|
[
"region:us"
] |
2023-10-24T19:08:05+00:00
|
{"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5827530910, "num_examples": 135}], "download_size": 5834902692, "dataset_size": 5827530910}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T19:11:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_Croatian"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_Croatian\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_Croatian\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_Croatian\"\n\nMore Information needed"
] |
260231ddd1c6382ad1454112b1e756d1ec33ed3e
|
# **NOTE**: Please see [EleutherAI/proof-pile-2](https://huggingface.co/datasets/EleutherAI/proof-pile-2/blob/main/README.md)
This is a cherry-picked repackaging of the `algebraic-stack` segment from the proof-pile-2 dataset as parquet files
### License
see [EleutherAI/proof-pile-2](https://huggingface.co/datasets/EleutherAI/proof-pile-2/blob/main/README.md)
### Citation
see [EleutherAI/proof-pile-2](https://huggingface.co/datasets/EleutherAI/proof-pile-2/blob/main/README.md)
|
typeof/algebraic-stack
|
[
"task_categories:text-generation",
"size_categories:10B<n<100B",
"language:en",
"math",
"region:us"
] |
2023-10-24T19:12:06+00:00
|
{"language": ["en"], "size_categories": ["10B<n<100B"], "task_categories": ["text-generation"], "tags": ["math"]}
|
2023-10-24T20:56:39+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #size_categories-10B<n<100B #language-English #math #region-us
|
# NOTE: Please see EleutherAI/proof-pile-2
This is a cherry-picked repackaging of the 'algebraic-stack' segment from the proof-pile-2 dataset as parquet files
### License
see EleutherAI/proof-pile-2
see EleutherAI/proof-pile-2
|
[
"# NOTE: Please see EleutherAI/proof-pile-2\n\nThis is a cherry-picked repackaging of the 'algebraic-stack' segment from the proof-pile-2 dataset as parquet files",
"### License\nsee EleutherAI/proof-pile-2\n\n\nsee EleutherAI/proof-pile-2"
] |
[
"TAGS\n#task_categories-text-generation #size_categories-10B<n<100B #language-English #math #region-us \n",
"# NOTE: Please see EleutherAI/proof-pile-2\n\nThis is a cherry-picked repackaging of the 'algebraic-stack' segment from the proof-pile-2 dataset as parquet files",
"### License\nsee EleutherAI/proof-pile-2\n\n\nsee EleutherAI/proof-pile-2"
] |
[
35,
52,
25
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-10B<n<100B #language-English #math #region-us \n# NOTE: Please see EleutherAI/proof-pile-2\n\nThis is a cherry-picked repackaging of the 'algebraic-stack' segment from the proof-pile-2 dataset as parquet files### License\nsee EleutherAI/proof-pile-2\n\n\nsee EleutherAI/proof-pile-2"
] |
85cf57ee3ebf65adb31a965f0838163bafda844f
|
# Dataset Card for "TalkBank_CA_Bergmann"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_Bergmann
|
[
"region:us"
] |
2023-10-24T19:13:23+00:00
|
{"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 170179623, "num_examples": 83}], "download_size": 169288052, "dataset_size": 170179623}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T19:13:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_Bergmann"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_Bergmann\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_Bergmann\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_Bergmann\"\n\nMore Information needed"
] |
0fce8e28597ff2e8fc43bdfdf9cb9bc368a70018
|
# Dataset Card for "TalkBank_CA_CLAPI"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_CLAPI
|
[
"region:us"
] |
2023-10-24T19:16:02+00:00
|
{"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1650568570, "num_examples": 24}], "download_size": 1653241682, "dataset_size": 1650568570}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T19:17:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_CLAPI"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_CLAPI\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_CLAPI\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_CLAPI\"\n\nMore Information needed"
] |
955078231ec69d16b895c8f75dbbb772979888a8
|
# Dataset Card for "TalkBank_CA_CORAAL"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_CORAAL
|
[
"region:us"
] |
2023-10-24T19:26:01+00:00
|
{"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9325350278, "num_examples": 85}], "download_size": 9338282164, "dataset_size": 9325350278}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T19:32:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_CORAAL"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_CORAAL\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_CORAAL\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_CORAAL\"\n\nMore Information needed"
] |
36ffb8917d00111da0a969012563ee6b764038d9
|
image to sounds using facebook: images here?
space: button, images and audios,
|
Violetmae14/image-to-sound-with-effects-using-videos
|
[
"license:artistic-2.0",
"region:us"
] |
2023-10-24T19:35:23+00:00
|
{"license": "artistic-2.0"}
|
2023-10-24T19:40:56+00:00
|
[] |
[] |
TAGS
#license-artistic-2.0 #region-us
|
image to sounds using facebook: images here?
space: button, images and audios,
|
[] |
[
"TAGS\n#license-artistic-2.0 #region-us \n"
] |
[
14
] |
[
"passage: TAGS\n#license-artistic-2.0 #region-us \n"
] |
973f08872576d9bf8f528dc940b666b7be0009e8
|
# Dataset Card for "chemnlp-chem-caption"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kjappelbaum/chemnlp-chem-caption
|
[
"region:us"
] |
2023-10-24T19:58:29+00:00
|
{"dataset_info": [{"config_name": "default", "features": [{"name": "representation", "dtype": "string"}, {"name": "representation_type", "dtype": "string"}, {"name": "prompt_template", "dtype": "string"}, {"name": "completion_template", "dtype": "string"}, {"name": "completion", "dtype": "string"}, {"name": "completion_names", "dtype": "string"}, {"name": "completion_labels", "dtype": "string"}, {"name": "constraint", "dtype": "string"}, {"name": "filled_prompt", "dtype": "string"}, {"name": "filled_completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 206763010, "num_examples": 323500}], "download_size": 15384432, "dataset_size": 206763010}, {"config_name": "rdkit_feat", "features": [{"name": "representation_type", "dtype": "string"}, {"name": "num_valence_electrons", "dtype": "int64"}, {"name": "rotable_proportion", "dtype": "float64"}, {"name": "non_rotable_proportion", "dtype": "float64"}, {"name": "num_unspecified_bond", "dtype": "float64"}, {"name": "num_single_bonds", "dtype": "float64"}, {"name": "num_double_bonds", "dtype": "float64"}, {"name": "num_triple_bonds", "dtype": "float64"}, {"name": "num_quadruple_bonds", "dtype": "float64"}, {"name": "num_quintuple_bonds", "dtype": "float64"}, {"name": "num_hextuple_bonds", "dtype": "float64"}, {"name": "num_oneandahalf_bonds", "dtype": "float64"}, {"name": "num_twoandahalf_bonds", "dtype": "float64"}, {"name": "num_threeandahalf_bonds", "dtype": "float64"}, {"name": "num_fourandahalf_bonds", "dtype": "float64"}, {"name": "num_fiveandahalf_bonds", "dtype": "float64"}, {"name": "num_aromatic_bonds", "dtype": "float64"}, {"name": "num_ionic_bonds", "dtype": "float64"}, {"name": "num_hydrogen_bonds", "dtype": "float64"}, {"name": "num_threecenter_bonds", "dtype": "float64"}, {"name": "num_dativeone_bonds", "dtype": "float64"}, {"name": "num_dative_bonds", "dtype": "float64"}, {"name": "num_other_bonds", "dtype": "float64"}, {"name": "num_zero_bonds", "dtype": "float64"}, {"name": "num_bonds", "dtype": "int64"}, {"name": "molecular_formula", "dtype": "string"}, {"name": "monoisotopic_molecular_mass", "dtype": "float64"}, {"name": "num_carbon_atoms", "dtype": "int64"}, {"name": "num_hydrogen_atoms", "dtype": "int64"}, {"name": "num_nitrogen_atoms", "dtype": "int64"}, {"name": "num_oxygen_atoms", "dtype": "int64"}, {"name": "carbon_mass", "dtype": "float64"}, {"name": "hydrogen_mass", "dtype": "float64"}, {"name": "nitrogen_mass", "dtype": "float64"}, {"name": "oxygen_mass", "dtype": "float64"}, {"name": "num_hydrogen_bond_acceptors", "dtype": "int64"}, {"name": "num_hydrogen_bond_donors", "dtype": "int64"}, {"name": "num_lipinski_violations", "dtype": "int64"}, {"name": "inertial_shape_factor", "dtype": "float64"}, {"name": "eccentricity", "dtype": "float64"}, {"name": "asphericity", "dtype": "float64"}, {"name": "npr1_value", "dtype": "float64"}, {"name": "npr2_value", "dtype": "float64"}, {"name": "pmi1_value", "dtype": "float64"}, {"name": "pmi2_value", "dtype": "float64"}, {"name": "pmi3_value", "dtype": "float64"}, {"name": "num_chiral_centers", "dtype": "int64"}, {"name": "representation", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 41660808, "num_examples": 82337}], "download_size": 16423699, "dataset_size": 41660808}, {"config_name": "rdkit_feat_0", "features": [{"name": "tert-butyloxycarbonyl_count", "dtype": "int64"}, {"name": "representation_type", "dtype": "string"}, {"name": "trityl_count", "dtype": "int64"}, {"name": "3,5-dimethoxyphenylisoproxycarbonyl_count", "dtype": "int64"}, {"name": "2-(4-biphenyl)isopropoxycarbonyl_count", "dtype": "int64"}, {"name": "2-nitrophenylsulfenyl_count", "dtype": "int64"}, {"name": "boc_count", "dtype": "int64"}, {"name": "trt_count", "dtype": "int64"}, {"name": "ddz_count", "dtype": "int64"}, {"name": "bpoc_count", "dtype": "int64"}, {"name": "nps_count", "dtype": "int64"}, {"name": "9-fluorenylmethoxycarbonyl_count", "dtype": "int64"}, {"name": "2-(4-nitrophenylsulfonyl)ethoxycarbonyl_count", "dtype": "int64"}, {"name": "(1,1-dioxobenzo[b]thiophene-2-yl)methyloxycarbonyl_count", "dtype": "int64"}, {"name": "(1,1-dioxonaptho[1,2-b]thiophene-2-yl)methyloxycarbonyl_count", "dtype": "int64"}, {"name": "1-(4,4-dimethyl-2,6-dioxocyclohex-1-ylidene)-3-methylbutyl_count", "dtype": "int64"}, {"name": "2,7-di-tert-butyl-fmoc_count", "dtype": "int64"}, {"name": "2-fluoro-fmoc_count", "dtype": "int64"}, {"name": "2-monoisooctyl-fmoc_count", "dtype": "int64"}, {"name": "2,7-diisooctyl-fmoc_count", "dtype": "int64"}, {"name": "tetrachlorophthaloyl_count", "dtype": "int64"}, {"name": "2-[phenyl(methyl)sulfonio])ethyloxycarbonyltetrafluoroborate_count", "dtype": "int64"}, {"name": "ethanesulfonylethoxycarbonyl_count", "dtype": "int64"}, {"name": "2-(4-sulfophenylsulfonyl)ethoxycarbonyl_count", "dtype": "int64"}, {"name": "fmoc_count", "dtype": "int64"}, {"name": "nsc_count", "dtype": "int64"}, {"name": "bsmoc_count", "dtype": "int64"}, {"name": "alpha-nsmoc_count", "dtype": "int64"}, {"name": "ivdde_count", "dtype": "int64"}, {"name": "fmoc*_count", "dtype": "int64"}, {"name": "fmoc(fmoc(2f))_count", "dtype": "int64"}, {"name": "mio-fmoc_count", "dtype": "int64"}, {"name": "dio-fmoc_count", "dtype": "int64"}, {"name": "tcp_count", "dtype": "int64"}, {"name": "pms_count", "dtype": "int64"}, {"name": "esc_count", "dtype": "int64"}, {"name": "sps_count", "dtype": "int64"}, {"name": "benzyloxycarbonyl_count", "dtype": "int64"}, {"name": "allyloxycarbonyl_count", "dtype": "int64"}, {"name": "o-nitrobenzenesulfonyl_count", "dtype": "int64"}, {"name": "2,4-dinitrobenzenesulfonyl_count", "dtype": "int64"}, {"name": "benzothiazole-2-sulfonyl_count", "dtype": "int64"}, {"name": "2,2,2-trichloroethyloxycarbonyl_count", "dtype": "int64"}, {"name": "dithiasuccinoyl_count", "dtype": "int64"}, {"name": "p-nitrobenzyloxycarbonyl_count", "dtype": "int64"}, {"name": "alpha-azidoacids_count", "dtype": "int64"}, {"name": "proparglyoxycarbonyl_count", "dtype": "int64"}, {"name": "o-nitrobenzylcarbonyl_count", "dtype": "int64"}, {"name": "4-nitroveratryloxycarbonyl_count", "dtype": "int64"}, {"name": "2-(2-nitrophenyl)propyloxycarbonyl_count", "dtype": "int64"}, {"name": "2-(3,4-methylenedioxy-6-nitrophenyl)propyloxycarbonyl_count", "dtype": "int64"}, {"name": "9-(4-bromophenyl)-9-fluorenyl_count", "dtype": "int64"}, {"name": "azidomethoxycarbonyl_count", "dtype": "int64"}, {"name": "hexafluoroacetone_count", "dtype": "int64"}, {"name": "Z_count", "dtype": "int64"}, {"name": "alloc_count", "dtype": "int64"}, {"name": "o-nbs_count", "dtype": "int64"}, {"name": "d-nbs_count", "dtype": "int64"}, {"name": "bts_count", "dtype": "int64"}, {"name": "troc_count", "dtype": "int64"}, {"name": "dts_count", "dtype": "int64"}, {"name": "pnz_count", "dtype": "int64"}, {"name": "poc_count", "dtype": "int64"}, {"name": "onz_count", "dtype": "int64"}, {"name": "nvoc_count", "dtype": "int64"}, {"name": "nppoc_count", "dtype": "int64"}, {"name": "mnppoc_count", "dtype": "int64"}, {"name": "brphf_count", "dtype": "int64"}, {"name": "azoc_count", "dtype": "int64"}, {"name": "hfa_count", "dtype": "int64"}, {"name": "2-chlorobenzyloxycarbonyl_count", "dtype": "int64"}, {"name": "4-methyltrityl_count", "dtype": "int64"}, {"name": "cl-z_count", "dtype": "int64"}, {"name": "mtt_count", "dtype": "int64"}, {"name": "1-(4,4-dimethyl-2,6-dioxocylohex-1-ylidene)-3-methylbutyl_count", "dtype": "int64"}, {"name": "trifluoroacetyl_count", "dtype": "int64"}, {"name": "2-(methylsulfonyl)ethoxycarbonyl_count", "dtype": "int64"}, {"name": "tfa_count", "dtype": "int64"}, {"name": "msc_count", "dtype": "int64"}, {"name": "phenyldisulphanylethyloxycarbonyl_count", "dtype": "int64"}, {"name": "2-pyridyldisulphanylethyloxycarbonyl_count", "dtype": "int64"}, {"name": "phdec_count", "dtype": "int64"}, {"name": "pydec_count", "dtype": "int64"}, {"name": "tert-butyl_count", "dtype": "int64"}, {"name": 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{"name": "benzyl_count", "dtype": "int64"}, {"name": "phenacyl_count", "dtype": "int64"}, {"name": "p-nitrobenzyl_count", "dtype": "int64"}, {"name": "2-trimethylsilyethyl_count", "dtype": "int64"}, {"name": "(2-phenyl-2-trimethylsilyl)ethyl_count", "dtype": "int64"}, {"name": "2-(trimethylsilyl)isopropyl_count", "dtype": "int64"}, {"name": "2,2,2-trichloroethyl_count", "dtype": "int64"}, {"name": "p-hydroxyphenacyl_count", "dtype": "int64"}, {"name": "4,5-dimethyoxy-2-nitrobenzyl_count", "dtype": "int64"}, {"name": "1,1-dimethylallyl_count", "dtype": "int64"}, {"name": "pentaaminecobalt_III_count", "dtype": "int64"}, {"name": "al_count", "dtype": "int64"}, {"name": "bn_count", "dtype": "int64"}, {"name": "pac_count", "dtype": "int64"}, {"name": "pnb_count", "dtype": "int64"}, {"name": "tmse_count", "dtype": "int64"}, {"name": "ptmse_count", "dtype": "int64"}, {"name": "tmsi_count", "dtype": "int64"}, {"name": "tce_count", "dtype": "int64"}, {"name": "php_count", "dtype": "int64"}, 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{"name": "1H-2,3-benzoxazine_count", "dtype": "int64"}, {"name": "4H-3,1-benzoxazine_count", "dtype": "int64"}, {"name": "2H-1,2-benzoxazine_count", "dtype": "int64"}, {"name": "4H-1,3-benzoxazine_count", "dtype": "int64"}, {"name": "anthracene_count", "dtype": "int64"}, {"name": "phenanthrene_count", "dtype": "int64"}, {"name": "phenalene_count", "dtype": "int64"}, {"name": "fluorene_count", "dtype": "int64"}, {"name": "carbazole_count", "dtype": "int64"}, {"name": "xanthene_count", "dtype": "int64"}, {"name": "acridine_count", "dtype": "int64"}, {"name": "norpinane_count", "dtype": "int64"}, {"name": "7H-purine_count", "dtype": "int64"}, {"name": "steroid_ring_system_count", "dtype": "int64"}, {"name": "imidazole_count", "dtype": "int64"}, {"name": "thiazol-2-amine_count", "dtype": "int64"}, {"name": "tetrazole_count", "dtype": "int64"}, {"name": "cytosine_count", "dtype": "int64"}, {"name": "adenine_count", "dtype": "int64"}, {"name": "5-methylindole_count", "dtype": "int64"}, 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"4H-1,2-diazepine_count", "dtype": "int64"}, {"name": "indene_count", "dtype": "int64"}, {"name": "2H-indene_count", "dtype": "int64"}, {"name": "benzofuran_count", "dtype": "int64"}, {"name": "isobenzofuran_count", "dtype": "int64"}, {"name": "benzo[b]thiophene_count", "dtype": "int64"}, {"name": "benzo[c]thiophene_count", "dtype": "int64"}, {"name": "indole_count", "dtype": "int64"}, {"name": "3H-indole_count", "dtype": "int64"}, {"name": "1H-indole_count", "dtype": "int64"}, {"name": "cyclopenta[b]pyridine_count", "dtype": "int64"}, {"name": "pyrano[3,4-b]-pyrrole_count", "dtype": "int64"}, {"name": "indazole_count", "dtype": "int64"}, {"name": "benzisoxazole_count", "dtype": "int64"}, {"name": "benzoxazole_count", "dtype": "int64"}, {"name": "2,1-benzisoxazole_count", "dtype": "int64"}, {"name": "naphthalene_count", "dtype": "int64"}, {"name": "1,2,3,4-tetrahydronaphthalene_count", "dtype": "int64"}, {"name": "octahydronaphthalene_count", "dtype": "int64"}, {"name": "2H-1-benzopyran_count", "dtype": "int64"}, {"name": "2H-1-benzopyran-2-one_count", "dtype": "int64"}, {"name": "4H-1-benzopyran-4-one_count", "dtype": "int64"}, {"name": "1H-2-benzopyran-1-one_count", "dtype": "int64"}, {"name": "3H-2-benzopyran-1-one_count", "dtype": "int64"}, {"name": "quinoline_count", "dtype": "int64"}, {"name": "isoquinoline_count", "dtype": "int64"}, {"name": "cinnoline_count", "dtype": "int64"}, {"name": "quinazoline_count", "dtype": "int64"}, {"name": "1,8-napthyhridine_count", "dtype": "int64"}, {"name": "1,7-napththyridine_count", "dtype": "int64"}, {"name": "1,5-napththridine_count", "dtype": "int64"}, {"name": "1,6-napthyridine_count", "dtype": "int64"}, {"name": "2H-1,3-benzoxazine_count", "dtype": "int64"}, {"name": "2H-1,4-benzoxazine_count", "dtype": "int64"}, {"name": "1H-2,3-benzoxazine_count", "dtype": "int64"}, {"name": "4H-3,1-benzoxazine_count", "dtype": "int64"}, {"name": "2H-1,2-benzoxazine_count", "dtype": "int64"}, {"name": "4H-1,3-benzoxazine_count", "dtype": "int64"}, {"name": "anthracene_count", "dtype": "int64"}, {"name": "phenanthrene_count", "dtype": "int64"}, {"name": "phenalene_count", "dtype": "int64"}, {"name": "fluorene_count", "dtype": "int64"}, {"name": "carbazole_count", "dtype": "int64"}, {"name": "xanthene_count", "dtype": "int64"}, {"name": "acridine_count", "dtype": "int64"}, {"name": "norpinane_count", "dtype": "int64"}, {"name": "7H-purine_count", "dtype": "int64"}, {"name": "steroid_ring_system_count", "dtype": "int64"}, {"name": "imidazole_count", "dtype": "int64"}, {"name": "thiazol-2-amine_count", "dtype": "int64"}, {"name": "tetrazole_count", "dtype": "int64"}, {"name": "cytosine_count", "dtype": "int64"}, {"name": "adenine_count", "dtype": "int64"}, {"name": "5-methylindole_count", "dtype": "int64"}, {"name": "isocaffeine_count", "dtype": "int64"}, {"name": "tetrazolethiol_count", "dtype": "int64"}, {"name": "3-methylisoxazole_count", "dtype": "int64"}, {"name": "1-methylimidazole_count", "dtype": "int64"}, {"name": "2-methylimidazole_count", "dtype": "int64"}, {"name": "guanine_count", "dtype": "int64"}, {"name": "tosufloxacin_count", "dtype": "int64"}, {"name": "acetamido_count", "dtype": "int64"}, {"name": "acetoacetyl_count", "dtype": "int64"}, {"name": "acetyl_count", "dtype": "int64"}, {"name": "acryloyl_count", "dtype": "int64"}, {"name": "alanyl_count", "dtype": "int64"}, {"name": "beta-alanyl_count", "dtype": "int64"}, {"name": "allylidene_count", "dtype": "int64"}, {"name": "amidino_count", "dtype": "int64"}, {"name": "amino_count", "dtype": "int64"}, {"name": "amyl_count", "dtype": "int64"}, {"name": "anilino_count", "dtype": "int64"}, {"name": "anisidino_count", "dtype": "int64"}, {"name": "anthranoyl_count", "dtype": "int64"}, {"name": "arsino_count", "dtype": "int64"}, {"name": "azelaoyl_count", "dtype": "int64"}, {"name": "azido_count", "dtype": "int64"}, {"name": "azo_count", "dtype": "int64"}, {"name": "azoxy_count", "dtype": "int64"}, {"name": "benzal_count", "dtype": "int64"}, {"name": "benzamido_count", "dtype": "int64"}, {"name": "benzhydrol_count", "dtype": "int64"}, {"name": "benzoxy_count", "dtype": "int64"}, {"name": "benzoyl_count", "dtype": "int64"}, {"name": "benzylidene_count", "dtype": "int64"}, {"name": "benzylidyne_count", "dtype": "int64"}, {"name": "biphenylyl_count", "dtype": "int64"}, {"name": "biphenylene_count", "dtype": "int64"}, {"name": "butoxy_count", "dtype": "int64"}, {"name": "sec-butoxy_count", "dtype": "int64"}, {"name": "tert-butoxy_count", "dtype": "int64"}, {"name": "butyl_count", "dtype": "int64"}, {"name": "sec-butyl_count", "dtype": "int64"}, {"name": "butyryl_count", "dtype": "int64"}, {"name": "caproyl_count", "dtype": "int64"}, {"name": "capryl_count", "dtype": "int64"}, {"name": "capryloyl_count", "dtype": "int64"}, {"name": "carbamido_count", "dtype": "int64"}, {"name": "carbamoyl_count", "dtype": "int64"}, {"name": "carbamyl_count", "dtype": "int64"}, {"name": 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"heptadecanoyl_count", "dtype": "int64"}, {"name": "heptanamido_count", "dtype": "int64"}, {"name": "heptanoyl_count", "dtype": "int64"}, {"name": "hexadecanoyl_count", "dtype": "int64"}, {"name": "hexamethylene_count", "dtype": "int64"}, {"name": "hexanedioyl_count", "dtype": "int64"}, {"name": "hippuryl_count", "dtype": "int64"}, {"name": "hydrazino_count", "dtype": "int64"}, {"name": "hydrazo_count", "dtype": "int64"}, {"name": "hydrocinnamoyl_count", "dtype": "int64"}, {"name": "hydroperoxy_count", "dtype": "int64"}, {"name": "hydroxyamino_count", "dtype": "int64"}, {"name": "imino_count", "dtype": "int64"}, {"name": "iodoso_count", "dtype": "int64"}, {"name": "iodyl_count", "dtype": "int64"}, {"name": "isoamyl_count", "dtype": "int64"}, {"name": "isobutenyl_count", "dtype": "int64"}, {"name": "isobutoxy_count", "dtype": "int64"}, {"name": "isobutyl_count", "dtype": "int64"}, {"name": "isobutylidene_count", "dtype": "int64"}, {"name": "isobutyryl_count", "dtype": "int64"}, {"name": "isocyanato_count", "dtype": "int64"}, {"name": "isocyano_count", "dtype": "int64"}, {"name": "isohexyl_count", "dtype": "int64"}, {"name": "isoleucyl_count", "dtype": "int64"}, {"name": "isonitroso_count", "dtype": "int64"}, {"name": "isopentyl_count", "dtype": "int64"}, {"name": "isopentylidene_count", "dtype": "int64"}, {"name": "isopropenyl_count", "dtype": "int64"}, {"name": "isopropoxy_count", "dtype": "int64"}, {"name": "isopropyl_count", "dtype": "int64"}, {"name": "isopropylidene_count", "dtype": "int64"}, {"name": "isothiocynato_count", "dtype": "int64"}, {"name": "isovaleryl_count", "dtype": "int64"}, {"name": "lactoyl_count", "dtype": "int64"}, {"name": "lauroyl_count", "dtype": "int64"}, {"name": "lauryl_count", "dtype": "int64"}, {"name": "leucyl_count", "dtype": "int64"}, {"name": "levulinoyl_count", "dtype": "int64"}, {"name": "malonyl_count", "dtype": "int64"}, {"name": "mandeloyl_count", "dtype": "int64"}, {"name": "mercapto_count", "dtype": "int64"}, {"name": "mesityl_count", "dtype": "int64"}, {"name": "methacryloyl_count", "dtype": "int64"}, {"name": "methallyl_count", "dtype": "int64"}, {"name": "methionyl_count", "dtype": "int64"}, {"name": "methoxy_count", "dtype": "int64"}, {"name": "methylene_count", "dtype": "int64"}, {"name": "methylthio_count", "dtype": "int64"}, {"name": "myristoyl_count", "dtype": "int64"}, {"name": "myristyl_count", "dtype": "int64"}, {"name": "naphthyl_count", "dtype": "int64"}, {"name": "naphthylene_count", "dtype": "int64"}, {"name": "neopentyl_count", "dtype": "int64"}, {"name": "nitramino_count", "dtype": "int64"}, {"name": "nitrosamino_count", "dtype": "int64"}, {"name": "nitroso_count", "dtype": "int64"}, {"name": "nonanoyl_count", "dtype": "int64"}, {"name": "oleoyl_count", "dtype": "int64"}, {"name": "oxalyl_count", "dtype": "int64"}, {"name": "oxo_count", "dtype": "int64"}, {"name": "palmitoyl_count", "dtype": "int64"}, {"name": "pentamethylene_count", "dtype": "int64"}, {"name": "pentyl_count", "dtype": "int64"}, {"name": "tert-pentyl_count", "dtype": "int64"}, {"name": "phenacylidene_count", "dtype": "int64"}, {"name": "phenethyl_count", "dtype": "int64"}, {"name": "phenoxy_count", "dtype": "int64"}, {"name": "phenyl_count", "dtype": "int64"}, {"name": "phenylene_count", "dtype": "int64"}, {"name": "phosphino_count", "dtype": "int64"}, {"name": "phosphinyl_count", "dtype": "int64"}, {"name": "phospho_count", "dtype": "int64"}, {"name": "phosphono_count", "dtype": "int64"}, {"name": "phthaloyl_count", "dtype": "int64"}, {"name": "picryl_count", "dtype": "int64"}, {"name": "pimeloyl_count", "dtype": "int64"}, {"name": "piperidino_count", "dtype": "int64"}, {"name": "pivaloyl_count", "dtype": "int64"}, {"name": "prenyl_count", "dtype": "int64"}, {"name": "propargyl_count", "dtype": "int64"}, {"name": "1-propenyl_count", "dtype": "int64"}, {"name": "2-propenyl_count", "dtype": "int64"}, {"name": "propionyl_count", "dtype": "int64"}, {"name": "propoxy_count", "dtype": "int64"}, {"name": "propyl_count", "dtype": "int64"}, {"name": "propylidene_count", "dtype": "int64"}, {"name": "pyrryl_count", "dtype": "int64"}, {"name": "salicyloyl_count", "dtype": "int64"}, {"name": "selenyl_count", "dtype": "int64"}, {"name": "seryl_count", "dtype": "int64"}, {"name": "siloxy_count", "dtype": "int64"}, {"name": "silyl_count", "dtype": "int64"}, {"name": "silyene_count", "dtype": "int64"}, {"name": "sorboyl_count", "dtype": "int64"}, {"name": "stearoyl_count", "dtype": "int64"}, {"name": "stearyl_count", "dtype": "int64"}, {"name": "styryl_count", "dtype": "int64"}, {"name": "suberoyl_count", "dtype": "int64"}, {"name": "succinyl_count", "dtype": "int64"}, {"name": "sulfamino_count", "dtype": "int64"}, {"name": "sulfamoyl_count", "dtype": "int64"}, {"name": "sulfanilyl_count", "dtype": "int64"}, {"name": "sulfeno_count", "dtype": "int64"}, {"name": "sulfhydryl_count", "dtype": "int64"}, {"name": "sulfinyl_count", "dtype": "int64"}, {"name": "sulfo_count", "dtype": "int64"}, {"name": "sulfonyl_count", "dtype": "int64"}, {"name": "terephthaloyl_count", "dtype": "int64"}, {"name": "tetramethylene_count", "dtype": "int64"}, {"name": "thienyl_count", "dtype": "int64"}, {"name": "thiocarbonyl_count", "dtype": "int64"}, {"name": "thiocarboxy_count", "dtype": "int64"}, {"name": "thiocyanato_count", "dtype": "int64"}, {"name": "thionyl_count", "dtype": "int64"}, {"name": "threonyl_count", "dtype": "int64"}, {"name": "toluidino_count", "dtype": "int64"}, {"name": "toluoyl_count", "dtype": "int64"}, {"name": "tolyl_count", "dtype": "int64"}, {"name": "alpha-tolyl_count", "dtype": "int64"}, {"name": "tolylene_count", "dtype": "int64"}, {"name": "tosyl_count", "dtype": "int64"}, {"name": "triazano_count", "dtype": "int64"}, {"name": "trimethylene_count", "dtype": "int64"}, {"name": "valeryl_count", "dtype": "int64"}, {"name": "valyl_count", "dtype": "int64"}, {"name": "vinyl_count", "dtype": "int64"}, {"name": "vinylidene_count", "dtype": "int64"}, {"name": "xylidino_count", "dtype": "int64"}, {"name": "xylyl_count", "dtype": "int64"}, {"name": "xylylene_count", "dtype": "int64"}, {"name": "propiolamide_count", "dtype": "int64"}, {"name": "fumarate ester_count", "dtype": "int64"}, {"name": "allenamide_count", "dtype": "int64"}, {"name": "propiolonitrile_count", "dtype": "int64"}, {"name": "propargylamide_count", "dtype": "int64"}, {"name": "arylsulfonyl bicyclobutane_count", "dtype": "int64"}, {"name": "haloalkane_count", "dtype": "int64"}, {"name": "alpha-halomethyl_count", "dtype": "int64"}, {"name": "alpha-haloamide_count", "dtype": "int64"}, {"name": "alpha-haloester_count", "dtype": "int64"}, {"name": "epoxide_count", "dtype": "int64"}, {"name": "aziridine_count", "dtype": "int64"}, {"name": "nitroalkane_count", "dtype": "int64"}, {"name": "acrylamide_count", "dtype": "int64"}, {"name": "cyanoenone_count", "dtype": "int64"}, {"name": "aldehyde_count", "dtype": "int64"}, {"name": "ketone_count", "dtype": "int64"}, {"name": "nitrile_count", "dtype": "int64"}, {"name": "cyanamide_count", "dtype": "int64"}, {"name": "isothicyanate_count", "dtype": "int64"}, {"name": "sulfone_count", "dtype": "int64"}, {"name": "sulfonyl fluoride_count", "dtype": "int64"}, {"name": "sulfonimidoyl fluoride_count", "dtype": "int64"}, {"name": "aryl fluorosulfate_count", "dtype": "int64"}, {"name": "ester_count", "dtype": "int64"}, {"name": "sulfonamide_count", "dtype": "int64"}, {"name": "2-carbonyl arylboronic acid_count", "dtype": "int64"}, {"name": "n-methyl isoxazolium_count", "dtype": "int64"}, {"name": "oxaziridine_count", "dtype": "int64"}, {"name": "carboxyl_count", "dtype": "int64"}, {"name": "ether_count", "dtype": "int64"}, {"name": "alkanol_count", "dtype": "int64"}, {"name": "thiol_count", "dtype": "int64"}, {"name": "halogen_count", "dtype": "int64"}, {"name": "amine_count", "dtype": "int64"}, {"name": "amide_count", "dtype": "int64"}, {"name": "num_valence_electrons", "dtype": "int64"}, {"name": "rotable_proportion", "dtype": "float64"}, {"name": "non_rotable_proportion", "dtype": "float64"}, {"name": "num_unspecified_bond", "dtype": "float64"}, {"name": "num_single_bonds", "dtype": "float64"}, {"name": "num_double_bonds", "dtype": "float64"}, {"name": "num_triple_bonds", "dtype": "float64"}, {"name": "num_quadruple_bonds", "dtype": "float64"}, {"name": "num_quintuple_bonds", "dtype": "float64"}, {"name": "num_hextuple_bonds", "dtype": "float64"}, {"name": "num_oneandahalf_bonds", "dtype": "float64"}, {"name": "num_twoandahalf_bonds", "dtype": "float64"}, {"name": "num_threeandahalf_bonds", "dtype": "float64"}, {"name": "num_fourandahalf_bonds", "dtype": "float64"}, {"name": "num_fiveandahalf_bonds", "dtype": "float64"}, {"name": "num_aromatic_bonds", "dtype": "float64"}, {"name": "num_ionic_bonds", "dtype": "float64"}, {"name": "num_hydrogen_bonds", "dtype": "float64"}, {"name": "num_threecenter_bonds", "dtype": "float64"}, {"name": "num_dativeone_bonds", "dtype": "float64"}, {"name": "num_dative_bonds", "dtype": "float64"}, {"name": "num_other_bonds", "dtype": "float64"}, {"name": "num_zero_bonds", "dtype": "float64"}, {"name": "num_bonds", "dtype": "int64"}, {"name": "molecular_formula", "dtype": "string"}, {"name": "monoisotopic_molecular_mass", "dtype": "float64"}, {"name": "carbon_mass", "dtype": "float64"}, {"name": "hydrogen_mass", "dtype": "float64"}, {"name": "nitrogen_mass", "dtype": "float64"}, {"name": "oxygen_mass", "dtype": "float64"}, {"name": "num_carbon_atoms", "dtype": "int64"}, {"name": "num_hydrogen_atoms", "dtype": "int64"}, {"name": "num_nitrogen_atoms", "dtype": "int64"}, {"name": "num_oxygen_atoms", "dtype": "int64"}, {"name": "num_hydrogen_bond_acceptors", "dtype": "int64"}, {"name": "num_hydrogen_bond_donors", "dtype": "int64"}, {"name": "num_lipinski_violations", "dtype": "int64"}, {"name": "inertial_shape_factor", "dtype": "float64"}, {"name": "eccentricity", "dtype": "float64"}, {"name": "asphericity", "dtype": "float64"}, {"name": "npr1_value", "dtype": "float64"}, {"name": "npr2_value", "dtype": "float64"}, {"name": "pmi1_value", "dtype": "float64"}, {"name": "pmi2_value", "dtype": "float64"}, {"name": "pmi3_value", "dtype": "float64"}, {"name": "num_chiral_centers", "dtype": "int64"}, {"name": "representation", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2737391, "num_examples": 495}], "download_size": 488441, "dataset_size": 2737391}, {"config_name": "smarts", "features": [{"name": "representation", "dtype": "string"}, {"name": "representation_type", "dtype": "string"}, {"name": "completion", "dtype": "int64"}, {"name": "completion_labels", "dtype": "string"}, {"name": "smarts", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 157359982, "num_examples": 812177}], "download_size": 24877597, "dataset_size": 157359982}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "rdkit_feat", "data_files": [{"split": "train", "path": "rdkit_feat/train-*"}]}, {"config_name": "rdkit_feat_0", "data_files": [{"split": "train", "path": "rdkit_feat_0/train-*"}]}, {"config_name": "rdkit_feat_1", "data_files": [{"split": "train", "path": "rdkit_feat_1/train-*"}]}, {"config_name": "rdkit_feat_10", "data_files": [{"split": "train", "path": "rdkit_feat_10/train-*"}]}, {"config_name": "rdkit_feat_11", "data_files": [{"split": "train", "path": "rdkit_feat_11/train-*"}]}, {"config_name": "rdkit_feat_2", "data_files": [{"split": "train", "path": "rdkit_feat_2/train-*"}]}, {"config_name": "rdkit_feat_3", "data_files": [{"split": "train", "path": "rdkit_feat_3/train-*"}]}, {"config_name": "rdkit_feat_4", "data_files": [{"split": "train", "path": "rdkit_feat_4/train-*"}]}, {"config_name": "rdkit_feat_5", "data_files": [{"split": "train", "path": "rdkit_feat_5/train-*"}]}, {"config_name": "rdkit_feat_6", "data_files": [{"split": "train", "path": "rdkit_feat_6/train-*"}]}, {"config_name": "rdkit_feat_7", "data_files": [{"split": "train", "path": "rdkit_feat_7/train-*"}]}, {"config_name": "rdkit_feat_8", "data_files": [{"split": "train", "path": "rdkit_feat_8/train-*"}]}, {"config_name": "smarts", "data_files": [{"split": "train", "path": "smarts/train-*"}]}]}
|
2023-11-07T20:41:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "chemnlp-chem-caption"
More Information needed
|
[
"# Dataset Card for \"chemnlp-chem-caption\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"chemnlp-chem-caption\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"chemnlp-chem-caption\"\n\nMore Information needed"
] |
62636a611ae6d7c55c8f02992a12a0855fe0ed7b
|
# Dataset Card for "sorted_generate_sub_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Jing24/sorted_generate_sub_0
|
[
"region:us"
] |
2023-10-24T19:59:37+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "conf", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 71874690, "num_examples": 78391}], "download_size": 13264944, "dataset_size": 71874690}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T19:59:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sorted_generate_sub_0"
More Information needed
|
[
"# Dataset Card for \"sorted_generate_sub_0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sorted_generate_sub_0\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sorted_generate_sub_0\"\n\nMore Information needed"
] |
625d83473e14bfa1be950a74e8d2e4dbf7b8a52e
|
# A High-Quality AI Augmented Dataset for RP and conversation
This dataset is comprised of lines from the Visual Novel Steins;Gate, which have been filtered, reformatted, AI-rewritten (many of them twice), and in a few cases, manually quality checked.
The flagship model of this dataset (a finetune on top of MythoMax) can be found [here](https://huggingface.co/Heralax/Augmental-13b)!
It contains a large number of RP-focused, multiturn conversational training examples, from the perspectives of multiple characters.
The "Scenario" column (AI-generated) describes the context behind the conversation in which a line takes place.
The "Completion" column (human-written, AI-enhanced), is the content of the line in that row
The "Speaker" column contains the name of the speaker of that line, and is one of ["Okabe", "Kurisu","Faris", "Luka","Itaru","Suzuha","Mayuri"]
The "History" column contains the lines that come before the completion, in a given conversation. There will always be at least one.
See the repo with the dataset generation code here https://github.com/e-p-armstrong/amadeus (train.py and make_card_evanchat.py) for an example of how these might be formatted for training.
# IMPORTANT NOTE
I'll be honest: I don't know where this dataset sits legally. On one hand, the source text was substantially revised over the course of costly weeks of experimentation; the source is not included in its entirety; and the source has been used by other AI creators before with the knowledge of the publisher of the source materia. Finally there's good precedent for using copyrighted work in AI training (CommonCrawl contains plenty of copyrighted work, for instance. Big companies use copyrighted data all the time).
On the other hand, this dataset is entirely composed of text that was generated using copyrighted text as an input.
This all being considered, I've left the license field of the model BLANK and will take the dataset down without any questions asked if the legal holders of the copyright ask me to do so. I hope they don't ask me to do so, because this is a project I'm really proud of, and I think it could be useful to the open source AI RP community. If someone from MAGES is reading this, then I ask them, as someone who's bought nearly every SciAdv game from C;H to A;C, please don't kill it. Thank you.
|
Heralax/Augmental-Dataset
|
[
"license:unknown",
"region:us"
] |
2023-10-24T20:11:30+00:00
|
{"license": "unknown"}
|
2023-12-20T22:09:53+00:00
|
[] |
[] |
TAGS
#license-unknown #region-us
|
# A High-Quality AI Augmented Dataset for RP and conversation
This dataset is comprised of lines from the Visual Novel Steins;Gate, which have been filtered, reformatted, AI-rewritten (many of them twice), and in a few cases, manually quality checked.
The flagship model of this dataset (a finetune on top of MythoMax) can be found here!
It contains a large number of RP-focused, multiturn conversational training examples, from the perspectives of multiple characters.
The "Scenario" column (AI-generated) describes the context behind the conversation in which a line takes place.
The "Completion" column (human-written, AI-enhanced), is the content of the line in that row
The "Speaker" column contains the name of the speaker of that line, and is one of ["Okabe", "Kurisu","Faris", "Luka","Itaru","Suzuha","Mayuri"]
The "History" column contains the lines that come before the completion, in a given conversation. There will always be at least one.
See the repo with the dataset generation code here URL (URL and make_card_evanchat.py) for an example of how these might be formatted for training.
# IMPORTANT NOTE
I'll be honest: I don't know where this dataset sits legally. On one hand, the source text was substantially revised over the course of costly weeks of experimentation; the source is not included in its entirety; and the source has been used by other AI creators before with the knowledge of the publisher of the source materia. Finally there's good precedent for using copyrighted work in AI training (CommonCrawl contains plenty of copyrighted work, for instance. Big companies use copyrighted data all the time).
On the other hand, this dataset is entirely composed of text that was generated using copyrighted text as an input.
This all being considered, I've left the license field of the model BLANK and will take the dataset down without any questions asked if the legal holders of the copyright ask me to do so. I hope they don't ask me to do so, because this is a project I'm really proud of, and I think it could be useful to the open source AI RP community. If someone from MAGES is reading this, then I ask them, as someone who's bought nearly every SciAdv game from C;H to A;C, please don't kill it. Thank you.
|
[
"# A High-Quality AI Augmented Dataset for RP and conversation\n\nThis dataset is comprised of lines from the Visual Novel Steins;Gate, which have been filtered, reformatted, AI-rewritten (many of them twice), and in a few cases, manually quality checked.\n\nThe flagship model of this dataset (a finetune on top of MythoMax) can be found here!\n\nIt contains a large number of RP-focused, multiturn conversational training examples, from the perspectives of multiple characters. \nThe \"Scenario\" column (AI-generated) describes the context behind the conversation in which a line takes place.\nThe \"Completion\" column (human-written, AI-enhanced), is the content of the line in that row\nThe \"Speaker\" column contains the name of the speaker of that line, and is one of [\"Okabe\", \"Kurisu\",\"Faris\", \"Luka\",\"Itaru\",\"Suzuha\",\"Mayuri\"]\nThe \"History\" column contains the lines that come before the completion, in a given conversation. There will always be at least one.\n\nSee the repo with the dataset generation code here URL (URL and make_card_evanchat.py) for an example of how these might be formatted for training.",
"# IMPORTANT NOTE\nI'll be honest: I don't know where this dataset sits legally. On one hand, the source text was substantially revised over the course of costly weeks of experimentation; the source is not included in its entirety; and the source has been used by other AI creators before with the knowledge of the publisher of the source materia. Finally there's good precedent for using copyrighted work in AI training (CommonCrawl contains plenty of copyrighted work, for instance. Big companies use copyrighted data all the time). \n\nOn the other hand, this dataset is entirely composed of text that was generated using copyrighted text as an input. \n\nThis all being considered, I've left the license field of the model BLANK and will take the dataset down without any questions asked if the legal holders of the copyright ask me to do so. I hope they don't ask me to do so, because this is a project I'm really proud of, and I think it could be useful to the open source AI RP community. If someone from MAGES is reading this, then I ask them, as someone who's bought nearly every SciAdv game from C;H to A;C, please don't kill it. Thank you."
] |
[
"TAGS\n#license-unknown #region-us \n",
"# A High-Quality AI Augmented Dataset for RP and conversation\n\nThis dataset is comprised of lines from the Visual Novel Steins;Gate, which have been filtered, reformatted, AI-rewritten (many of them twice), and in a few cases, manually quality checked.\n\nThe flagship model of this dataset (a finetune on top of MythoMax) can be found here!\n\nIt contains a large number of RP-focused, multiturn conversational training examples, from the perspectives of multiple characters. \nThe \"Scenario\" column (AI-generated) describes the context behind the conversation in which a line takes place.\nThe \"Completion\" column (human-written, AI-enhanced), is the content of the line in that row\nThe \"Speaker\" column contains the name of the speaker of that line, and is one of [\"Okabe\", \"Kurisu\",\"Faris\", \"Luka\",\"Itaru\",\"Suzuha\",\"Mayuri\"]\nThe \"History\" column contains the lines that come before the completion, in a given conversation. There will always be at least one.\n\nSee the repo with the dataset generation code here URL (URL and make_card_evanchat.py) for an example of how these might be formatted for training.",
"# IMPORTANT NOTE\nI'll be honest: I don't know where this dataset sits legally. On one hand, the source text was substantially revised over the course of costly weeks of experimentation; the source is not included in its entirety; and the source has been used by other AI creators before with the knowledge of the publisher of the source materia. Finally there's good precedent for using copyrighted work in AI training (CommonCrawl contains plenty of copyrighted work, for instance. Big companies use copyrighted data all the time). \n\nOn the other hand, this dataset is entirely composed of text that was generated using copyrighted text as an input. \n\nThis all being considered, I've left the license field of the model BLANK and will take the dataset down without any questions asked if the legal holders of the copyright ask me to do so. I hope they don't ask me to do so, because this is a project I'm really proud of, and I think it could be useful to the open source AI RP community. If someone from MAGES is reading this, then I ask them, as someone who's bought nearly every SciAdv game from C;H to A;C, please don't kill it. Thank you."
] |
[
13,
306,
275
] |
[
"passage: TAGS\n#license-unknown #region-us \n# A High-Quality AI Augmented Dataset for RP and conversation\n\nThis dataset is comprised of lines from the Visual Novel Steins;Gate, which have been filtered, reformatted, AI-rewritten (many of them twice), and in a few cases, manually quality checked.\n\nThe flagship model of this dataset (a finetune on top of MythoMax) can be found here!\n\nIt contains a large number of RP-focused, multiturn conversational training examples, from the perspectives of multiple characters. \nThe \"Scenario\" column (AI-generated) describes the context behind the conversation in which a line takes place.\nThe \"Completion\" column (human-written, AI-enhanced), is the content of the line in that row\nThe \"Speaker\" column contains the name of the speaker of that line, and is one of [\"Okabe\", \"Kurisu\",\"Faris\", \"Luka\",\"Itaru\",\"Suzuha\",\"Mayuri\"]\nThe \"History\" column contains the lines that come before the completion, in a given conversation. There will always be at least one.\n\nSee the repo with the dataset generation code here URL (URL and make_card_evanchat.py) for an example of how these might be formatted for training."
] |
b7197600841f5a9dab6f4ed8f7f53aadbe938043
|
# Dataset Card for "sort_low_all_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Jing24/sort_low_all_train
|
[
"region:us"
] |
2023-10-24T20:12:51+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 79661311, "num_examples": 87599}], "download_size": 46213808, "dataset_size": 79661311}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T20:12:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sort_low_all_train"
More Information needed
|
[
"# Dataset Card for \"sort_low_all_train\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sort_low_all_train\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sort_low_all_train\"\n\nMore Information needed"
] |
600e77dbfa3fef97da357273082483a78f0b9ee4
|
# Dataset Card for "TalkBank_CA_CABNC_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_CABNC_1
|
[
"region:us"
] |
2023-10-24T20:18:44+00:00
|
{"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17080612876, "num_examples": 100}], "download_size": 17100223923, "dataset_size": 17080612876}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T20:29:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_CABNC_1"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_CABNC_1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_CABNC_1\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_CABNC_1\"\n\nMore Information needed"
] |
652c1fd4167017e66cf18253d16f41035b55957c
|
# Dataset Card for "sort_high_all_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Jing24/sort_high_all_train
|
[
"region:us"
] |
2023-10-24T20:24:05+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 79676027, "num_examples": 87599}], "download_size": 32663100, "dataset_size": 79676027}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T20:24:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sort_high_all_train"
More Information needed
|
[
"# Dataset Card for \"sort_high_all_train\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sort_high_all_train\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sort_high_all_train\"\n\nMore Information needed"
] |
4c2088b9e58811ddd33c028bc7cc2bfefeda7a3c
|
# Dataset Card for "TalkBank_CA_CABNC_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_CABNC_2
|
[
"region:us"
] |
2023-10-24T20:37:39+00:00
|
{"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16314017575, "num_examples": 93}], "download_size": 16328534636, "dataset_size": 16314017575}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T20:48:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_CABNC_2"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_CABNC_2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_CABNC_2\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_CABNC_2\"\n\nMore Information needed"
] |
affcb0589e534220f190c84e5cbaa3d43f6d759d
|
# Dataset Card for "TalkBank_CA_CABNC_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_CABNC_3
|
[
"region:us"
] |
2023-10-24T20:49:14+00:00
|
{"dataset_info": {"features": [{"name": "__index_level_0__", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 0, "num_examples": 0}], "download_size": 582, "dataset_size": 0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T20:49:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_CABNC_3"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_CABNC_3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_CABNC_3\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_CABNC_3\"\n\nMore Information needed"
] |
1df6d0e17b11b1e8d53b17884932e9216172dbdb
|
# Dataset Card for "TalkBank_CA_CABNC_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_CABNC_4
|
[
"region:us"
] |
2023-10-24T20:50:49+00:00
|
{"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1619533958, "num_examples": 10}], "download_size": 1622055892, "dataset_size": 1619533958}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T20:51:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_CABNC_4"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_CABNC_4\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_CABNC_4\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_CABNC_4\"\n\nMore Information needed"
] |
a6b98f8ee7348716faafd8677c2a654137262dab
|
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
Collection of NC DOT Public Traffic Safety Cameras. Gathered to detect icy bridges for traffic safety.
This dataset card based upon [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
North Carolina Traffic Safety Cameras. Gathered to detect icy bridges for traffic safety. Weather data from openweathermaps.
- **Curated by:** John F. Davis [email protected]
- **Funded by [optional]:** John F. Davis
- **Shared by [optional]:** John F. Davis
- **Language(s) (NLP):** English
- **License:** Public Domain
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/rtp-gcp/gcp_icy_bridge
- **Paper [optional]:** none
- **Demo [optional]:** none
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
Used for an open source project to detect icy bridges using computer vision and weather data.
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
* classify images if they are usable
* usable/notusable
* lens flare
* lens moisture
* low light
* object identification
* bridge
* roadway
* sky
* grass
* car
* snow
* ice
* object detection
* same but with bounding boxes
* image segmentation
* pixels which constraint a particular item, say bridge surface or seam
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
* dogs
* cats
* don't know
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about
the dataset structure such as criteria used to create the splits, relationships between data points,
etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed]
|
netskink/hftesty
|
[
"region:us"
] |
2023-10-24T20:55:49+00:00
|
{}
|
2024-01-07T18:39:46+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Dataset Name
Collection of NC DOT Public Traffic Safety Cameras. Gathered to detect icy bridges for traffic safety.
This dataset card based upon this raw template.
## Dataset Details
### Dataset Description
North Carolina Traffic Safety Cameras. Gathered to detect icy bridges for traffic safety. Weather data from openweathermaps.
- Curated by: John F. Davis davisjf@URL
- Funded by [optional]: John F. Davis
- Shared by [optional]: John F. Davis
- Language(s) (NLP): English
- License: Public Domain
### Dataset Sources [optional]
- Repository: URL
- Paper [optional]: none
- Demo [optional]: none
## Uses
Used for an open source project to detect icy bridges using computer vision and weather data.
### Direct Use
* classify images if they are usable
* usable/notusable
* lens flare
* lens moisture
* low light
* object identification
* bridge
* roadway
* sky
* grass
* car
* snow
* ice
* object detection
* same but with bounding boxes
* image segmentation
* pixels which constraint a particular item, say bridge surface or seam
### Out-of-Scope Use
* dogs
* cats
* don't know
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
|
[
"# Dataset Card for Dataset Name\n\n\nCollection of NC DOT Public Traffic Safety Cameras. Gathered to detect icy bridges for traffic safety.\n\nThis dataset card based upon this raw template.",
"## Dataset Details",
"### Dataset Description\n\n\nNorth Carolina Traffic Safety Cameras. Gathered to detect icy bridges for traffic safety. Weather data from openweathermaps.\n\n\n\n\n- Curated by: John F. Davis davisjf@URL\n- Funded by [optional]: John F. Davis\n- Shared by [optional]: John F. Davis\n- Language(s) (NLP): English\n- License: Public Domain",
"### Dataset Sources [optional]\n\n\n\n- Repository: URL\n- Paper [optional]: none\n- Demo [optional]: none",
"## Uses\n\n\nUsed for an open source project to detect icy bridges using computer vision and weather data.",
"### Direct Use\n\n\n* classify images if they are usable\n * usable/notusable\n * lens flare\n * lens moisture\n * low light \n* object identification\n * bridge\n * roadway\n * sky\n * grass\n * car\n * snow\n * ice\n* object detection\n * same but with bounding boxes\n* image segmentation\n * pixels which constraint a particular item, say bridge surface or seam",
"### Out-of-Scope Use\n\n\n\n* dogs\n* cats\n* don't know",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Dataset Name\n\n\nCollection of NC DOT Public Traffic Safety Cameras. Gathered to detect icy bridges for traffic safety.\n\nThis dataset card based upon this raw template.",
"## Dataset Details",
"### Dataset Description\n\n\nNorth Carolina Traffic Safety Cameras. Gathered to detect icy bridges for traffic safety. Weather data from openweathermaps.\n\n\n\n\n- Curated by: John F. Davis davisjf@URL\n- Funded by [optional]: John F. Davis\n- Shared by [optional]: John F. Davis\n- Language(s) (NLP): English\n- License: Public Domain",
"### Dataset Sources [optional]\n\n\n\n- Repository: URL\n- Paper [optional]: none\n- Demo [optional]: none",
"## Uses\n\n\nUsed for an open source project to detect icy bridges using computer vision and weather data.",
"### Direct Use\n\n\n* classify images if they are usable\n * usable/notusable\n * lens flare\n * lens moisture\n * low light \n* object identification\n * bridge\n * roadway\n * sky\n * grass\n * car\n * snow\n * ice\n* object detection\n * same but with bounding boxes\n* image segmentation\n * pixels which constraint a particular item, say bridge surface or seam",
"### Out-of-Scope Use\n\n\n\n* dogs\n* cats\n* don't know",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Dataset Name\n\n\nCollection of NC DOT Public Traffic Safety Cameras. Gathered to detect icy bridges for traffic safety.\n\nThis dataset card based upon this raw template.## Dataset Details### Dataset Description\n\n\nNorth Carolina Traffic Safety Cameras. Gathered to detect icy bridges for traffic safety. Weather data from openweathermaps.\n\n\n\n\n- Curated by: John F. Davis davisjf@URL\n- Funded by [optional]: John F. Davis\n- Shared by [optional]: John F. Davis\n- Language(s) (NLP): English\n- License: Public Domain### Dataset Sources [optional]\n\n\n\n- Repository: URL\n- Paper [optional]: none\n- Demo [optional]: none## Uses\n\n\nUsed for an open source project to detect icy bridges using computer vision and weather data.### Direct Use\n\n\n* classify images if they are usable\n * usable/notusable\n * lens flare\n * lens moisture\n * low light \n* object identification\n * bridge\n * roadway\n * sky\n * grass\n * car\n * snow\n * ice\n* object detection\n * same but with bounding boxes\n* image segmentation\n * pixels which constraint a particular item, say bridge surface or seam### Out-of-Scope Use\n\n\n\n* dogs\n* cats\n* don't know## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
] |
663d45e44ed50d84baee457bf4d698b9c0341442
|
# Dataset Card for "TalkBank_CA_CABNC_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_CABNC_5
|
[
"region:us"
] |
2023-10-24T21:06:29+00:00
|
{"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17917023985, "num_examples": 99}], "download_size": 17934314304, "dataset_size": 17917023985}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T21:18:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_CABNC_5"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_CABNC_5\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_CABNC_5\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_CABNC_5\"\n\nMore Information needed"
] |
c5268c868a0e71566f3c00ceeae81a2e6c752261
|
# Robot Demonstrations with Environment States
Dataset of robot demonstrations for various benchmarks formatted to contain environment states following the ManiSkill2 trajectory format: https://haosulab.github.io/ManiSkill2/concepts/demonstrations.html#format
Benchmarks supported:
- ManiSkill2
- Adroit (D4RL)
- Metaworld
For code to load and reset to states, as well as formatting the origianl demonstration datasets in supported benchmarks, see this repo: https://github.com/stonet2000/robot-state-reset-utils.
Data originally used for and generated from the [Reverse Forward Curriculum Learning for Extreme Sample and Demo Efficiency]() research project.
|
stonet2000/robot_demos_with_state_reset
|
[
"task_categories:robotics",
"task_categories:reinforcement-learning",
"language:en",
"license:mit",
"Robotics",
"Embodied AI",
"Imitation Learning",
"Learning from Demonstrations",
"State Reset",
"Simulation",
"region:us"
] |
2023-10-24T21:06:59+00:00
|
{"language": ["en"], "license": "mit", "task_categories": ["robotics", "reinforcement-learning"], "tags": ["Robotics", "Embodied AI", "Imitation Learning", "Learning from Demonstrations", "State Reset", "Simulation"]}
|
2023-10-24T21:20:34+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-robotics #task_categories-reinforcement-learning #language-English #license-mit #Robotics #Embodied AI #Imitation Learning #Learning from Demonstrations #State Reset #Simulation #region-us
|
# Robot Demonstrations with Environment States
Dataset of robot demonstrations for various benchmarks formatted to contain environment states following the ManiSkill2 trajectory format: URL
Benchmarks supported:
- ManiSkill2
- Adroit (D4RL)
- Metaworld
For code to load and reset to states, as well as formatting the origianl demonstration datasets in supported benchmarks, see this repo: URL
Data originally used for and generated from the [Reverse Forward Curriculum Learning for Extreme Sample and Demo Efficiency]() research project.
|
[
"# Robot Demonstrations with Environment States\n\nDataset of robot demonstrations for various benchmarks formatted to contain environment states following the ManiSkill2 trajectory format: URL\n\nBenchmarks supported:\n- ManiSkill2\n- Adroit (D4RL)\n- Metaworld\n\nFor code to load and reset to states, as well as formatting the origianl demonstration datasets in supported benchmarks, see this repo: URL\n\n\nData originally used for and generated from the [Reverse Forward Curriculum Learning for Extreme Sample and Demo Efficiency]() research project."
] |
[
"TAGS\n#task_categories-robotics #task_categories-reinforcement-learning #language-English #license-mit #Robotics #Embodied AI #Imitation Learning #Learning from Demonstrations #State Reset #Simulation #region-us \n",
"# Robot Demonstrations with Environment States\n\nDataset of robot demonstrations for various benchmarks formatted to contain environment states following the ManiSkill2 trajectory format: URL\n\nBenchmarks supported:\n- ManiSkill2\n- Adroit (D4RL)\n- Metaworld\n\nFor code to load and reset to states, as well as formatting the origianl demonstration datasets in supported benchmarks, see this repo: URL\n\n\nData originally used for and generated from the [Reverse Forward Curriculum Learning for Extreme Sample and Demo Efficiency]() research project."
] |
[
64,
130
] |
[
"passage: TAGS\n#task_categories-robotics #task_categories-reinforcement-learning #language-English #license-mit #Robotics #Embodied AI #Imitation Learning #Learning from Demonstrations #State Reset #Simulation #region-us \n# Robot Demonstrations with Environment States\n\nDataset of robot demonstrations for various benchmarks formatted to contain environment states following the ManiSkill2 trajectory format: URL\n\nBenchmarks supported:\n- ManiSkill2\n- Adroit (D4RL)\n- Metaworld\n\nFor code to load and reset to states, as well as formatting the origianl demonstration datasets in supported benchmarks, see this repo: URL\n\n\nData originally used for and generated from the [Reverse Forward Curriculum Learning for Extreme Sample and Demo Efficiency]() research project."
] |
4a14a0a67b1ce54e4a247bb0bde6d68b6d1fa87a
|
# AutoTrain Dataset for project: dsadsad
## Dataset Description
This dataset has been automatically processed by AutoTrain for project dsadsad.
### 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
[
{
"context": "Adjustment Card Transaction\nAccount XXXX9820\nCard XXXX5713\nCNY 896.2\nAmazon Sydney Australia\n25/09/23 18:30\nAvailable Balance CNY 249385.43",
"question": "What is the transaction currency?",
"answers.text": [
"CNY"
],
"answers.answer_start": [
59
]
},
{
"context": "Withdrawal Card Transaction\nAccount XXXX4602\nCard XXXX9502\nINR 59.17\nWalmart Tokyo Japan\n09/12/22 08:30\nAvailable Balance INR 92357.83",
"question": "What is the Merchant Name?",
"answers.text": [
"Walmart"
],
"answers.answer_start": [
69
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"context": "Value(dtype='string', id=None)",
"question": "Value(dtype='string', id=None)",
"answers.text": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"answers.answer_start": "Sequence(feature=Value(dtype='int32', id=None), length=-1, 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 | 2400 |
| valid | 600 |
|
sidaga123/autotrain-data-dsadsad
|
[
"region:us"
] |
2023-10-24T22:02:14+00:00
|
{}
|
2023-10-24T22:02:41+00:00
|
[] |
[] |
TAGS
#region-us
|
AutoTrain Dataset for project: dsadsad
======================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project dsadsad.
### 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:"
] |
c5973eb9e314bd4abe7ac993e6fcecc558316863
|
# Dataset Card for Evaluation run of shaohang/SparseOPT-1.3B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/shaohang/SparseOPT-1.3B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [shaohang/SparseOPT-1.3B](https://huggingface.co/shaohang/SparseOPT-1.3B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_shaohang__SparseOPT-1.3B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T23:18:41.395628](https://huggingface.co/datasets/open-llm-leaderboard/details_shaohang__SparseOPT-1.3B/blob/main/results_2023-10-24T23-18-41.395628.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.003145973154362416,
"em_stderr": 0.0005734993648436396,
"f1": 0.048264471476510196,
"f1_stderr": 0.0013025272598396927,
"acc": 0.29319675461487227,
"acc_stderr": 0.007301498172995543
},
"harness|drop|3": {
"em": 0.003145973154362416,
"em_stderr": 0.0005734993648436396,
"f1": 0.048264471476510196,
"f1_stderr": 0.0013025272598396927
},
"harness|gsm8k|5": {
"acc": 0.000758150113722517,
"acc_stderr": 0.0007581501137225237
},
"harness|winogrande|5": {
"acc": 0.585635359116022,
"acc_stderr": 0.013844846232268563
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_shaohang__SparseOPT-1.3B
|
[
"region:us"
] |
2023-10-24T22:18:45+00:00
|
{"pretty_name": "Evaluation run of shaohang/SparseOPT-1.3B", "dataset_summary": "Dataset automatically created during the evaluation run of model [shaohang/SparseOPT-1.3B](https://huggingface.co/shaohang/SparseOPT-1.3B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_shaohang__SparseOPT-1.3B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T23:18:41.395628](https://huggingface.co/datasets/open-llm-leaderboard/details_shaohang__SparseOPT-1.3B/blob/main/results_2023-10-24T23-18-41.395628.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.003145973154362416,\n \"em_stderr\": 0.0005734993648436396,\n \"f1\": 0.048264471476510196,\n \"f1_stderr\": 0.0013025272598396927,\n \"acc\": 0.29319675461487227,\n \"acc_stderr\": 0.007301498172995543\n },\n \"harness|drop|3\": {\n \"em\": 0.003145973154362416,\n \"em_stderr\": 0.0005734993648436396,\n \"f1\": 0.048264471476510196,\n \"f1_stderr\": 0.0013025272598396927\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \"acc_stderr\": 0.0007581501137225237\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.585635359116022,\n \"acc_stderr\": 0.013844846232268563\n }\n}\n```", "repo_url": "https://huggingface.co/shaohang/SparseOPT-1.3B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T23_18_41.395628", "path": ["**/details_harness|drop|3_2023-10-24T23-18-41.395628.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-24T23-18-41.395628.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T23_18_41.395628", "path": ["**/details_harness|gsm8k|5_2023-10-24T23-18-41.395628.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-24T23-18-41.395628.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_24T23_18_41.395628", "path": ["**/details_harness|winogrande|5_2023-10-24T23-18-41.395628.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-24T23-18-41.395628.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_24T23_18_41.395628", "path": ["results_2023-10-24T23-18-41.395628.parquet"]}, {"split": "latest", "path": ["results_2023-10-24T23-18-41.395628.parquet"]}]}]}
|
2023-10-24T22:18:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of shaohang/SparseOPT-1.3B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model shaohang/SparseOPT-1.3B on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-24T23:18:41.395628(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of shaohang/SparseOPT-1.3B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model shaohang/SparseOPT-1.3B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T23:18:41.395628(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of shaohang/SparseOPT-1.3B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model shaohang/SparseOPT-1.3B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T23:18:41.395628(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
20,
31,
168,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of shaohang/SparseOPT-1.3B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model shaohang/SparseOPT-1.3B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T23:18:41.395628(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
de626f0ba03fc36bbd375890b0c69ca2674eb30e
|
# AutoTrain Dataset for project: fdsfds
## Dataset Description
This dataset has been automatically processed by AutoTrain for project fdsfds.
### 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
[
{
"context": "Levy Card Transaction\nAccount XXXX3724\nCard XXXX6333\nRUB 950.9\nMcDonald's Fast Food Berlin Germany\n16/02/23 16:26\nAvailable Balance RUB 265123.86",
"question": "What is the transaction type?",
"answers.text": [
"Debit"
],
"answers.answer_start": [
0
]
},
{
"context": "Purchase Card Transaction\nAccount XXXX8364\nCard XXXX8220\nMYR 221.08\nAmazon Online Marketplace Tokyo Japan\n24/11/22 00:51\nAvailable Balance MYR 386077.5",
"question": "What is the Merchant Name?",
"answers.text": [
"Amazon"
],
"answers.answer_start": [
68
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"context": "Value(dtype='string', id=None)",
"question": "Value(dtype='string', id=None)",
"answers.text": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"answers.answer_start": "Sequence(feature=Value(dtype='int32', id=None), length=-1, 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 | 2240 |
| valid | 560 |
|
sidaga123/autotrain-data-fdsfds
|
[
"region:us"
] |
2023-10-24T22:19:19+00:00
|
{}
|
2023-10-24T22:22:08+00:00
|
[] |
[] |
TAGS
#region-us
|
AutoTrain Dataset for project: fdsfds
=====================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project fdsfds.
### 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:"
] |
be9d4d7f6a533163b190af0d683edb981b149ffb
|
# Dataset Card for Evaluation run of csitfun/llama-7b-logicot
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/csitfun/llama-7b-logicot
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [csitfun/llama-7b-logicot](https://huggingface.co/csitfun/llama-7b-logicot) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_csitfun__llama-7b-logicot",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T23:32:58.123828](https://huggingface.co/datasets/open-llm-leaderboard/details_csitfun__llama-7b-logicot/blob/main/results_2023-10-24T23-32-58.123828.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0020973154362416107,
"em_stderr": 0.0004685065030368161,
"f1": 0.05921245805369133,
"f1_stderr": 0.0013293292378975478,
"acc": 0.3378058405682715,
"acc_stderr": 0.006578612863320816
},
"harness|drop|3": {
"em": 0.0020973154362416107,
"em_stderr": 0.0004685065030368161,
"f1": 0.05921245805369133,
"f1_stderr": 0.0013293292378975478
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.675611681136543,
"acc_stderr": 0.013157225726641632
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_csitfun__llama-7b-logicot
|
[
"region:us"
] |
2023-10-24T22:33:02+00:00
|
{"pretty_name": "Evaluation run of csitfun/llama-7b-logicot", "dataset_summary": "Dataset automatically created during the evaluation run of model [csitfun/llama-7b-logicot](https://huggingface.co/csitfun/llama-7b-logicot) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_csitfun__llama-7b-logicot\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T23:32:58.123828](https://huggingface.co/datasets/open-llm-leaderboard/details_csitfun__llama-7b-logicot/blob/main/results_2023-10-24T23-32-58.123828.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0020973154362416107,\n \"em_stderr\": 0.0004685065030368161,\n \"f1\": 0.05921245805369133,\n \"f1_stderr\": 0.0013293292378975478,\n \"acc\": 0.3378058405682715,\n \"acc_stderr\": 0.006578612863320816\n },\n \"harness|drop|3\": {\n \"em\": 0.0020973154362416107,\n \"em_stderr\": 0.0004685065030368161,\n \"f1\": 0.05921245805369133,\n \"f1_stderr\": 0.0013293292378975478\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.675611681136543,\n \"acc_stderr\": 0.013157225726641632\n }\n}\n```", "repo_url": "https://huggingface.co/csitfun/llama-7b-logicot", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T23_32_58.123828", "path": ["**/details_harness|drop|3_2023-10-24T23-32-58.123828.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-24T23-32-58.123828.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T23_32_58.123828", "path": ["**/details_harness|gsm8k|5_2023-10-24T23-32-58.123828.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-24T23-32-58.123828.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_24T23_32_58.123828", "path": ["**/details_harness|winogrande|5_2023-10-24T23-32-58.123828.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-24T23-32-58.123828.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_24T23_32_58.123828", "path": ["results_2023-10-24T23-32-58.123828.parquet"]}, {"split": "latest", "path": ["results_2023-10-24T23-32-58.123828.parquet"]}]}]}
|
2023-10-24T22:33:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of csitfun/llama-7b-logicot
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model csitfun/llama-7b-logicot on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-24T23:32:58.123828(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of csitfun/llama-7b-logicot",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model csitfun/llama-7b-logicot on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T23:32:58.123828(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"# Dataset Card for Evaluation run of csitfun/llama-7b-logicot",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model csitfun/llama-7b-logicot on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T23:32:58.123828(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
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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",
"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of csitfun/llama-7b-logicot## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model csitfun/llama-7b-logicot on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T23:32:58.123828(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
18b2c5519e78c70379688d168d1e9d0b524968f4
|
# Dataset Card for Evaluation run of HuggingFaceH4/zephyr-7b-alpha
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [HuggingFaceH4/zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-alpha",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T23:49:05.604326](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-alpha/blob/main/results_2023-10-24T23-49-05.604326.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.010171979865771811,
"em_stderr": 0.001027595678252703,
"f1": 0.0982225251677855,
"f1_stderr": 0.00197861649884212,
"acc": 0.46318334487213475,
"acc_stderr": 0.010544787617759458
},
"harness|drop|3": {
"em": 0.010171979865771811,
"em_stderr": 0.001027595678252703,
"f1": 0.0982225251677855,
"f1_stderr": 0.00197861649884212
},
"harness|gsm8k|5": {
"acc": 0.14025777103866566,
"acc_stderr": 0.00956510828142867
},
"harness|winogrande|5": {
"acc": 0.7861089187056038,
"acc_stderr": 0.011524466954090248
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-alpha
|
[
"region:us"
] |
2023-10-24T22:49:09+00:00
|
{"pretty_name": "Evaluation run of HuggingFaceH4/zephyr-7b-alpha", "dataset_summary": "Dataset automatically created during the evaluation run of model [HuggingFaceH4/zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-alpha\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T23:49:05.604326](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-alpha/blob/main/results_2023-10-24T23-49-05.604326.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.010171979865771811,\n \"em_stderr\": 0.001027595678252703,\n \"f1\": 0.0982225251677855,\n \"f1_stderr\": 0.00197861649884212,\n \"acc\": 0.46318334487213475,\n \"acc_stderr\": 0.010544787617759458\n },\n \"harness|drop|3\": {\n \"em\": 0.010171979865771811,\n \"em_stderr\": 0.001027595678252703,\n \"f1\": 0.0982225251677855,\n \"f1_stderr\": 0.00197861649884212\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.14025777103866566,\n \"acc_stderr\": 0.00956510828142867\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7861089187056038,\n \"acc_stderr\": 0.011524466954090248\n }\n}\n```", "repo_url": "https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T23_49_05.604326", "path": ["**/details_harness|drop|3_2023-10-24T23-49-05.604326.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-24T23-49-05.604326.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T23_49_05.604326", "path": ["**/details_harness|gsm8k|5_2023-10-24T23-49-05.604326.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-24T23-49-05.604326.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_24T23_49_05.604326", "path": ["**/details_harness|winogrande|5_2023-10-24T23-49-05.604326.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-24T23-49-05.604326.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_24T23_49_05.604326", "path": ["results_2023-10-24T23-49-05.604326.parquet"]}, {"split": "latest", "path": ["results_2023-10-24T23-49-05.604326.parquet"]}]}]}
|
2023-10-24T22:49:17+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of HuggingFaceH4/zephyr-7b-alpha
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model HuggingFaceH4/zephyr-7b-alpha on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-24T23:49:05.604326(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of HuggingFaceH4/zephyr-7b-alpha",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model HuggingFaceH4/zephyr-7b-alpha on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T23:49:05.604326(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of HuggingFaceH4/zephyr-7b-alpha",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model HuggingFaceH4/zephyr-7b-alpha on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T23:49:05.604326(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of HuggingFaceH4/zephyr-7b-alpha## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model HuggingFaceH4/zephyr-7b-alpha on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T23:49:05.604326(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
e1322b8afb827591972c86776003040a55692750
|
# Dataset Card for "ds_by_sys_prompt_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_0
|
[
"region:us"
] |
2023-10-24T22:55:51+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 792945192.7634923, "num_examples": 464912}], "download_size": 449699774, "dataset_size": 792945192.7634923}}
|
2023-10-24T22:56:46+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_0"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_0\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_0\"\n\nMore Information needed"
] |
fe9b5217e92b563217d6e0e4b60c5ee4d7876c67
|
# Dataset Card for "ds_by_sys_prompt_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_1
|
[
"region:us"
] |
2023-10-24T22:57:48+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 56262008.882733345, "num_examples": 32987}], "download_size": 27809798, "dataset_size": 56262008.882733345}}
|
2023-10-24T22:57:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_1"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_1\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_1\"\n\nMore Information needed"
] |
9d427fc99ae998a75fd14adcbf3da4ea2e3427bc
|
# Dataset Card for "ds_by_sys_prompt_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_2
|
[
"region:us"
] |
2023-10-24T22:58:42+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 787059231.8499715, "num_examples": 461461}], "download_size": 456893655, "dataset_size": 787059231.8499715}}
|
2023-10-24T22:59:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_2"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_2\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_2\"\n\nMore Information needed"
] |
090b8ccbff50846b9fa526b2e22e3bbb8df38c4a
|
# Dataset Card for "ds_by_sys_prompt_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_3
|
[
"region:us"
] |
2023-10-24T23:00:26+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 56945946.966280684, "num_examples": 33388}], "download_size": 30077160, "dataset_size": 56945946.966280684}}
|
2023-10-24T23:00:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_3"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_3\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_3\"\n\nMore Information needed"
] |
aba18d059e9bb1d126eb6302161040e6436364dc
|
# Dataset Card for "ds_by_sys_prompt_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_4
|
[
"region:us"
] |
2023-10-24T23:01:23+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 55803207.524942234, "num_examples": 32718}], "download_size": 24839940, "dataset_size": 55803207.524942234}}
|
2023-10-24T23:01:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_4"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_4\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_4\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_4\"\n\nMore Information needed"
] |
6c712e8c24e02fb40cad9e61fe874cdd2c746808
|
# Dataset Card for "ds_by_sys_prompt_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_5
|
[
"region:us"
] |
2023-10-24T23:02:19+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1646317423.8362083, "num_examples": 965253}], "download_size": 935877105, "dataset_size": 1646317423.8362083}}
|
2023-10-24T23:04:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_5"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_5\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_5\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_5\"\n\nMore Information needed"
] |
a1ae192ad78fe46aefc12245144ff7f2d8e690b5
|
Pederinhonovo.zip
|
Eididkd/Pedrinhonovo.zip
|
[
"region:us"
] |
2023-10-24T23:04:39+00:00
|
{}
|
2023-10-25T02:18:09+00:00
|
[] |
[] |
TAGS
#region-us
|
URL
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
fc9c85d3214fae6d430d9f6182493f1d0e4686d8
|
# Dataset Card for "ds_by_sys_prompt_6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_6
|
[
"region:us"
] |
2023-10-24T23:05:11+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 885194966.1421082, "num_examples": 518999}], "download_size": 354133820, "dataset_size": 885194966.1421082}}
|
2023-10-24T23:06:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_6"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_6\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_6\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_6\"\n\nMore Information needed"
] |
f2a686aa629839183fa191aae1b5545d1be2daea
|
# Dataset Card for "ds_by_sys_prompt_7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_7
|
[
"region:us"
] |
2023-10-24T23:06:54+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 80587008.7519407, "num_examples": 47249}], "download_size": 24293019, "dataset_size": 80587008.7519407}}
|
2023-10-24T23:07:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_7"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_7\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_7\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_7\"\n\nMore Information needed"
] |
1ff2c198d0787bb4f4bc8c412b27f47a5d893537
|
# Dataset Card for "ds_by_sys_prompt_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_8
|
[
"region:us"
] |
2023-10-24T23:08:05+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 55085157.81628031, "num_examples": 32297}], "download_size": 21824628, "dataset_size": 55085157.81628031}}
|
2023-10-24T23:08:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_8"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_8\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_8\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_8\"\n\nMore Information needed"
] |
5c951cce1c86a9ef1b6c58ce859092fe7c6d4ad5
|
# Dataset Card for "ds_by_sys_prompt_9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_9
|
[
"region:us"
] |
2023-10-24T23:09:15+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 800642480.9705975, "num_examples": 469425}], "download_size": 515492123, "dataset_size": 800642480.9705975}}
|
2023-10-24T23:10:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_9"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_9\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_9\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_9\"\n\nMore Information needed"
] |
4e4d47c9d23f224b70f6347b8d484d099305d1d5
|
# Dataset Card for "ds_by_sys_prompt_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_10
|
[
"region:us"
] |
2023-10-24T23:11:02+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 80897424.54048337, "num_examples": 47431}], "download_size": 24051168, "dataset_size": 80897424.54048337}}
|
2023-10-24T23:11:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_10"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_10\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_10\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_10\"\n\nMore Information needed"
] |
e828960434c1985a540d79231f074e8a272aab29
|
# Dataset Card for "ds_by_sys_prompt_11"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_11
|
[
"region:us"
] |
2023-10-24T23:11:56+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 80187902.7381001, "num_examples": 47015}], "download_size": 17360728, "dataset_size": 80187902.7381001}}
|
2023-10-24T23:12:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_11"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_11\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_11\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_11\"\n\nMore Information needed"
] |
2bb2fe78b3ad1dec5ce03185dd1f77cb39341ece
|
# Amazon Polarity 10pct
This is a direct subset of the original [Amazon Polarity](https://huggingface.co/datasets/amazon_polarity) dataset, downsampled 10pct with a random shuffle
### Dataset Summary
For quicker testing on Amazon Polarity. See https://huggingface.co/datasets/amazon_polarity for details and attributions
### Source Data
```python
from datasets import ClassLabel, Dataset, DatasetDict, load_dataset
ds_full = load_dataset("amazon_polarity", streaming=True)
ds_train_10_pct = Dataset.from_list(list(ds_full["train"].shuffle(seed=42).take(360_000)))
ds_test_10_pct = Dataset.from_list(list(ds_full["test"].shuffle(seed=42).take(40_000)))
ds_10_pct = DatasetDict({"train": ds_train_10_pct, "test": ds_test_10_pct})
# Need to recreate the class labels
class_label = ClassLabel(num_classes=2, names=["negative", "positive"])
ds_10_pct = ds_10_pct.map(lambda row: {"title": row["title"], "content": row["content"], "label": "negative" if not row["label"] else "positive"})
ds_10_pct = ds_10_pct.cast_column("label", class_label)
```
|
Domino-ai/amazon_polarity_10_pct
|
[
"region:us"
] |
2023-10-24T23:12:18+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "negative", "1": "positive"}}}}, {"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 163359702, "num_examples": 360000}, {"name": "test", "num_bytes": 18182813, "num_examples": 40000}], "download_size": 120691417, "dataset_size": 181542515}}
|
2023-10-24T23:14:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Amazon Polarity 10pct
This is a direct subset of the original Amazon Polarity dataset, downsampled 10pct with a random shuffle
### Dataset Summary
For quicker testing on Amazon Polarity. See URL for details and attributions
### Source Data
|
[
"# Amazon Polarity 10pct\n\nThis is a direct subset of the original Amazon Polarity dataset, downsampled 10pct with a random shuffle",
"### Dataset Summary\n\nFor quicker testing on Amazon Polarity. See URL for details and attributions",
"### Source Data"
] |
[
"TAGS\n#region-us \n",
"# Amazon Polarity 10pct\n\nThis is a direct subset of the original Amazon Polarity dataset, downsampled 10pct with a random shuffle",
"### Dataset Summary\n\nFor quicker testing on Amazon Polarity. See URL for details and attributions",
"### Source Data"
] |
[
6,
33,
22,
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[
"passage: TAGS\n#region-us \n# Amazon Polarity 10pct\n\nThis is a direct subset of the original Amazon Polarity dataset, downsampled 10pct with a random shuffle### Dataset Summary\n\nFor quicker testing on Amazon Polarity. See URL for details and attributions### Source Data"
] |
49d8c98576be0d4c151fb42f3db4a36eaeae11af
|
# Dataset Card for "ds_by_sys_prompt_12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_12
|
[
"region:us"
] |
2023-10-24T23:12:52+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 76027990.055377, "num_examples": 44576}], "download_size": 26594526, "dataset_size": 76027990.055377}}
|
2023-10-24T23:12:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_12"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_12\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_12\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_12\"\n\nMore Information needed"
] |
368612292711d4ee1fdd5208f5010f884d1e9d48
|
# Dataset Card for "ds_by_sys_prompt_13"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_13
|
[
"region:us"
] |
2023-10-24T23:13:52+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 75900071.46119733, "num_examples": 44501}], "download_size": 24694465, "dataset_size": 75900071.46119733}}
|
2023-10-24T23:13:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_13"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_13\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_13\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_13\"\n\nMore Information needed"
] |
c81e1835102754f147949655429cdabb98ba70fb
|
# Dataset Card for "ds_by_sys_prompt_14"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_14
|
[
"region:us"
] |
2023-10-24T23:14:47+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 55078335.4912574, "num_examples": 32293}], "download_size": 27776587, "dataset_size": 55078335.4912574}}
|
2023-10-24T23:14:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_14"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_14\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_14\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_14\"\n\nMore Information needed"
] |
ef6d53747836184640db31702d560527ef0868b9
|
# Dataset Card for "ds_by_sys_prompt_15"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_15
|
[
"region:us"
] |
2023-10-24T23:15:43+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1636232321.8710828, "num_examples": 959340}], "download_size": 1139633886, "dataset_size": 1636232321.8710828}}
|
2023-10-24T23:17:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_15"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_15\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_15\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_15\"\n\nMore Information needed"
] |
125fcefe6ff83bf6c8176bcfd79c7ad3cd3a2908
|
# Dataset Card for "ds_by_sys_prompt_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamnguyen/ds_by_sys_prompt_16
|
[
"region:us"
] |
2023-10-24T23:18:46+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 133035.33794686393, "num_examples": 78}], "download_size": 89536, "dataset_size": 133035.33794686393}}
|
2023-10-24T23:18:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ds_by_sys_prompt_16"
More Information needed
|
[
"# Dataset Card for \"ds_by_sys_prompt_16\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ds_by_sys_prompt_16\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ds_by_sys_prompt_16\"\n\nMore Information needed"
] |
51c8863e53fc39edbacc8d23d279a27e2beb9dfb
|
# Dataset Card for "TalkBank_CA_wM_TaiwanHakka"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_wM_TaiwanHakka
|
[
"region:us"
] |
2023-10-24T23:29:53+00:00
|
{"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2411964909, "num_examples": 44}], "download_size": 2413211183, "dataset_size": 2411964909}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T23:34:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_wM_TaiwanHakka"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_wM_TaiwanHakka\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_wM_TaiwanHakka\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_wM_TaiwanHakka\"\n\nMore Information needed"
] |
f81f23daa3d7f65c11a5a97839a98d21e47bb113
|
# Dataset Card for "TalkBank_CA_wM_SBCSAE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_wM_SBCSAE
|
[
"region:us"
] |
2023-10-24T23:39:20+00:00
|
{"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5375202658, "num_examples": 60}], "download_size": 5380925506, "dataset_size": 5375202658}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T23:42:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_wM_SBCSAE"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_wM_SBCSAE\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_wM_SBCSAE\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_wM_SBCSAE\"\n\nMore Information needed"
] |
80e345206ef79956dc4f7f808fce64aa00197f5e
|
# Dataset Card for "TalkBank_CA_wM_GCSAusE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_wM_GCSAusE
|
[
"region:us"
] |
2023-10-24T23:43:55+00:00
|
{"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 832830161, "num_examples": 36}], "download_size": 833725190, "dataset_size": 832830161}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T23:44:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_wM_GCSAusE"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_wM_GCSAusE\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_wM_GCSAusE\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_wM_GCSAusE\"\n\nMore Information needed"
] |
764c1977f760ded17686ba5e50b399802cea337e
|
# Dataset Card for "TalkBank_CA_wM_Croatian"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_wM_Croatian
|
[
"region:us"
] |
2023-10-24T23:51:40+00:00
|
{"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5830164732, "num_examples": 135}], "download_size": 5836483668, "dataset_size": 5830164732}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T23:54:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_wM_Croatian"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_wM_Croatian\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_wM_Croatian\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_wM_Croatian\"\n\nMore Information needed"
] |
e2468aee990a524577f1c6d7d92068e51c9d1a50
|
# Dataset Card for "TalkBank_CA_wM_Bergmann"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_wM_Bergmann
|
[
"region:us"
] |
2023-10-24T23:56:58+00:00
|
{"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 170307012, "num_examples": 83}], "download_size": 169370475, "dataset_size": 170307012}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T23:57:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_wM_Bergmann"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_wM_Bergmann\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_wM_Bergmann\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_wM_Bergmann\"\n\nMore Information needed"
] |
c710bfcc4a4189eb94f81bfae69cec581f8cb825
|
# Dataset Card for "TalkBank_CA_wM_CLAPI"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_wM_CLAPI
|
[
"region:us"
] |
2023-10-24T23:58:50+00:00
|
{"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1651019172, "num_examples": 24}], "download_size": 1653522367, "dataset_size": 1651019172}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T23:59:45+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_wM_CLAPI"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_wM_CLAPI\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_wM_CLAPI\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_wM_CLAPI\"\n\nMore Information needed"
] |
72faff4168ebcfda45158094c1f40245b50473fe
|
# Dataset Card for "TalkBank_CA_wM_CORAAL"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_wM_CORAAL
|
[
"region:us"
] |
2023-10-25T00:06:39+00:00
|
{"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9337421051, "num_examples": 85}], "download_size": 9344589919, "dataset_size": 9337421051}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T00:12:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_wM_CORAAL"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_wM_CORAAL\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_wM_CORAAL\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_wM_CORAAL\"\n\nMore Information needed"
] |
68351931a584aa19b6399989656992acf2529764
|
# Dataset Card for "TalkBank_CA_wM_cv_gender_accent_50k_16kHz.pkl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/TalkBank_CA_wM_cv_gender_accent_50k_16kHz.pkl
|
[
"region:us"
] |
2023-10-25T00:12:13+00:00
|
{"dataset_info": {"features": [{"name": "__index_level_0__", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 0, "num_examples": 0}], "download_size": 582, "dataset_size": 0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T00:12:15+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TalkBank_CA_wM_cv_gender_accent_50k_16kHz.pkl"
More Information needed
|
[
"# Dataset Card for \"TalkBank_CA_wM_cv_gender_accent_50k_16kHz.pkl\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TalkBank_CA_wM_cv_gender_accent_50k_16kHz.pkl\"\n\nMore Information needed"
] |
[
6,
36
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TalkBank_CA_wM_cv_gender_accent_50k_16kHz.pkl\"\n\nMore Information needed"
] |
d8205360ff94dd33bf3e8705f75e541728e95f61
|
asdxaswcdaqqqqqqqq
|
jackeyhug/jackeydataset
|
[
"language:aa",
"license:apache-2.0",
"region:us"
] |
2023-10-25T00:25:44+00:00
|
{"language": ["aa"], "license": "apache-2.0"}
|
2023-11-20T05:37:15+00:00
|
[] |
[
"aa"
] |
TAGS
#language-Afar #license-apache-2.0 #region-us
|
asdxaswcdaqqqqqqqq
|
[] |
[
"TAGS\n#language-Afar #license-apache-2.0 #region-us \n"
] |
[
19
] |
[
"passage: TAGS\n#language-Afar #license-apache-2.0 #region-us \n"
] |
5911cd2963395587cc10d21e5877ac3775ab6032
|
# Dataset Card for Evaluation run of Locutusque/gpt2-large-conversational
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Locutusque/gpt2-large-conversational
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Locutusque/gpt2-large-conversational](https://huggingface.co/Locutusque/gpt2-large-conversational) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Locutusque__gpt2-large-conversational",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T01:46:41.651316](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__gpt2-large-conversational/blob/main/results_2023-10-25T01-46-41.651316.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0026216442953020135,
"em_stderr": 0.00052366856429659,
"f1": 0.05192533557046985,
"f1_stderr": 0.001344504704587554,
"acc": 0.2805684988926939,
"acc_stderr": 0.007353899944869109
},
"harness|drop|3": {
"em": 0.0026216442953020135,
"em_stderr": 0.00052366856429659,
"f1": 0.05192533557046985,
"f1_stderr": 0.001344504704587554
},
"harness|gsm8k|5": {
"acc": 0.000758150113722517,
"acc_stderr": 0.0007581501137225207
},
"harness|winogrande|5": {
"acc": 0.5603788476716653,
"acc_stderr": 0.013949649776015698
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Locutusque__gpt2-large-conversational
|
[
"region:us"
] |
2023-10-25T00:46:44+00:00
|
{"pretty_name": "Evaluation run of Locutusque/gpt2-large-conversational", "dataset_summary": "Dataset automatically created during the evaluation run of model [Locutusque/gpt2-large-conversational](https://huggingface.co/Locutusque/gpt2-large-conversational) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Locutusque__gpt2-large-conversational\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T01:46:41.651316](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__gpt2-large-conversational/blob/main/results_2023-10-25T01-46-41.651316.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0026216442953020135,\n \"em_stderr\": 0.00052366856429659,\n \"f1\": 0.05192533557046985,\n \"f1_stderr\": 0.001344504704587554,\n \"acc\": 0.2805684988926939,\n \"acc_stderr\": 0.007353899944869109\n },\n \"harness|drop|3\": {\n \"em\": 0.0026216442953020135,\n \"em_stderr\": 0.00052366856429659,\n \"f1\": 0.05192533557046985,\n \"f1_stderr\": 0.001344504704587554\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \"acc_stderr\": 0.0007581501137225207\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5603788476716653,\n \"acc_stderr\": 0.013949649776015698\n }\n}\n```", "repo_url": "https://huggingface.co/Locutusque/gpt2-large-conversational", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_25T01_46_41.651316", "path": ["**/details_harness|drop|3_2023-10-25T01-46-41.651316.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-25T01-46-41.651316.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_25T01_46_41.651316", "path": ["**/details_harness|gsm8k|5_2023-10-25T01-46-41.651316.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-25T01-46-41.651316.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_25T01_46_41.651316", "path": ["**/details_harness|winogrande|5_2023-10-25T01-46-41.651316.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-25T01-46-41.651316.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_25T01_46_41.651316", "path": ["results_2023-10-25T01-46-41.651316.parquet"]}, {"split": "latest", "path": ["results_2023-10-25T01-46-41.651316.parquet"]}]}]}
|
2023-10-25T00:46:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Locutusque/gpt2-large-conversational
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Locutusque/gpt2-large-conversational on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-25T01:46:41.651316(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Locutusque/gpt2-large-conversational",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Locutusque/gpt2-large-conversational on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T01:46:41.651316(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Locutusque/gpt2-large-conversational",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Locutusque/gpt2-large-conversational on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T01:46:41.651316(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
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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",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Locutusque/gpt2-large-conversational## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Locutusque/gpt2-large-conversational on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-25T01:46:41.651316(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
2ffe008d2faa4f84f04870ca32ff5510b6acedb1
|
# Dataset Card for "quac-lamini-instruction-indo-2.6M"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
manfredmichael/quac-lamini-instruction-indo-2.6M
|
[
"region:us"
] |
2023-10-25T00:50:24+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "float64"}, {"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "instruction_sources", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1276671946, "num_examples": 2585614}], "download_size": 713557171, "dataset_size": 1276671946}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T01:55:46+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "quac-lamini-instruction-indo-2.6M"
More Information needed
|
[
"# Dataset Card for \"quac-lamini-instruction-indo-2.6M\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"quac-lamini-instruction-indo-2.6M\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"quac-lamini-instruction-indo-2.6M\"\n\nMore Information needed"
] |
39c1bc95ef8b59c30c90f00248d1adf0121ca35b
|
# Dataset Card for "Long-Data-Col-rp_pile_pretrain"
This dataset is a subset of [togethercomputer/Long-Data-Collections](https://huggingface.co/datasets/togethercomputer/Long-Data-Collections), namely the `rp_sub.jsonl.zst` and `pile_sub.jsonl.zst` files from the `pretrain` split.
Like the source dataset, we do not attempt to modify/change licenses of underlying data. Refer to the source dataset (and its source datasets) for details.
## changes
1. as this is supposed to be a "long text dataset", we drop all rows where `text` contains <= 250 characters. This drops approx 100k rows from the raw data. Resulting stats are below.
| | text_len |
|:------|----------------:|
| count | 2.75956e+06 |
| mean | 6195.11 |
| std | 56364.9 |
| min | 251 |
| 25% | 1102 |
| 50% | 2147 |
| 75% | 4762 |
| max | 4.66452e+07 |
---
|
BEE-spoke-data/Long-Data-Col-rp_pile_pretrain
|
[
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:feature-extraction",
"size_categories:1M<n<10M",
"source_datasets:togethercomputer/Long-Data-Collections",
"license:other",
"long boi",
"region:us"
] |
2023-10-25T00:52:15+00:00
|
{"license": "other", "size_categories": ["1M<n<10M"], "source_datasets": "togethercomputer/Long-Data-Collections", "task_categories": ["text-generation", "fill-mask", "feature-extraction"], "configs": [{"config_name": "cleaned", "data_files": [{"split": "train", "path": "cleaned/train-*"}]}, {"config_name": "cleaned-dedup", "data_files": [{"split": "train", "path": "cleaned-dedup/train-*"}]}, {"config_name": "cleaned-dedup-en", "data_files": [{"split": "train", "path": "cleaned-dedup-en/train-*"}]}, {"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": [{"config_name": "cleaned", "features": [{"name": "text", "dtype": "string"}, {"name": "meta", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16969436991, "num_examples": 2759555}], "download_size": 9521997027, "dataset_size": 16969436991}, {"config_name": "cleaned-dedup", "features": [{"name": "text", "dtype": "string"}, {"name": "meta", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13009681081, "num_examples": 2712907}], "download_size": 7319241627, "dataset_size": 13009681081}, {"config_name": "cleaned-dedup-en", "features": [{"name": "text", "dtype": "string"}, {"name": "meta", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12723856310.202166, "num_examples": 2653304}], "download_size": 7180653999, "dataset_size": 12723856310.202166}, {"config_name": "default", "features": [{"name": "text", "dtype": "string"}, {"name": "meta", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16821991568.354612, "num_examples": 2759555}], "download_size": 9685120636, "dataset_size": 16821991568.354612}], "tags": ["long boi"]}
|
2023-10-26T01:01:57+00:00
|
[] |
[] |
TAGS
#task_categories-text-generation #task_categories-fill-mask #task_categories-feature-extraction #size_categories-1M<n<10M #source_datasets-togethercomputer/Long-Data-Collections #license-other #long boi #region-us
|
Dataset Card for "Long-Data-Col-rp\_pile\_pretrain"
===================================================
This dataset is a subset of togethercomputer/Long-Data-Collections, namely the 'rp\_sub.URL' and 'pile\_sub.URL' files from the 'pretrain' split.
Like the source dataset, we do not attempt to modify/change licenses of underlying data. Refer to the source dataset (and its source datasets) for details.
changes
-------
1. as this is supposed to be a "long text dataset", we drop all rows where 'text' contains <= 250 characters. This drops approx 100k rows from the raw data. Resulting stats are below.
---
|
[] |
[
"TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_categories-feature-extraction #size_categories-1M<n<10M #source_datasets-togethercomputer/Long-Data-Collections #license-other #long boi #region-us \n"
] |
[
79
] |
[
"passage: TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_categories-feature-extraction #size_categories-1M<n<10M #source_datasets-togethercomputer/Long-Data-Collections #license-other #long boi #region-us \n"
] |
df2684f224559e0f49fb0abff23eefb5e57d6b9c
|
# Dataset Card for "mlcb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nathanReitinger/mlcb
|
[
"region:us"
] |
2023-10-25T00:54:40+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8132250961, "num_examples": 76369}, {"name": "test", "num_bytes": 897865830, "num_examples": 8486}], "download_size": 2715307703, "dataset_size": 9030116791}}
|
2023-11-04T02:30:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "mlcb"
More Information needed
|
[
"# Dataset Card for \"mlcb\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"mlcb\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"mlcb\"\n\nMore Information needed"
] |
415a99a8f52081666a409222faad7092e4e0f618
|
# Dataset Card for "imaginary_patient_cases"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
BLACKBUN/imaginary_patient_cases
|
[
"region:us"
] |
2023-10-25T01:10:32+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "explanation", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2673420, "num_examples": 4970}], "download_size": 680987, "dataset_size": 2673420}}
|
2023-10-25T01:10:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "imaginary_patient_cases"
More Information needed
|
[
"# Dataset Card for \"imaginary_patient_cases\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"imaginary_patient_cases\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"imaginary_patient_cases\"\n\nMore Information needed"
] |
7f180a27d083331181d2e44fd04c5ffd19fcf081
|
# Dataset Card for "repobench_ablation_64k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tianyang/repobench_ablation_64k
|
[
"region:us"
] |
2023-10-25T01:21:36+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "cross_file_first", "path": "data/cross_file_first-*"}, {"split": "cross_file_random", "path": "data/cross_file_random-*"}, {"split": "in_file", "path": "data/in_file-*"}]}], "dataset_info": {"features": [{"name": "repo_name", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "context", "list": [{"name": "identifier", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "snippet", "dtype": "string"}]}, {"name": "import_statement", "dtype": "string"}, {"name": "token_num", "dtype": "int64"}, {"name": "cropped_code", "dtype": "string"}, {"name": "all_code", "dtype": "string"}, {"name": "next_line", "dtype": "string"}, {"name": "gold_snippet_index", "dtype": "int64"}], "splits": [{"name": "cross_file_first", "num_bytes": 248891343, "num_examples": 3644}, {"name": "cross_file_random", "num_bytes": 201499818, "num_examples": 3321}, {"name": "in_file", "num_bytes": 231190696, "num_examples": 3549}], "download_size": 209884822, "dataset_size": 681581857}}
|
2023-10-25T01:25:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "repobench_ablation_64k"
More Information needed
|
[
"# Dataset Card for \"repobench_ablation_64k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"repobench_ablation_64k\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"repobench_ablation_64k\"\n\nMore Information needed"
] |
78924cd02e0bd3d2730ddb50a454c51a9ac9bd86
|
# Dataset Card for "repobench_ablation_8k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tianyang/repobench_ablation_8k
|
[
"region:us"
] |
2023-10-25T01:21:48+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "cross_file_first", "path": "data/cross_file_first-*"}, {"split": "cross_file_random", "path": "data/cross_file_random-*"}, {"split": "in_file", "path": "data/in_file-*"}]}], "dataset_info": {"features": [{"name": "repo_name", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "context", "list": [{"name": "identifier", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "snippet", "dtype": "string"}]}, {"name": "import_statement", "dtype": "string"}, {"name": "token_num", "dtype": "int64"}, {"name": "cropped_code", "dtype": "string"}, {"name": "all_code", "dtype": "string"}, {"name": "next_line", "dtype": "string"}, {"name": "gold_snippet_index", "dtype": "int64"}], "splits": [{"name": "cross_file_first", "num_bytes": 76590132, "num_examples": 4000}, {"name": "cross_file_random", "num_bytes": 77383139, "num_examples": 3919}, {"name": "in_file", "num_bytes": 74963194, "num_examples": 4000}], "download_size": 83713495, "dataset_size": 228936465}}
|
2023-10-25T01:25:45+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "repobench_ablation_8k"
More Information needed
|
[
"# Dataset Card for \"repobench_ablation_8k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"repobench_ablation_8k\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"repobench_ablation_8k\"\n\nMore Information needed"
] |
a490f5292b8d30df3e3c2dca47767eec06fd400f
|
# Bangumi Image Base of Food Wars!
This is the image base of bangumi Food Wars!, we detected 129 characters, 16059 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:----------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|
| 0 | 87 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 664 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 69 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 172 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
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| 6 | 83 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
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| 12 | 73 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
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| 27 | 87 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 112 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 73 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 84 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
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| 111 | 12 | [Download](111/dataset.zip) |  |  |  |  |  |  |  |  |
| 112 | 6 | [Download](112/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
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| 118 | 34 | [Download](118/dataset.zip) |  |  |  |  |  |  |  |  |
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| 120 | 120 | [Download](120/dataset.zip) |  |  |  |  |  |  |  |  |
| 121 | 28 | [Download](121/dataset.zip) |  |  |  |  |  |  |  |  |
| 122 | 27 | [Download](122/dataset.zip) |  |  |  |  |  |  |  |  |
| 123 | 64 | [Download](123/dataset.zip) |  |  |  |  |  |  |  |  |
| 124 | 19 | [Download](124/dataset.zip) |  |  |  |  |  |  |  |  |
| 125 | 21 | [Download](125/dataset.zip) |  |  |  |  |  |  |  |  |
| 126 | 8 | [Download](126/dataset.zip) |  |  |  |  |  |  |  |  |
| 127 | 24 | [Download](127/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 576 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/foodwars
|
[
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
] |
2023-10-25T01:25:25+00:00
|
{"license": "mit", "size_categories": ["10K<n<100K"], "tags": ["art"]}
|
2023-10-25T08:29:56+00:00
|
[] |
[] |
TAGS
#size_categories-10K<n<100K #license-mit #art #region-us
|
Bangumi Image Base of Food Wars!
================================
This is the image base of bangumi Food Wars!, we detected 129 characters, 16059 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-10K<n<100K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-10K<n<100K #license-mit #art #region-us \n"
] |
cbf5749b2d3e0ca57a6da7a1b06fd7a5895b2bad
|
# Dataset Card for "orca-filtered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
thangvip/orca-filtered
|
[
"region:us"
] |
2023-10-25T01:39:53+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2241780185.3238597, "num_examples": 1314379}], "download_size": 639463930, "dataset_size": 2241780185.3238597}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T01:41:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "orca-filtered"
More Information needed
|
[
"# Dataset Card for \"orca-filtered\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"orca-filtered\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"orca-filtered\"\n\nMore Information needed"
] |
bd27b3871d91302e7a40037fb781233287cb3da1
|
# Dataset Card for "a17bd262"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/a17bd262
|
[
"region:us"
] |
2023-10-25T01:44:54+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 201, "num_examples": 10}], "download_size": 1374, "dataset_size": 201}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T01:44:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "a17bd262"
More Information needed
|
[
"# Dataset Card for \"a17bd262\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"a17bd262\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"a17bd262\"\n\nMore Information needed"
] |
a5f9b4ece06afb1e1eff8ef950455b4794d3778b
|
# KOR-OpenOrca-Platypus-v2
- KOR-OpenOrca-Platypus 데이터셋에서 수작업으로 번역 오류 200건 이상을 고친 데이터셋.
- 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다😭😭
## KOpen-platpyus
Repo: [KOpen-platypus](https://huggingface.co/datasets/kyujinpy/KOpen-platypus)
- 고품질 한국어 데이터셋
1. 코드와 주석은 그대로 유지하고, 설명 부분만 한국어로 수정
2. 1번과 더불어서, Python, Java, Cpp, xml 등등 결과들은 전부 기존의 데이터 형태로 최대한 보존
3. 단일 숫자와 영어는 본래의 결과 그대로 가져옴
4. DeepL Pro 번역 결과 중 미완성 변역 결과 직접 수정(예를 들면, '[...]'가 포함되어 있음)
5. DeepL Pro 번역 결과가 본래의 데이터에 비해 글자수가 50% 이하로 낮으면, 번역 결과 수정
6. 번역하고자 하는 글자수가 1500자 이상일 경우, API로 변경해서 번역
7. 고유명사는 최대한 유지함
> Post-processing 작업 내용
## OpenOrca-Ko-v2
1. NIV // 약 1500개
2. FLAN // 약 9000개
3. T0 // 약 6000개
4. CoT // 약 2000개
> Dataset 구성
- 수작업으로 고친 내용(v2)
1. 영어로 된 답변 수정. (Ex. Nick -> 닉, Lucky -> 운이 좋음, ...)
2. KoCoT 데이터셋 제거.
3. Yes, True, False 등등 일부 답변 수정
> Post-processing 작업 내용
## Translation
Using DeepL Pro API. Thanks.
---
>Below is original dataset card
## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Dataset Attribution](#dataset-attribution)
- [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)
- [Dataset Use](#dataset-use)
- [Use Cases](#use-cases)
- [Usage Caveats](#usage-caveats)
- [Getting Started](#getting-started)
<p><h1>🐋 The OpenOrca Dataset! 🐋</h1></p>
<a name="dataset-announcement"></a>
We are thrilled to announce the release of the OpenOrca dataset!
This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the [Orca paper](https://arxiv.org/abs/2306.02707).
It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!
# Official Models
## OpenOrca-Platypus2-13B
Our [latest release](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B), the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!
Released in partnership with Platypus.
## LlongOrca 7B & 13B
* Our [first 7B release](https://huggingface.co/Open-Orca/LlongOrca-7B-16k), trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.
* [LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k), trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.
## OpenOrcaxOpenChat-Preview2-13B
Our [second model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B), highlighting that we've surpassed the performance reported in the Orca paper.
Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.
Released in partnership with OpenChat.
## OpenOrca-Preview1-13B
[OpenOrca-Preview1-13B](https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B)
This model was trained in less than a day, for <$200, with <10% of our data.
At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.
<a name="dataset-summary"></a>
# Dataset Summary
The OpenOrca dataset is a collection of augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688).
Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.
It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.
The data is primarily used for training and evaluation in the field of natural language processing.
<a name="dataset-attribution"></a>
# Dataset Attribution
We would like to give special recognition to the following contributors for their significant efforts and dedication:
Teknium
WingLian/Caseus
Eric Hartford
NanoBit
Pankaj
Winddude
Rohan
http://AlignmentLab.ai:
Autometa
Entropi
AtlasUnified
NeverendingToast
NanoBit
WingLian/Caseus
Also of course, as always, TheBloke, for being the backbone of the whole community.
Many thanks to NanoBit and Caseus, makers of [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others!
We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:
http://Alignmentlab.ai https://discord.gg/n9hXaBPWxx
Want to visualize our full dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2).
[<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2)
<a name="supported-tasks-and-leaderboards"></a>
# Supported Tasks and Leaderboards
This dataset supports a range of tasks including language modeling, text generation, and text augmentation.
It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.
Further information on leaderboards will be updated as they become available.
<a name="languages"></a>
# Languages
The language of the data is primarily English.
<a name="dataset-structure"></a>
# Dataset Structure
<a name="data-instances"></a>
## Data Instances
A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.
The response is then entered into the response field.
<a name="data-fields"></a>
## Data Fields
The fields are:
1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
3) 'question', representing a question entry as provided by the FLAN Collection
4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.
<a name="data-splits"></a>
## Data Splits
The data is unsplit.
<a name="dataset-creation"></a>
# Dataset Creation
<a name="curation-rationale"></a>
## Curation Rationale
The dataset was created to provide a source of augmented text data for researchers and developers.
The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.
This "reasoning trace" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.
<a name="source-data"></a>
## Source Data
The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:
1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use.
We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.
2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. [conceptofmind/flan2021](https://huggingface.co/datasets/conceptofmind/flan2021_submix_original).
These are referenced by the [official FLAN Collection repo](https://github.com/google-research/FLAN/tree/main/flan/v2) as the preferred data source.
However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.
Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.
<a name="dataset-use"></a>
# Dataset Use
<a name="use-cases"></a>
## Use Cases
The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.
<a name="usage-caveats"></a>
## Usage Caveats
Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements.
Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.
<a name="getting-started"></a>
## Getting Started
This dataset is organized such that it can be naively loaded via Hugging Face datasets library.
We recommend using streaming due to the large size of the files.
Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face.
# Citation
```bibtex
@misc{OpenOrca,
title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces},
author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca},
}
```
```bibtex
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
```bibtex
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint= arXiv 2307.09288
}
@software{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
```
|
kyujinpy/KOR-OpenOrca-Platypus-v2
|
[
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<50K",
"language:ko",
"license:cc-by-nc-4.0",
"arxiv:2306.02707",
"arxiv:2301.13688",
"region:us"
] |
2023-10-25T01:50:26+00:00
|
{"language": ["ko"], "license": "cc-by-nc-4.0", "size_categories": ["10K<n<50K"], "task_categories": ["conversational", "text-classification", "token-classification", "table-question-answering", "question-answering", "zero-shot-classification", "summarization", "feature-extraction", "text-generation", "text2text-generation"], "pretty_name": "OpenOrca", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 75960444, "num_examples": 44394}], "download_size": 38457343, "dataset_size": 75960444}}
|
2023-10-25T10:50:48+00:00
|
[
"2306.02707",
"2301.13688"
] |
[
"ko"
] |
TAGS
#task_categories-conversational #task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-summarization #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #size_categories-10K<n<50K #language-Korean #license-cc-by-nc-4.0 #arxiv-2306.02707 #arxiv-2301.13688 #region-us
|
# KOR-OpenOrca-Platypus-v2
- KOR-OpenOrca-Platypus 데이터셋에서 수작업으로 번역 오류 200건 이상을 고친 데이터셋.
- 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다
## KOpen-platpyus
Repo: KOpen-platypus
- 고품질 한국어 데이터셋
1. 코드와 주석은 그대로 유지하고, 설명 부분만 한국어로 수정
2. 1번과 더불어서, Python, Java, Cpp, xml 등등 결과들은 전부 기존의 데이터 형태로 최대한 보존
3. 단일 숫자와 영어는 본래의 결과 그대로 가져옴
4. DeepL Pro 번역 결과 중 미완성 변역 결과 직접 수정(예를 들면, '[...]'가 포함되어 있음)
5. DeepL Pro 번역 결과가 본래의 데이터에 비해 글자수가 50% 이하로 낮으면, 번역 결과 수정
6. 번역하고자 하는 글자수가 1500자 이상일 경우, API로 변경해서 번역
7. 고유명사는 최대한 유지함
> Post-processing 작업 내용
## OpenOrca-Ko-v2
1. NIV // 약 1500개
2. FLAN // 약 9000개
3. T0 // 약 6000개
4. CoT // 약 2000개
> Dataset 구성
- 수작업으로 고친 내용(v2)
1. 영어로 된 답변 수정. (Ex. Nick -> 닉, Lucky -> 운이 좋음, ...)
2. KoCoT 데이터셋 제거.
3. Yes, True, False 등등 일부 답변 수정
> Post-processing 작업 내용
## Translation
Using DeepL Pro API. Thanks.
---
>Below is original dataset card
## Table of Contents
- Dataset Summary
- Dataset Attribution
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Dataset Use
- Use Cases
- Usage Caveats
- Getting Started
<p><h1> The OpenOrca Dataset! </h1></p>
!OpenOrca Logo
<a name="dataset-announcement"></a>
We are thrilled to announce the release of the OpenOrca dataset!
This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the Orca paper.
It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!
# Official Models
## OpenOrca-Platypus2-13B
Our latest release, the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!
Released in partnership with Platypus.
## LlongOrca 7B & 13B
* Our first 7B release, trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.
* LlongOrca-13B-16k, trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.
## OpenOrcaxOpenChat-Preview2-13B
Our second model, highlighting that we've surpassed the performance reported in the Orca paper.
Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.
Released in partnership with OpenChat.
## OpenOrca-Preview1-13B
OpenOrca-Preview1-13B
This model was trained in less than a day, for <$200, with <10% of our data.
At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.
<a name="dataset-summary"></a>
# Dataset Summary
The OpenOrca dataset is a collection of augmented FLAN Collection data.
Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.
It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.
The data is primarily used for training and evaluation in the field of natural language processing.
<a name="dataset-attribution"></a>
# Dataset Attribution
We would like to give special recognition to the following contributors for their significant efforts and dedication:
Teknium
WingLian/Caseus
Eric Hartford
NanoBit
Pankaj
Winddude
Rohan
URL:
Autometa
Entropi
AtlasUnified
NeverendingToast
NanoBit
WingLian/Caseus
Also of course, as always, TheBloke, for being the backbone of the whole community.
Many thanks to NanoBit and Caseus, makers of Axolotl, for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others!
We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:
URL URL
Want to visualize our full dataset? Check out our Nomic Atlas Map.
<img src="URL alt="Atlas Nomic Dataset Map" width="400" height="400" />
<a name="supported-tasks-and-leaderboards"></a>
# Supported Tasks and Leaderboards
This dataset supports a range of tasks including language modeling, text generation, and text augmentation.
It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.
Further information on leaderboards will be updated as they become available.
<a name="languages"></a>
# Languages
The language of the data is primarily English.
<a name="dataset-structure"></a>
# Dataset Structure
<a name="data-instances"></a>
## Data Instances
A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.
The response is then entered into the response field.
<a name="data-fields"></a>
## Data Fields
The fields are:
1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
3) 'question', representing a question entry as provided by the FLAN Collection
4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.
<a name="data-splits"></a>
## Data Splits
The data is unsplit.
<a name="dataset-creation"></a>
# Dataset Creation
<a name="curation-rationale"></a>
## Curation Rationale
The dataset was created to provide a source of augmented text data for researchers and developers.
The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.
This "reasoning trace" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.
<a name="source-data"></a>
## Source Data
The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:
1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use.
We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.
2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. conceptofmind/flan2021.
These are referenced by the official FLAN Collection repo as the preferred data source.
However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.
Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.
<a name="dataset-use"></a>
# Dataset Use
<a name="use-cases"></a>
## Use Cases
The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.
<a name="usage-caveats"></a>
## Usage Caveats
Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements.
Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.
<a name="getting-started"></a>
## Getting Started
This dataset is organized such that it can be naively loaded via Hugging Face datasets library.
We recommend using streaming due to the large size of the files.
Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face.
|
[
"# KOR-OpenOrca-Platypus-v2\n- KOR-OpenOrca-Platypus 데이터셋에서 수작업으로 번역 오류 200건 이상을 고친 데이터셋. \n- 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다",
"## KOpen-platpyus\nRepo: KOpen-platypus\n\n- 고품질 한국어 데이터셋 \n1. 코드와 주석은 그대로 유지하고, 설명 부분만 한국어로 수정\n2. 1번과 더불어서, Python, Java, Cpp, xml 등등 결과들은 전부 기존의 데이터 형태로 최대한 보존\n3. 단일 숫자와 영어는 본래의 결과 그대로 가져옴\n4. DeepL Pro 번역 결과 중 미완성 변역 결과 직접 수정(예를 들면, '[...]'가 포함되어 있음)\n5. DeepL Pro 번역 결과가 본래의 데이터에 비해 글자수가 50% 이하로 낮으면, 번역 결과 수정\n6. 번역하고자 하는 글자수가 1500자 이상일 경우, API로 변경해서 번역\n7. 고유명사는 최대한 유지함\n> Post-processing 작업 내용",
"## OpenOrca-Ko-v2 \n1. NIV // 약 1500개\n2. FLAN // 약 9000개\n3. T0 // 약 6000개\n4. CoT // 약 2000개\n> Dataset 구성 \n \n- 수작업으로 고친 내용(v2) \n1. 영어로 된 답변 수정. (Ex. Nick -> 닉, Lucky -> 운이 좋음, ...) \n2. KoCoT 데이터셋 제거. \n3. Yes, True, False 등등 일부 답변 수정 \n> Post-processing 작업 내용",
"## Translation\nUsing DeepL Pro API. Thanks.\n\n---\n>Below is original dataset card",
"## Table of Contents\n- Dataset Summary\n- Dataset Attribution\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- Dataset Use\n - Use Cases\n - Usage Caveats\n - Getting Started\n\n\n<p><h1> The OpenOrca Dataset! </h1></p>\n\n!OpenOrca Logo\n\n<a name=\"dataset-announcement\"></a>\n\nWe are thrilled to announce the release of the OpenOrca dataset!\nThis rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the Orca paper.\nIt has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!",
"# Official Models",
"## OpenOrca-Platypus2-13B\n\nOur latest release, the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!\nReleased in partnership with Platypus.",
"## LlongOrca 7B & 13B\n\n* Our first 7B release, trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.\n* LlongOrca-13B-16k, trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.",
"## OpenOrcaxOpenChat-Preview2-13B\n\nOur second model, highlighting that we've surpassed the performance reported in the Orca paper.\nWas #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.\nReleased in partnership with OpenChat.",
"## OpenOrca-Preview1-13B\n\nOpenOrca-Preview1-13B\nThis model was trained in less than a day, for <$200, with <10% of our data.\nAt release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.\n\n<a name=\"dataset-summary\"></a>",
"# Dataset Summary\n\nThe OpenOrca dataset is a collection of augmented FLAN Collection data.\nCurrently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.\nIt is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.\nThe data is primarily used for training and evaluation in the field of natural language processing.\n\n<a name=\"dataset-attribution\"></a>",
"# Dataset Attribution\n\nWe would like to give special recognition to the following contributors for their significant efforts and dedication:\n \n\n Teknium \n WingLian/Caseus\n Eric Hartford\n NanoBit\n Pankaj\n Winddude\n Rohan\n\n URL:\n Autometa\n Entropi\n AtlasUnified\n NeverendingToast\n NanoBit\n WingLian/Caseus\n\nAlso of course, as always, TheBloke, for being the backbone of the whole community.\n\nMany thanks to NanoBit and Caseus, makers of Axolotl, for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others! \n\nWe are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:\nURL URL\n\nWant to visualize our full dataset? Check out our Nomic Atlas Map.\n <img src=\"URL alt=\"Atlas Nomic Dataset Map\" width=\"400\" height=\"400\" />\n\n\n<a name=\"supported-tasks-and-leaderboards\"></a>",
"# Supported Tasks and Leaderboards\n\nThis dataset supports a range of tasks including language modeling, text generation, and text augmentation.\nIt has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.\nFurther information on leaderboards will be updated as they become available.\n\n<a name=\"languages\"></a>",
"# Languages\n\nThe language of the data is primarily English.\n\n<a name=\"dataset-structure\"></a>",
"# Dataset Structure\n\n<a name=\"data-instances\"></a>",
"## Data Instances\n\nA data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.\nThe response is then entered into the response field.\n\n<a name=\"data-fields\"></a>",
"## Data Fields\n\nThe fields are:\n1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.\n2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint\n3) 'question', representing a question entry as provided by the FLAN Collection\n4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.\n\n<a name=\"data-splits\"></a>",
"## Data Splits\n\nThe data is unsplit.\n\n<a name=\"dataset-creation\"></a>",
"# Dataset Creation\n\n<a name=\"curation-rationale\"></a>",
"## Curation Rationale\n\nThe dataset was created to provide a source of augmented text data for researchers and developers.\nThe datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.\nThis \"reasoning trace\" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.\n\n<a name=\"source-data\"></a>",
"## Source Data\n\nThe data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:\n\n1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use.\n We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.\n2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. conceptofmind/flan2021.\n These are referenced by the official FLAN Collection repo as the preferred data source.\n However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.\n\nCombined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.\n\n<a name=\"dataset-use\"></a>",
"# Dataset Use\n\n<a name=\"use-cases\"></a>",
"## Use Cases\n\nThe dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.\n\n<a name=\"usage-caveats\"></a>",
"## Usage Caveats\n\nGiven that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements.\nFurther, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.\n\n<a name=\"getting-started\"></a>",
"## Getting Started\n\nThis dataset is organized such that it can be naively loaded via Hugging Face datasets library.\nWe recommend using streaming due to the large size of the files.\nRegular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face."
] |
[
"TAGS\n#task_categories-conversational #task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-summarization #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #size_categories-10K<n<50K #language-Korean #license-cc-by-nc-4.0 #arxiv-2306.02707 #arxiv-2301.13688 #region-us \n",
"# KOR-OpenOrca-Platypus-v2\n- KOR-OpenOrca-Platypus 데이터셋에서 수작업으로 번역 오류 200건 이상을 고친 데이터셋. \n- 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다",
"## KOpen-platpyus\nRepo: KOpen-platypus\n\n- 고품질 한국어 데이터셋 \n1. 코드와 주석은 그대로 유지하고, 설명 부분만 한국어로 수정\n2. 1번과 더불어서, Python, Java, Cpp, xml 등등 결과들은 전부 기존의 데이터 형태로 최대한 보존\n3. 단일 숫자와 영어는 본래의 결과 그대로 가져옴\n4. DeepL Pro 번역 결과 중 미완성 변역 결과 직접 수정(예를 들면, '[...]'가 포함되어 있음)\n5. DeepL Pro 번역 결과가 본래의 데이터에 비해 글자수가 50% 이하로 낮으면, 번역 결과 수정\n6. 번역하고자 하는 글자수가 1500자 이상일 경우, API로 변경해서 번역\n7. 고유명사는 최대한 유지함\n> Post-processing 작업 내용",
"## OpenOrca-Ko-v2 \n1. NIV // 약 1500개\n2. FLAN // 약 9000개\n3. T0 // 약 6000개\n4. CoT // 약 2000개\n> Dataset 구성 \n \n- 수작업으로 고친 내용(v2) \n1. 영어로 된 답변 수정. (Ex. Nick -> 닉, Lucky -> 운이 좋음, ...) \n2. KoCoT 데이터셋 제거. \n3. Yes, True, False 등등 일부 답변 수정 \n> Post-processing 작업 내용",
"## Translation\nUsing DeepL Pro API. Thanks.\n\n---\n>Below is original dataset card",
"## Table of Contents\n- Dataset Summary\n- Dataset Attribution\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- Dataset Use\n - Use Cases\n - Usage Caveats\n - Getting Started\n\n\n<p><h1> The OpenOrca Dataset! </h1></p>\n\n!OpenOrca Logo\n\n<a name=\"dataset-announcement\"></a>\n\nWe are thrilled to announce the release of the OpenOrca dataset!\nThis rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the Orca paper.\nIt has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!",
"# Official Models",
"## OpenOrca-Platypus2-13B\n\nOur latest release, the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!\nReleased in partnership with Platypus.",
"## LlongOrca 7B & 13B\n\n* Our first 7B release, trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.\n* LlongOrca-13B-16k, trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.",
"## OpenOrcaxOpenChat-Preview2-13B\n\nOur second model, highlighting that we've surpassed the performance reported in the Orca paper.\nWas #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.\nReleased in partnership with OpenChat.",
"## OpenOrca-Preview1-13B\n\nOpenOrca-Preview1-13B\nThis model was trained in less than a day, for <$200, with <10% of our data.\nAt release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.\n\n<a name=\"dataset-summary\"></a>",
"# Dataset Summary\n\nThe OpenOrca dataset is a collection of augmented FLAN Collection data.\nCurrently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.\nIt is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.\nThe data is primarily used for training and evaluation in the field of natural language processing.\n\n<a name=\"dataset-attribution\"></a>",
"# Dataset Attribution\n\nWe would like to give special recognition to the following contributors for their significant efforts and dedication:\n \n\n Teknium \n WingLian/Caseus\n Eric Hartford\n NanoBit\n Pankaj\n Winddude\n Rohan\n\n URL:\n Autometa\n Entropi\n AtlasUnified\n NeverendingToast\n NanoBit\n WingLian/Caseus\n\nAlso of course, as always, TheBloke, for being the backbone of the whole community.\n\nMany thanks to NanoBit and Caseus, makers of Axolotl, for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others! \n\nWe are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:\nURL URL\n\nWant to visualize our full dataset? Check out our Nomic Atlas Map.\n <img src=\"URL alt=\"Atlas Nomic Dataset Map\" width=\"400\" height=\"400\" />\n\n\n<a name=\"supported-tasks-and-leaderboards\"></a>",
"# Supported Tasks and Leaderboards\n\nThis dataset supports a range of tasks including language modeling, text generation, and text augmentation.\nIt has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.\nFurther information on leaderboards will be updated as they become available.\n\n<a name=\"languages\"></a>",
"# Languages\n\nThe language of the data is primarily English.\n\n<a name=\"dataset-structure\"></a>",
"# Dataset Structure\n\n<a name=\"data-instances\"></a>",
"## Data Instances\n\nA data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.\nThe response is then entered into the response field.\n\n<a name=\"data-fields\"></a>",
"## Data Fields\n\nThe fields are:\n1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.\n2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint\n3) 'question', representing a question entry as provided by the FLAN Collection\n4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.\n\n<a name=\"data-splits\"></a>",
"## Data Splits\n\nThe data is unsplit.\n\n<a name=\"dataset-creation\"></a>",
"# Dataset Creation\n\n<a name=\"curation-rationale\"></a>",
"## Curation Rationale\n\nThe dataset was created to provide a source of augmented text data for researchers and developers.\nThe datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.\nThis \"reasoning trace\" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.\n\n<a name=\"source-data\"></a>",
"## Source Data\n\nThe data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:\n\n1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use.\n We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.\n2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. conceptofmind/flan2021.\n These are referenced by the official FLAN Collection repo as the preferred data source.\n However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.\n\nCombined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.\n\n<a name=\"dataset-use\"></a>",
"# Dataset Use\n\n<a name=\"use-cases\"></a>",
"## Use Cases\n\nThe dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.\n\n<a name=\"usage-caveats\"></a>",
"## Usage Caveats\n\nGiven that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements.\nFurther, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.\n\n<a name=\"getting-started\"></a>",
"## Getting Started\n\nThis dataset is organized such that it can be naively loaded via Hugging Face datasets library.\nWe recommend using streaming due to the large size of the files.\nRegular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face."
] |
[
169,
71,
168,
104,
20,
199,
4,
48,
98,
67,
95,
122,
233,
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25,
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67,
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[
"passage: TAGS\n#task_categories-conversational #task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-summarization #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #size_categories-10K<n<50K #language-Korean #license-cc-by-nc-4.0 #arxiv-2306.02707 #arxiv-2301.13688 #region-us \n# KOR-OpenOrca-Platypus-v2\n- KOR-OpenOrca-Platypus 데이터셋에서 수작업으로 번역 오류 200건 이상을 고친 데이터셋. \n- 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다## KOpen-platpyus\nRepo: KOpen-platypus\n\n- 고품질 한국어 데이터셋 \n1. 코드와 주석은 그대로 유지하고, 설명 부분만 한국어로 수정\n2. 1번과 더불어서, Python, Java, Cpp, xml 등등 결과들은 전부 기존의 데이터 형태로 최대한 보존\n3. 단일 숫자와 영어는 본래의 결과 그대로 가져옴\n4. DeepL Pro 번역 결과 중 미완성 변역 결과 직접 수정(예를 들면, '[...]'가 포함되어 있음)\n5. DeepL Pro 번역 결과가 본래의 데이터에 비해 글자수가 50% 이하로 낮으면, 번역 결과 수정\n6. 번역하고자 하는 글자수가 1500자 이상일 경우, API로 변경해서 번역\n7. 고유명사는 최대한 유지함\n> Post-processing 작업 내용",
"passage: ## OpenOrca-Ko-v2 \n1. NIV // 약 1500개\n2. FLAN // 약 9000개\n3. T0 // 약 6000개\n4. CoT // 약 2000개\n> Dataset 구성 \n \n- 수작업으로 고친 내용(v2) \n1. 영어로 된 답변 수정. (Ex. Nick -> 닉, Lucky -> 운이 좋음, ...) \n2. KoCoT 데이터셋 제거. \n3. Yes, True, False 등등 일부 답변 수정 \n> Post-processing 작업 내용## Translation\nUsing DeepL Pro API. Thanks.\n\n---\n>Below is original dataset card## Table of Contents\n- Dataset Summary\n- Dataset Attribution\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- Dataset Use\n - Use Cases\n - Usage Caveats\n - Getting Started\n\n\n<p><h1> The OpenOrca Dataset! </h1></p>\n\n!OpenOrca Logo\n\n<a name=\"dataset-announcement\"></a>\n\nWe are thrilled to announce the release of the OpenOrca dataset!\nThis rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the Orca paper.\nIt has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!# Official Models## OpenOrca-Platypus2-13B\n\nOur latest release, the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!\nReleased in partnership with Platypus.## LlongOrca 7B & 13B\n\n* Our first 7B release, trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.\n* LlongOrca-13B-16k, trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.## OpenOrcaxOpenChat-Preview2-13B\n\nOur second model, highlighting that we've surpassed the performance reported in the Orca paper.\nWas #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.\nReleased in partnership with OpenChat.",
"passage: ## OpenOrca-Preview1-13B\n\nOpenOrca-Preview1-13B\nThis model was trained in less than a day, for <$200, with <10% of our data.\nAt release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.\n\n<a name=\"dataset-summary\"></a># Dataset Summary\n\nThe OpenOrca dataset is a collection of augmented FLAN Collection data.\nCurrently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.\nIt is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.\nThe data is primarily used for training and evaluation in the field of natural language processing.\n\n<a name=\"dataset-attribution\"></a># Dataset Attribution\n\nWe would like to give special recognition to the following contributors for their significant efforts and dedication:\n \n\n Teknium \n WingLian/Caseus\n Eric Hartford\n NanoBit\n Pankaj\n Winddude\n Rohan\n\n URL:\n Autometa\n Entropi\n AtlasUnified\n NeverendingToast\n NanoBit\n WingLian/Caseus\n\nAlso of course, as always, TheBloke, for being the backbone of the whole community.\n\nMany thanks to NanoBit and Caseus, makers of Axolotl, for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others! \n\nWe are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:\nURL URL\n\nWant to visualize our full dataset? Check out our Nomic Atlas Map.\n <img src=\"URL alt=\"Atlas Nomic Dataset Map\" width=\"400\" height=\"400\" />\n\n\n<a name=\"supported-tasks-and-leaderboards\"></a># Supported Tasks and Leaderboards\n\nThis dataset supports a range of tasks including language modeling, text generation, and text augmentation.\nIt has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.\nFurther information on leaderboards will be updated as they become available.\n\n<a name=\"languages\"></a># Languages\n\nThe language of the data is primarily English.\n\n<a name=\"dataset-structure\"></a># Dataset Structure\n\n<a name=\"data-instances\"></a>",
"passage: ## Data Instances\n\nA data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.\nThe response is then entered into the response field.\n\n<a name=\"data-fields\"></a>## Data Fields\n\nThe fields are:\n1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.\n2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint\n3) 'question', representing a question entry as provided by the FLAN Collection\n4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.\n\n<a name=\"data-splits\"></a>## Data Splits\n\nThe data is unsplit.\n\n<a name=\"dataset-creation\"></a># Dataset Creation\n\n<a name=\"curation-rationale\"></a>## Curation Rationale\n\nThe dataset was created to provide a source of augmented text data for researchers and developers.\nThe datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.\nThis \"reasoning trace\" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.\n\n<a name=\"source-data\"></a>"
] |
d6fca2e4623f031826a2a0ea1905ea852ededcb5
|
# Dataset Card for "ct_scans_90pct_2048_cutoff_falcon"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Rewcifer/ct_scans_90pct_2048_cutoff_falcon
|
[
"region:us"
] |
2023-10-25T02:01:24+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 880984917.8756735, "num_examples": 176406}], "download_size": 166046892, "dataset_size": 880984917.8756735}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T02:01:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ct_scans_90pct_2048_cutoff_falcon"
More Information needed
|
[
"# Dataset Card for \"ct_scans_90pct_2048_cutoff_falcon\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ct_scans_90pct_2048_cutoff_falcon\"\n\nMore Information needed"
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ct_scans_90pct_2048_cutoff_falcon\"\n\nMore Information needed"
] |
14235b0fcdc6213337503ce0c27854fc037291ea
|
# Dataset Card for "7cbe3776"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/7cbe3776
|
[
"region:us"
] |
2023-10-25T02:40:40+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 168, "num_examples": 10}], "download_size": 1340, "dataset_size": 168}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T02:40:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "7cbe3776"
More Information needed
|
[
"# Dataset Card for \"7cbe3776\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"7cbe3776\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"7cbe3776\"\n\nMore Information needed"
] |
7fd073fb12dd5a3a44c796f9447c34eee6b1bfd4
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
xiaopeiwu/hrw_test_binary_flagged_data
|
[
"region:us"
] |
2023-10-25T03:08:28+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]}
|
2023-10-25T03:08:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
8,
24,
6,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
da23b4ab5fc285675794b2a8214562e5b474411d
|
# Dataset Card for "orca-filter2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
thangvip/orca-filter2
|
[
"region:us"
] |
2023-10-25T03:11:38+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1147795895.593268, "num_examples": 1261986}], "download_size": 615068751, "dataset_size": 1147795895.593268}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T03:13:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "orca-filter2"
More Information needed
|
[
"# Dataset Card for \"orca-filter2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"orca-filter2\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"orca-filter2\"\n\nMore Information needed"
] |
d99a8c9ed20c29a6850bbcec5713eab0062082c4
|
# Dataset Card for "slurp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jxie/slurp
|
[
"region:us"
] |
2023-10-25T03:13:20+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2917605666.136, "num_examples": 50628}, {"name": "validation", "num_bytes": 476772454.9, "num_examples": 8690}, {"name": "test", "num_bytes": 708848109.726, "num_examples": 13078}], "download_size": 3913982157, "dataset_size": 4103226230.762}}
|
2023-10-25T03:31:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "slurp"
More Information needed
|
[
"# Dataset Card for \"slurp\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"slurp\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"slurp\"\n\nMore Information needed"
] |
bc6e8498ea01e4889756f3eeb00787087ccb65f8
|
# Dataset Card for "orca-filter-half-open"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
thangvip/orca-filter-half-open
|
[
"region:us"
] |
2023-10-25T03:16:52+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 636502840.4529436, "num_examples": 655016}], "download_size": 338685611, "dataset_size": 636502840.4529436}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-07T07:44:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "orca-filter-half-open"
More Information needed
|
[
"# Dataset Card for \"orca-filter-half-open\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"orca-filter-half-open\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"orca-filter-half-open\"\n\nMore Information needed"
] |
1f74306619534f71836f6ef9143f6debb8697297
|
# Dataset Card for "trec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
getawayfrommeXD/trec
|
[
"region:us"
] |
2023-10-25T03:17:52+00:00
|
{"dataset_info": {"features": [{"name": "label-coarse", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "clean_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 485569, "num_examples": 4952}, {"name": "validation", "num_bytes": 50526, "num_examples": 500}, {"name": "test", "num_bytes": 36238, "num_examples": 500}], "download_size": 0, "dataset_size": 572333}}
|
2023-10-28T06:32:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "trec"
More Information needed
|
[
"# Dataset Card for \"trec\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"trec\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"trec\"\n\nMore Information needed"
] |
e15bc5df2d5998d8bc5323dd44a0a2c10d8e409e
|
# Data V4 for NeurIPS LLM Challenge
Contains 70949 samples collected from Huggingface:
1. Math: 1273
- gsm8k
- math_qa
- math-eval/TAL-SCQ5K
- TAL-SCQ5K-EN
- meta-math/MetaMathQA
- TIGER-Lab/MathInstruct
2. Science: 42513
- lighteval/mmlu - 'all', "split": 'auxiliary_train'
- lighteval/bbq_helm - 'all'
- openbookqa - 'main'
3. ComplexQA: 2940
- ARC-Challenge
- ARC-Easy
- piqa
- social_i_qa
- Muennighoff/babi
- Rowan/hellaswag
4. ComplexQA1: 2060
- medmcqa
- winogrande_xl,
- winogrande_debiased
- boolq
- sciq
5. CNN: 2787
cnn_dailymail - 'article' - 'highlights', version '3.0.0'
6. Oasst: 12771
OpenAssistant/oasst1
7. Dolly: 6605
databricks/databricks-dolly-15k
All data is preprocessed with the following pipeline in [the repo](https://github.com/TokisakiKurumi2001/lit-gpt)
1. collect of data: `prepare_data/select_data.py`
- we used `sentence-transformers/all-mpnet-base-v2` and `Fast Community Detection` for filtering data
2. get at most 5 shot for each sample: `prepare_data/few_shot_sample.py`
3. rearrange data in to fewshot sample: `prepare_data/fewshot_to_prompt.py`
4. prompt data: `prepare_data/prompt_design` \
-> the final data
|
transZ/efficient_llm
|
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"license:mit",
"region:us"
] |
2023-10-25T03:48:14+00:00
|
{"license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"]}
|
2023-11-19T03:50:02+00:00
|
[] |
[] |
TAGS
#task_categories-text-generation #size_categories-100K<n<1M #license-mit #region-us
|
# Data V4 for NeurIPS LLM Challenge
Contains 70949 samples collected from Huggingface:
1. Math: 1273
- gsm8k
- math_qa
- math-eval/TAL-SCQ5K
- TAL-SCQ5K-EN
- meta-math/MetaMathQA
- TIGER-Lab/MathInstruct
2. Science: 42513
- lighteval/mmlu - 'all', "split": 'auxiliary_train'
- lighteval/bbq_helm - 'all'
- openbookqa - 'main'
3. ComplexQA: 2940
- ARC-Challenge
- ARC-Easy
- piqa
- social_i_qa
- Muennighoff/babi
- Rowan/hellaswag
4. ComplexQA1: 2060
- medmcqa
- winogrande_xl,
- winogrande_debiased
- boolq
- sciq
5. CNN: 2787
cnn_dailymail - 'article' - 'highlights', version '3.0.0'
6. Oasst: 12771
OpenAssistant/oasst1
7. Dolly: 6605
databricks/databricks-dolly-15k
All data is preprocessed with the following pipeline in the repo
1. collect of data: 'prepare_data/select_data.py'
- we used 'sentence-transformers/all-mpnet-base-v2' and 'Fast Community Detection' for filtering data
2. get at most 5 shot for each sample: 'prepare_data/few_shot_sample.py'
3. rearrange data in to fewshot sample: 'prepare_data/fewshot_to_prompt.py'
4. prompt data: 'prepare_data/prompt_design' \
-> the final data
|
[
"# Data V4 for NeurIPS LLM Challenge\nContains 70949 samples collected from Huggingface:\n1. Math: 1273\n - gsm8k\n - math_qa\n - math-eval/TAL-SCQ5K\n - TAL-SCQ5K-EN\n - meta-math/MetaMathQA\n - TIGER-Lab/MathInstruct\n2. Science: 42513\n - lighteval/mmlu - 'all', \"split\": 'auxiliary_train'\n - lighteval/bbq_helm - 'all'\n - openbookqa - 'main'\n3. ComplexQA: 2940\n - ARC-Challenge\n - ARC-Easy\n - piqa\n - social_i_qa\n - Muennighoff/babi\n - Rowan/hellaswag\n4. ComplexQA1: 2060\n - medmcqa\n - winogrande_xl,\n - winogrande_debiased\n - boolq\n - sciq\n5. CNN: 2787\n cnn_dailymail - 'article' - 'highlights', version '3.0.0'\n6. Oasst: 12771\n OpenAssistant/oasst1\n7. Dolly: 6605\n databricks/databricks-dolly-15k\n\nAll data is preprocessed with the following pipeline in the repo\n1. collect of data: 'prepare_data/select_data.py'\n - we used 'sentence-transformers/all-mpnet-base-v2' and 'Fast Community Detection' for filtering data\n2. get at most 5 shot for each sample: 'prepare_data/few_shot_sample.py'\n3. rearrange data in to fewshot sample: 'prepare_data/fewshot_to_prompt.py'\n4. prompt data: 'prepare_data/prompt_design' \\\n-> the final data"
] |
[
"TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #license-mit #region-us \n",
"# Data V4 for NeurIPS LLM Challenge\nContains 70949 samples collected from Huggingface:\n1. Math: 1273\n - gsm8k\n - math_qa\n - math-eval/TAL-SCQ5K\n - TAL-SCQ5K-EN\n - meta-math/MetaMathQA\n - TIGER-Lab/MathInstruct\n2. Science: 42513\n - lighteval/mmlu - 'all', \"split\": 'auxiliary_train'\n - lighteval/bbq_helm - 'all'\n - openbookqa - 'main'\n3. ComplexQA: 2940\n - ARC-Challenge\n - ARC-Easy\n - piqa\n - social_i_qa\n - Muennighoff/babi\n - Rowan/hellaswag\n4. ComplexQA1: 2060\n - medmcqa\n - winogrande_xl,\n - winogrande_debiased\n - boolq\n - sciq\n5. CNN: 2787\n cnn_dailymail - 'article' - 'highlights', version '3.0.0'\n6. Oasst: 12771\n OpenAssistant/oasst1\n7. Dolly: 6605\n databricks/databricks-dolly-15k\n\nAll data is preprocessed with the following pipeline in the repo\n1. collect of data: 'prepare_data/select_data.py'\n - we used 'sentence-transformers/all-mpnet-base-v2' and 'Fast Community Detection' for filtering data\n2. get at most 5 shot for each sample: 'prepare_data/few_shot_sample.py'\n3. rearrange data in to fewshot sample: 'prepare_data/fewshot_to_prompt.py'\n4. prompt data: 'prepare_data/prompt_design' \\\n-> the final data"
] |
[
34,
411
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #license-mit #region-us \n# Data V4 for NeurIPS LLM Challenge\nContains 70949 samples collected from Huggingface:\n1. Math: 1273\n - gsm8k\n - math_qa\n - math-eval/TAL-SCQ5K\n - TAL-SCQ5K-EN\n - meta-math/MetaMathQA\n - TIGER-Lab/MathInstruct\n2. Science: 42513\n - lighteval/mmlu - 'all', \"split\": 'auxiliary_train'\n - lighteval/bbq_helm - 'all'\n - openbookqa - 'main'\n3. ComplexQA: 2940\n - ARC-Challenge\n - ARC-Easy\n - piqa\n - social_i_qa\n - Muennighoff/babi\n - Rowan/hellaswag\n4. ComplexQA1: 2060\n - medmcqa\n - winogrande_xl,\n - winogrande_debiased\n - boolq\n - sciq\n5. CNN: 2787\n cnn_dailymail - 'article' - 'highlights', version '3.0.0'\n6. Oasst: 12771\n OpenAssistant/oasst1\n7. Dolly: 6605\n databricks/databricks-dolly-15k\n\nAll data is preprocessed with the following pipeline in the repo\n1. collect of data: 'prepare_data/select_data.py'\n - we used 'sentence-transformers/all-mpnet-base-v2' and 'Fast Community Detection' for filtering data\n2. get at most 5 shot for each sample: 'prepare_data/few_shot_sample.py'\n3. rearrange data in to fewshot sample: 'prepare_data/fewshot_to_prompt.py'\n4. prompt data: 'prepare_data/prompt_design' \\\n-> the final data"
] |
9d693859d4eb4632fa2281f0c1f95f0a2b478afa
|
# Dataset Card for "banel_wit_postag_v0.1.2.3.4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fia24/banel_wit_postag_v0.1.2.3.4
|
[
"region:us"
] |
2023-10-25T04:58:51+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "val", "path": "data/val-*"}]}], "dataset_info": {"features": [{"name": "Inflected_Word", "dtype": "string"}, {"name": "Lemma", "dtype": "string"}, {"name": "POS", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1237478.719008634, "num_examples": 17882}, {"name": "test", "num_bytes": 154736.74173489018, "num_examples": 2236}, {"name": "val", "num_bytes": 154667.53925647563, "num_examples": 2235}], "download_size": 521864, "dataset_size": 1546883.0}}
|
2023-10-25T04:58:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "banel_wit_postag_v0.1.2.3.4"
More Information needed
|
[
"# Dataset Card for \"banel_wit_postag_v0.1.2.3.4\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"banel_wit_postag_v0.1.2.3.4\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"banel_wit_postag_v0.1.2.3.4\"\n\nMore Information needed"
] |
524db4a402a2b8f37ab45028c3df80835dfc4cec
|
This dataset repo contains the dataset (CC3M+CC12M+SBU) translated using opus-mt-en-ar and cleaned. Its size about 13M
|
Arabic-Clip/Arabic_dataset_13M_translated_cleaned_v2_jsonl_format_ViT-B-16-plus-240
|
[
"region:us"
] |
2023-10-25T05:28:50+00:00
|
{}
|
2023-10-27T20:09:55+00:00
|
[] |
[] |
TAGS
#region-us
|
This dataset repo contains the dataset (CC3M+CC12M+SBU) translated using opus-mt-en-ar and cleaned. Its size about 13M
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
692ef58020d79b21f54eb25b15a4813d4f9650d7
|
# Dataset Card for "Aishell1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carlot/AIShell
|
[
"region:us"
] |
2023-10-25T05:51:11+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17298206024.556, "num_examples": 120098}, {"name": "validation", "num_bytes": 2355985522.02, "num_examples": 14326}, {"name": "test", "num_bytes": 1041830607.408, "num_examples": 7176}], "download_size": 20301958805, "dataset_size": 20696022153.984}}
|
2023-10-25T06:11:42+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Aishell1"
More Information needed
|
[
"# Dataset Card for \"Aishell1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Aishell1\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Aishell1\"\n\nMore Information needed"
] |
0471b7d4d707c195465e20f5db027b66dd9d0ca0
|
# Europarl Scandinavian Languages
The data originates from the Europarl parallel corpus, where English transcriptions of parliamentary discussions were aligned
with a number of other languages algorithmically.
In order to align Danish and Swedish corpora in the dataset, English entries were hashed with 128bit Murmurhash3,
and the Danish and Swedish transcriptions were joined on the obtained hash values.
Entries that had more than one pair in the other dataset were removed, this ensures that no false positives due to hash collisions
got into the dataset.
Source code is available in the repository.
The dataset was created for aiding the training of sentence transformer models in the Danish Foundation Models project.
|
kardosdrur/europarl-scandinavian
|
[
"license:mit",
"region:us"
] |
2023-10-25T05:54:03+00:00
|
{"license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "da", "dtype": "string"}, {"name": "en", "dtype": "string"}, {"name": "sv", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 620348322.4, "num_examples": 1304296}, {"name": "test", "num_bytes": 155087080.6, "num_examples": 326074}], "download_size": 488376564, "dataset_size": 775435403.0}}
|
2023-10-25T07:38:29+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
# Europarl Scandinavian Languages
The data originates from the Europarl parallel corpus, where English transcriptions of parliamentary discussions were aligned
with a number of other languages algorithmically.
In order to align Danish and Swedish corpora in the dataset, English entries were hashed with 128bit Murmurhash3,
and the Danish and Swedish transcriptions were joined on the obtained hash values.
Entries that had more than one pair in the other dataset were removed, this ensures that no false positives due to hash collisions
got into the dataset.
Source code is available in the repository.
The dataset was created for aiding the training of sentence transformer models in the Danish Foundation Models project.
|
[
"# Europarl Scandinavian Languages\n\nThe data originates from the Europarl parallel corpus, where English transcriptions of parliamentary discussions were aligned\nwith a number of other languages algorithmically.\n\nIn order to align Danish and Swedish corpora in the dataset, English entries were hashed with 128bit Murmurhash3,\nand the Danish and Swedish transcriptions were joined on the obtained hash values.\nEntries that had more than one pair in the other dataset were removed, this ensures that no false positives due to hash collisions\ngot into the dataset.\nSource code is available in the repository.\n\nThe dataset was created for aiding the training of sentence transformer models in the Danish Foundation Models project."
] |
[
"TAGS\n#license-mit #region-us \n",
"# Europarl Scandinavian Languages\n\nThe data originates from the Europarl parallel corpus, where English transcriptions of parliamentary discussions were aligned\nwith a number of other languages algorithmically.\n\nIn order to align Danish and Swedish corpora in the dataset, English entries were hashed with 128bit Murmurhash3,\nand the Danish and Swedish transcriptions were joined on the obtained hash values.\nEntries that had more than one pair in the other dataset were removed, this ensures that no false positives due to hash collisions\ngot into the dataset.\nSource code is available in the repository.\n\nThe dataset was created for aiding the training of sentence transformer models in the Danish Foundation Models project."
] |
[
11,
159
] |
[
"passage: TAGS\n#license-mit #region-us \n# Europarl Scandinavian Languages\n\nThe data originates from the Europarl parallel corpus, where English transcriptions of parliamentary discussions were aligned\nwith a number of other languages algorithmically.\n\nIn order to align Danish and Swedish corpora in the dataset, English entries were hashed with 128bit Murmurhash3,\nand the Danish and Swedish transcriptions were joined on the obtained hash values.\nEntries that had more than one pair in the other dataset were removed, this ensures that no false positives due to hash collisions\ngot into the dataset.\nSource code is available in the repository.\n\nThe dataset was created for aiding the training of sentence transformer models in the Danish Foundation Models project."
] |
3084fc2aa889136185752eb2b24970d24aa15b3e
|
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed]
|
1NightRaid1/testing
|
[
"task_categories:question-answering",
"size_categories:1K<n<10K",
"license:apache-2.0",
"code",
"region:us"
] |
2023-10-25T06:05:36+00:00
|
{"license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering"], "tags": ["code"]}
|
2023-10-25T06:13:47+00:00
|
[] |
[] |
TAGS
#task_categories-question-answering #size_categories-1K<n<10K #license-apache-2.0 #code #region-us
|
# Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
|
[
"# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
[
"TAGS\n#task_categories-question-answering #size_categories-1K<n<10K #license-apache-2.0 #code #region-us \n",
"# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
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"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
[
40,
34,
4,
40,
29,
3,
4,
9,
6,
5,
7,
4,
7,
10,
9,
5,
9,
8,
10,
46,
8,
7,
10,
5
] |
[
"passage: TAGS\n#task_categories-question-answering #size_categories-1K<n<10K #license-apache-2.0 #code #region-us \n# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
] |
e925023373f265c7f9b295efaecef2ccba5f3afa
|
# LLaVA-Pretrain_Descriptive-Captions
A work of Maxlinn([林知](https://zhihu.com/people/lin-zhi-nlp)), please give credits if you like this work :)
Inspired by [DALLE-3 paper](https://cdn.openai.com/papers/dall-e-3.pdf), descriptive captions are much useful for Text-to-Image models(and possibly Language-Vision Language Models).
We recaptioned LLaVA's pretraining image-text pairs [blip_laion_cc_sbu_558k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain/blob/main/blip_laion_cc_sbu_558k.json) using LLaVA-v1.5-13B. It takes about 48 hours on 16 high-end gpus.
## Usage
Can be used as a drop-in replace of `blip_laion_cc_sbu_558k.json`.
The order of examples, ids, image paths, human questions are all the same. The only difference is the caption in the gpt's turn.
## Example
The original caption case be seen in the prompt.
## Generation Process
To keep the generated descriptive captions faithful but diverse, we use the following user instruction and sampling arguments:
user instructions: asked gpt-4 to write.
```
Please provide a detailed and objective description of the image based on the caption "{short_caption}", focusing only on elements that are fully visible. Do not include any inaccurate, emotional or subjective interpretations. Describe the objects, colors, shapes, and arrangement in the image.
```
sampling arguments: the same as gradio demo of LLaVA-v1.5.
- model precision: fp16
- temperature: 0.2
- max_new_tokens: 512
- top_p: 0.7
## Seen Bias
- `llava-v1.5-13b` loves to use the pattern `the image features...` to describe a image.
- `llava-v1.5-13b` may make some errors in counting and describing texts.
|
Maxlinn/LLaVA-Pretrain_Descriptive-Captions
|
[
"license:cc-by-sa-4.0",
"region:us"
] |
2023-10-25T06:13:07+00:00
|
{"license": "cc-by-sa-4.0"}
|
2023-10-25T06:52:43+00:00
|
[] |
[] |
TAGS
#license-cc-by-sa-4.0 #region-us
|
# LLaVA-Pretrain_Descriptive-Captions
A work of Maxlinn(林知), please give credits if you like this work :)
Inspired by DALLE-3 paper, descriptive captions are much useful for Text-to-Image models(and possibly Language-Vision Language Models).
We recaptioned LLaVA's pretraining image-text pairs blip_laion_cc_sbu_558k.json using LLaVA-v1.5-13B. It takes about 48 hours on 16 high-end gpus.
## Usage
Can be used as a drop-in replace of 'blip_laion_cc_sbu_558k.json'.
The order of examples, ids, image paths, human questions are all the same. The only difference is the caption in the gpt's turn.
## Example
The original caption case be seen in the prompt.
!demo_test
## Generation Process
To keep the generated descriptive captions faithful but diverse, we use the following user instruction and sampling arguments:
user instructions: asked gpt-4 to write.
sampling arguments: the same as gradio demo of LLaVA-v1.5.
- model precision: fp16
- temperature: 0.2
- max_new_tokens: 512
- top_p: 0.7
## Seen Bias
- 'llava-v1.5-13b' loves to use the pattern 'the image features...' to describe a image.
- 'llava-v1.5-13b' may make some errors in counting and describing texts.
|
[
"# LLaVA-Pretrain_Descriptive-Captions\n\nA work of Maxlinn(林知), please give credits if you like this work :)\n\n\nInspired by DALLE-3 paper, descriptive captions are much useful for Text-to-Image models(and possibly Language-Vision Language Models).\n\nWe recaptioned LLaVA's pretraining image-text pairs blip_laion_cc_sbu_558k.json using LLaVA-v1.5-13B. It takes about 48 hours on 16 high-end gpus.",
"## Usage\n\nCan be used as a drop-in replace of 'blip_laion_cc_sbu_558k.json'. \n\nThe order of examples, ids, image paths, human questions are all the same. The only difference is the caption in the gpt's turn.",
"## Example\n\nThe original caption case be seen in the prompt.\n\n!demo_test",
"## Generation Process\n\nTo keep the generated descriptive captions faithful but diverse, we use the following user instruction and sampling arguments:\n\nuser instructions: asked gpt-4 to write.\n\n\n\nsampling arguments: the same as gradio demo of LLaVA-v1.5.\n\n- model precision: fp16\n- temperature: 0.2\n- max_new_tokens: 512\n- top_p: 0.7",
"## Seen Bias\n\n- 'llava-v1.5-13b' loves to use the pattern 'the image features...' to describe a image.\n- 'llava-v1.5-13b' may make some errors in counting and describing texts."
] |
[
"TAGS\n#license-cc-by-sa-4.0 #region-us \n",
"# LLaVA-Pretrain_Descriptive-Captions\n\nA work of Maxlinn(林知), please give credits if you like this work :)\n\n\nInspired by DALLE-3 paper, descriptive captions are much useful for Text-to-Image models(and possibly Language-Vision Language Models).\n\nWe recaptioned LLaVA's pretraining image-text pairs blip_laion_cc_sbu_558k.json using LLaVA-v1.5-13B. It takes about 48 hours on 16 high-end gpus.",
"## Usage\n\nCan be used as a drop-in replace of 'blip_laion_cc_sbu_558k.json'. \n\nThe order of examples, ids, image paths, human questions are all the same. The only difference is the caption in the gpt's turn.",
"## Example\n\nThe original caption case be seen in the prompt.\n\n!demo_test",
"## Generation Process\n\nTo keep the generated descriptive captions faithful but diverse, we use the following user instruction and sampling arguments:\n\nuser instructions: asked gpt-4 to write.\n\n\n\nsampling arguments: the same as gradio demo of LLaVA-v1.5.\n\n- model precision: fp16\n- temperature: 0.2\n- max_new_tokens: 512\n- top_p: 0.7",
"## Seen Bias\n\n- 'llava-v1.5-13b' loves to use the pattern 'the image features...' to describe a image.\n- 'llava-v1.5-13b' may make some errors in counting and describing texts."
] |
[
17,
121,
66,
17,
90,
56
] |
[
"passage: TAGS\n#license-cc-by-sa-4.0 #region-us \n# LLaVA-Pretrain_Descriptive-Captions\n\nA work of Maxlinn(林知), please give credits if you like this work :)\n\n\nInspired by DALLE-3 paper, descriptive captions are much useful for Text-to-Image models(and possibly Language-Vision Language Models).\n\nWe recaptioned LLaVA's pretraining image-text pairs blip_laion_cc_sbu_558k.json using LLaVA-v1.5-13B. It takes about 48 hours on 16 high-end gpus.## Usage\n\nCan be used as a drop-in replace of 'blip_laion_cc_sbu_558k.json'. \n\nThe order of examples, ids, image paths, human questions are all the same. The only difference is the caption in the gpt's turn.## Example\n\nThe original caption case be seen in the prompt.\n\n!demo_test## Generation Process\n\nTo keep the generated descriptive captions faithful but diverse, we use the following user instruction and sampling arguments:\n\nuser instructions: asked gpt-4 to write.\n\n\n\nsampling arguments: the same as gradio demo of LLaVA-v1.5.\n\n- model precision: fp16\n- temperature: 0.2\n- max_new_tokens: 512\n- top_p: 0.7## Seen Bias\n\n- 'llava-v1.5-13b' loves to use the pattern 'the image features...' to describe a image.\n- 'llava-v1.5-13b' may make some errors in counting and describing texts."
] |
5f1eb8698a89d491a868945fa72c8a6800534d29
|
This is the dataset in [OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models](https://arxiv.org/abs/2310.16517)
Abstract:
The emergence of large language models (LLMs) has revolutionized natural language processing tasks.
However, existing instruction-tuning datasets suffer from occupational bias: the majority of data relates to only a few occupations, which hampers the instruction-tuned LLMs to generate helpful responses to professional queries from practitioners in specific fields.
To mitigate this issue and promote occupation-inclusive LLMs, we create an instruction-tuning dataset named OccuQuest, which contains 110,000+ prompt-completion pairs and 30,000+ dialogues covering over 1,000 occupations in 26 occupational categories.
We systematically request ChatGPT, organizing queries hierarchically based on Occupation, Responsibility, Topic, and Question, to ensure a comprehensive coverage of occupational specialty inquiries.
By comparing with three commonly used datasets (Dolly, ShareGPT, and WizardLM), we observe that OccuQuest exhibits a more balanced distribution across occupations.
Furthermore, we assemble three test sets for comprehensive evaluation, an occu-test set covering 25 occupational categories, an estate set focusing on real estate, and an occu-quora set containing real-world questions from Quora.
We then fine-tune LLaMA on OccuQuest to obtain OccuLLaMA, which significantly outperforms state-of-the-art LLaMA variants (Vicuna, Tulu, and WizardLM) on professional questions in GPT-4 and human evaluations.
Notably, on the occu-quora set, OccuLLaMA reaches a high win rate of 86.4\% against WizardLM.
Furthermore, we demonstrate the potential of combining OccuQuest with other instruction-tuning datasets to enhance the overall performance of LLMs.
By fine-tuning LLaMA on a mixture of OccuQuest and Tulu datasets, we introduce ProLLaMA, which excels in addressing occupational questions and exhibits superior performance in comprehensive evaluations such as MMLU, GSM8K, BBH, and HumanEval.
Among the different LLaMA variants, the 7B and 13B ProLLaMA models achieve the highest performance on MMLU and GSM8K, with the 7B ProLLaMA model demonstrating an improvement of more than 4 points over the other 7B variants on GSM8K.
We open release the dataset and models.
Please cite if you use this dataset:
```
@misc{xue2023occuquest,
title={OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models},
author={Mingfeng Xue and Dayiheng Liu and Kexin Yang and Guanting Dong and Wenqiang Lei and Zheng Yuan and Chang Zhou and Jingren Zhou},
year={2023},
eprint={2310.16517},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
OFA-Sys/OccuQuest
|
[
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"sft",
"occupational bias",
"arxiv:2310.16517",
"region:us"
] |
2023-10-25T07:29:51+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "dialog", "path": "data/dialog-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}, {"split": "estate", "path": "data/estate-*"}, {"split": "quora", "path": "data/quora-*"}]}], "dataset_info": {"features": [{"name": "category", "dtype": "string"}, {"name": "occupation", "dtype": "string"}, {"name": "topic", "dtype": "string"}, {"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 330314955, "num_examples": 114090}, {"name": "dialog", "num_bytes": 102158907, "num_examples": 31682}, {"name": "validation", "num_bytes": 7314741, "num_examples": 2500}, {"name": "test", "num_bytes": 718046, "num_examples": 250}, {"name": "estate", "num_bytes": 703613, "num_examples": 250}, {"name": "quora", "num_bytes": 45540, "num_examples": 250}], "download_size": 184974294, "dataset_size": 441255802}, "tags": ["sft", "occupational bias"]}
|
2023-10-26T02:18:10+00:00
|
[
"2310.16517"
] |
[
"en"
] |
TAGS
#size_categories-100K<n<1M #language-English #license-apache-2.0 #sft #occupational bias #arxiv-2310.16517 #region-us
|
This is the dataset in OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models
Abstract:
The emergence of large language models (LLMs) has revolutionized natural language processing tasks.
However, existing instruction-tuning datasets suffer from occupational bias: the majority of data relates to only a few occupations, which hampers the instruction-tuned LLMs to generate helpful responses to professional queries from practitioners in specific fields.
To mitigate this issue and promote occupation-inclusive LLMs, we create an instruction-tuning dataset named OccuQuest, which contains 110,000+ prompt-completion pairs and 30,000+ dialogues covering over 1,000 occupations in 26 occupational categories.
We systematically request ChatGPT, organizing queries hierarchically based on Occupation, Responsibility, Topic, and Question, to ensure a comprehensive coverage of occupational specialty inquiries.
By comparing with three commonly used datasets (Dolly, ShareGPT, and WizardLM), we observe that OccuQuest exhibits a more balanced distribution across occupations.
Furthermore, we assemble three test sets for comprehensive evaluation, an occu-test set covering 25 occupational categories, an estate set focusing on real estate, and an occu-quora set containing real-world questions from Quora.
We then fine-tune LLaMA on OccuQuest to obtain OccuLLaMA, which significantly outperforms state-of-the-art LLaMA variants (Vicuna, Tulu, and WizardLM) on professional questions in GPT-4 and human evaluations.
Notably, on the occu-quora set, OccuLLaMA reaches a high win rate of 86.4\% against WizardLM.
Furthermore, we demonstrate the potential of combining OccuQuest with other instruction-tuning datasets to enhance the overall performance of LLMs.
By fine-tuning LLaMA on a mixture of OccuQuest and Tulu datasets, we introduce ProLLaMA, which excels in addressing occupational questions and exhibits superior performance in comprehensive evaluations such as MMLU, GSM8K, BBH, and HumanEval.
Among the different LLaMA variants, the 7B and 13B ProLLaMA models achieve the highest performance on MMLU and GSM8K, with the 7B ProLLaMA model demonstrating an improvement of more than 4 points over the other 7B variants on GSM8K.
We open release the dataset and models.
Please cite if you use this dataset:
|
[] |
[
"TAGS\n#size_categories-100K<n<1M #language-English #license-apache-2.0 #sft #occupational bias #arxiv-2310.16517 #region-us \n"
] |
[
47
] |
[
"passage: TAGS\n#size_categories-100K<n<1M #language-English #license-apache-2.0 #sft #occupational bias #arxiv-2310.16517 #region-us \n"
] |
681c304e4d3361a5b807be955fa63b474352341f
|
# Dataset Card for "gcc12m_caption_only"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Geonmo/gcc12m_caption_only
|
[
"region:us"
] |
2023-10-25T07:32:24+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1329443791, "num_examples": 12423374}], "download_size": 943024335, "dataset_size": 1329443791}}
|
2023-10-25T07:40:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "gcc12m_caption_only"
More Information needed
|
[
"# Dataset Card for \"gcc12m_caption_only\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"gcc12m_caption_only\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"gcc12m_caption_only\"\n\nMore Information needed"
] |
ec8073d86c927012e05a2c8d5d265267b540ff71
|
# Dataset Card for "summary-text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Back-up/summary-text
|
[
"region:us"
] |
2023-10-25T07:41:10+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 181566694, "num_examples": 65361}, {"name": "test", "num_bytes": 2753008, "num_examples": 1000}], "download_size": 99917401, "dataset_size": 184319702}}
|
2023-10-25T07:52:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "summary-text"
More Information needed
|
[
"# Dataset Card for \"summary-text\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"summary-text\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"summary-text\"\n\nMore Information needed"
] |
05a52e4d3079675d49279ce61ea5cf1050ebb197
|
This dataset contains 70,000 synthetic images from the paper "A critical assessment of generative models for synthetic data augmentation on limited pneumonia X-ray data". It consists of synthetically generated X-ray images of bacterial, viral, fungal, and COVID-19 pneumonia, as well as healthy patients with no findings. CAUTION: the images are not real and do not depict real patients. Do not use for medical purposes!
|
dschaudt42/synthetic_pneumonia
|
[
"region:us"
] |
2023-10-25T07:46:23+00:00
|
{}
|
2023-10-25T12:22:12+00:00
|
[] |
[] |
TAGS
#region-us
|
This dataset contains 70,000 synthetic images from the paper "A critical assessment of generative models for synthetic data augmentation on limited pneumonia X-ray data". It consists of synthetically generated X-ray images of bacterial, viral, fungal, and COVID-19 pneumonia, as well as healthy patients with no findings. CAUTION: the images are not real and do not depict real patients. Do not use for medical purposes!
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
9700b53a330bc75dc82bc1097bfd0563fef56d6d
|
# Japanese Knowledge Base
JKB (Japanese Knowledge Base) is a work-in-progress question and answer dataset on the Japanese language.
The goal of JKB is to provide training data for large language models to better answer questions about the Japanese language, particularly for learners of Japanese.
## The dataset contains topics about:
* N5 to N1 grammar
* Structure and word order
* The writing systems
* Word types
* Particles and the commonly confused ones
* Conjugation
* Verbs and verb types
* Adjectives and adjective types
* Formality
* Name suffixes
## The dataset will not focus on:
* Translation
* Definitions of words
* Sentence nuance
## Sample
```
Question: What is a common usage scenario for 〜たり〜たり?
Answer: The 〜たり〜たり structure is for listing actions as examples among various possibilities. It is commonly used to describe routines or plans. For example, one might use it to describe what they typically do on their days off or what they plan to do during a vacation.
Question: How do you use 思う with na-adjectives?
Answer: For na-adjectives, you can attach だ to the adjective in its plain form and then add と思う. For example, if you think someone is happy, you could say "幸せだと思います".
Question: How do you use と思う with i-adjectives?
Answer: To use 〜と思う with i-adjectives, you simply take the i-adjective in its plain form and attach と思う to it. For example, if you think something is hot (熱い), you can say "熱いと思う".
Question: Is it possible to use 〜たい in a question?
Answer: Yes, you can use 〜たい to ask about someone's desires or wishes, but be cautious when using it with superiors or elders as it can be considered impolite.
```
## Citation
This dataset uses the `Creative Commons Attribution Non Commercial 4.0` licence which means you may use my dataset for non-commercial purposes and you must give credit.
```
@misc{Japanese Knowledge Base,
title = {Japanese Knowledge Base: A question and answer dataset on the Japanese language},
author = {JawGBoi},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/datasets/joujiboi/japanese-knowledge-base}},
}
```
|
joujiboi/japanese-knowledge-base
|
[
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-4.0",
"language",
"japanese language",
"nihongo",
"日本語",
"region:us"
] |
2023-10-25T07:59:41+00:00
|
{"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering", "text-generation"], "pretty_name": "Japanese Knowledge Base", "tags": ["language", "japanese language", "nihongo", "\u65e5\u672c\u8a9e"]}
|
2023-10-25T08:16:47+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-question-answering #task_categories-text-generation #size_categories-1K<n<10K #language-English #license-cc-by-nc-4.0 #language #japanese language #nihongo #日本語 #region-us
|
# Japanese Knowledge Base
JKB (Japanese Knowledge Base) is a work-in-progress question and answer dataset on the Japanese language.
The goal of JKB is to provide training data for large language models to better answer questions about the Japanese language, particularly for learners of Japanese.
## The dataset contains topics about:
* N5 to N1 grammar
* Structure and word order
* The writing systems
* Word types
* Particles and the commonly confused ones
* Conjugation
* Verbs and verb types
* Adjectives and adjective types
* Formality
* Name suffixes
## The dataset will not focus on:
* Translation
* Definitions of words
* Sentence nuance
## Sample
This dataset uses the 'Creative Commons Attribution Non Commercial 4.0' licence which means you may use my dataset for non-commercial purposes and you must give credit.
|
[
"# Japanese Knowledge Base\n\nJKB (Japanese Knowledge Base) is a work-in-progress question and answer dataset on the Japanese language.\n\nThe goal of JKB is to provide training data for large language models to better answer questions about the Japanese language, particularly for learners of Japanese.",
"## The dataset contains topics about:\n* N5 to N1 grammar\n* Structure and word order\n* The writing systems\n* Word types\n* Particles and the commonly confused ones\n* Conjugation\n* Verbs and verb types\n* Adjectives and adjective types\n* Formality\n* Name suffixes",
"## The dataset will not focus on:\n* Translation\n* Definitions of words\n* Sentence nuance",
"## Sample\n\n\nThis dataset uses the 'Creative Commons Attribution Non Commercial 4.0' licence which means you may use my dataset for non-commercial purposes and you must give credit."
] |
[
"TAGS\n#task_categories-question-answering #task_categories-text-generation #size_categories-1K<n<10K #language-English #license-cc-by-nc-4.0 #language #japanese language #nihongo #日本語 #region-us \n",
"# Japanese Knowledge Base\n\nJKB (Japanese Knowledge Base) is a work-in-progress question and answer dataset on the Japanese language.\n\nThe goal of JKB is to provide training data for large language models to better answer questions about the Japanese language, particularly for learners of Japanese.",
"## The dataset contains topics about:\n* N5 to N1 grammar\n* Structure and word order\n* The writing systems\n* Word types\n* Particles and the commonly confused ones\n* Conjugation\n* Verbs and verb types\n* Adjectives and adjective types\n* Formality\n* Name suffixes",
"## The dataset will not focus on:\n* Translation\n* Definitions of words\n* Sentence nuance",
"## Sample\n\n\nThis dataset uses the 'Creative Commons Attribution Non Commercial 4.0' licence which means you may use my dataset for non-commercial purposes and you must give credit."
] |
[
68,
61,
68,
21,
42
] |
[
"passage: TAGS\n#task_categories-question-answering #task_categories-text-generation #size_categories-1K<n<10K #language-English #license-cc-by-nc-4.0 #language #japanese language #nihongo #日本語 #region-us \n# Japanese Knowledge Base\n\nJKB (Japanese Knowledge Base) is a work-in-progress question and answer dataset on the Japanese language.\n\nThe goal of JKB is to provide training data for large language models to better answer questions about the Japanese language, particularly for learners of Japanese.## The dataset contains topics about:\n* N5 to N1 grammar\n* Structure and word order\n* The writing systems\n* Word types\n* Particles and the commonly confused ones\n* Conjugation\n* Verbs and verb types\n* Adjectives and adjective types\n* Formality\n* Name suffixes## The dataset will not focus on:\n* Translation\n* Definitions of words\n* Sentence nuance## Sample\n\n\nThis dataset uses the 'Creative Commons Attribution Non Commercial 4.0' licence which means you may use my dataset for non-commercial purposes and you must give credit."
] |
33bbc2e295fba2f1b8e41bd4c2676766dae740ce
|
# Dataset Card for "helm-samsum-dolly-lima-cot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quyanh/helm-samsum-dolly-lima-cot
|
[
"region:us"
] |
2023-10-25T08:07:09+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28230011.540207386, "num_examples": 30963}], "download_size": 19770554, "dataset_size": 28230011.540207386}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T08:11:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "helm-samsum-dolly-lima-cot"
More Information needed
|
[
"# Dataset Card for \"helm-samsum-dolly-lima-cot\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"helm-samsum-dolly-lima-cot\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"helm-samsum-dolly-lima-cot\"\n\nMore Information needed"
] |
5c0b992d78857a22c9aa0c84eb53ca146f83bf91
|
# Dataset Card for "midjourney-prompts-only"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Geonmo/midjourney-prompts-only
|
[
"region:us"
] |
2023-10-25T08:10:06+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 495800699, "num_examples": 3492544}], "download_size": 334934157, "dataset_size": 495800699}}
|
2023-10-25T08:12:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "midjourney-prompts-only"
More Information needed
|
[
"# Dataset Card for \"midjourney-prompts-only\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"midjourney-prompts-only\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"midjourney-prompts-only\"\n\nMore Information needed"
] |
0521456485714462337653925b69731e326816bc
|
# Matryoshka Diffusion Models - paper examples
This dataset contains the 1024x1024 images included in the [Matryoshka Diffusion Models
](https://huggingface.co/papers/2310.15111) paper.
Arxiv: https://arxiv.org/abs/2310.15111
|
multimodalart/matryoshka-diffusion-models-paper-examples
|
[
"license:mit",
"arxiv:2310.15111",
"region:us"
] |
2023-10-25T08:10:13+00:00
|
{"license": "mit"}
|
2023-10-26T07:48:38+00:00
|
[
"2310.15111"
] |
[] |
TAGS
#license-mit #arxiv-2310.15111 #region-us
|
# Matryoshka Diffusion Models - paper examples
This dataset contains the 1024x1024 images included in the Matryoshka Diffusion Models
paper.
Arxiv: URL
|
[
"# Matryoshka Diffusion Models - paper examples\n\nThis dataset contains the 1024x1024 images included in the Matryoshka Diffusion Models\n paper.\n\nArxiv: URL"
] |
[
"TAGS\n#license-mit #arxiv-2310.15111 #region-us \n",
"# Matryoshka Diffusion Models - paper examples\n\nThis dataset contains the 1024x1024 images included in the Matryoshka Diffusion Models\n paper.\n\nArxiv: URL"
] |
[
20,
44
] |
[
"passage: TAGS\n#license-mit #arxiv-2310.15111 #region-us \n# Matryoshka Diffusion Models - paper examples\n\nThis dataset contains the 1024x1024 images included in the Matryoshka Diffusion Models\n paper.\n\nArxiv: URL"
] |
b7a9581125eebf5b2d655dfff63de0008c2db144
|
# Dataset Card for "friends-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rjaiswal/friends-dataset
|
[
"region:us"
] |
2023-10-25T08:35:10+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 217527103.0, "num_examples": 30}], "download_size": 217511845, "dataset_size": 217527103.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T15:32:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "friends-dataset"
More Information needed
|
[
"# Dataset Card for \"friends-dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"friends-dataset\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"friends-dataset\"\n\nMore Information needed"
] |
43a9a8841794651e08e317925b9c16c2fad549da
|
# European Residential Building Dataset
*This dataset is not released and only serves as a temporary placeholder for academic reviewers.*
The European Residential Building dataset consists of 209,318 images of detached residential buildings sampled at random across the Netherlands and Denmark.
We refer to our paper for more information on the dataset.
|
anon345/ERBD
|
[
"license:other",
"region:us"
] |
2023-10-25T08:35:57+00:00
|
{"license": "other", "license_name": "under-review", "license_link": "LICENSE"}
|
2023-10-25T08:41:06+00:00
|
[] |
[] |
TAGS
#license-other #region-us
|
# European Residential Building Dataset
*This dataset is not released and only serves as a temporary placeholder for academic reviewers.*
The European Residential Building dataset consists of 209,318 images of detached residential buildings sampled at random across the Netherlands and Denmark.
We refer to our paper for more information on the dataset.
|
[
"# European Residential Building Dataset\n\n*This dataset is not released and only serves as a temporary placeholder for academic reviewers.*\n\nThe European Residential Building dataset consists of 209,318 images of detached residential buildings sampled at random across the Netherlands and Denmark.\n\nWe refer to our paper for more information on the dataset."
] |
[
"TAGS\n#license-other #region-us \n",
"# European Residential Building Dataset\n\n*This dataset is not released and only serves as a temporary placeholder for academic reviewers.*\n\nThe European Residential Building dataset consists of 209,318 images of detached residential buildings sampled at random across the Netherlands and Denmark.\n\nWe refer to our paper for more information on the dataset."
] |
[
11,
76
] |
[
"passage: TAGS\n#license-other #region-us \n# European Residential Building Dataset\n\n*This dataset is not released and only serves as a temporary placeholder for academic reviewers.*\n\nThe European Residential Building dataset consists of 209,318 images of detached residential buildings sampled at random across the Netherlands and Denmark.\n\nWe refer to our paper for more information on the dataset."
] |
803593504978e4cd21a8c28d5e490046a0e344b5
|
# Dataset Card for Lex.dk Open
## Dataset Description
- **Point of Contact:** [Dan Saattrup Nielsen](mailto:[email protected])
- **Size of downloaded dataset files:** 10.05 MB
- **Size of the generated dataset:** 18.34 MB
- **Total amount of disk used:** 28.39 MB
### Dataset Summary
This dataset consists of articles from the Danish encyclopedia [Lex.dk](https://www.lex.dk).
Only the articles released with a permissive license are included here, which constitutes about 7.5% of the total amount of articles.
### Languages
The dataset is available in Danish (`da`).
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 10.05 MB
- **Size of the generated dataset:** 18.34 MB
- **Total amount of disk used:** 28.39 MB
An example from the dataset looks as follows.
```
{
'url': 'https://denstoredanske.lex.dk/Kullmanns_M%C3%B8lle',
'title': 'Kullmanns Mølle',
'clarification': '',
'authors': ['https://brugere.lex.dk/6929'],
'date': '2021-01-20T13:23:20+01:00',
'license': 'fri anvendelse',
'text': 'Kullmanns Mølle er en mølle i Gudhjem, opkaldt efter Matts Kullmann, der byggede møllen i 1893 til sin søn, Christian Kullmann, se Gudhjem Mølle.'
}
```
### Data Fields
The data fields are the same among all splits.
- `url`: a `string` feature.
- `title`: a `string` feature.
- `clarification`: a `string` feature.
- `authors`: a `list` feature.
- `authors`: a `string` feature.
- `license`: a `string` feature.
- `text`: a `string` feature.
### Dataset Statistics
There are 11,887 samples in the dataset.
#### Article Length Distribution
## Additional Information
### Dataset Curators
[Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra
Institute](https://alexandra.dk/) built the dataset and uploaded it to the Hugging Face Hub.
### Licensing Information
The dataset is licensed under the [CC0
license](https://creativecommons.org/share-your-work/public-domain/cc0/).
|
alexandrainst/lexdk-open
|
[
"size_categories:10K<n<100K",
"language:da",
"license:cc0-1.0",
"region:us"
] |
2023-10-25T08:36:11+00:00
|
{"language": ["da"], "license": "cc0-1.0", "size_categories": ["10K<n<100K"], "pretty_name": "Lex.dk Open", "dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "clarification", "dtype": "string"}, {"name": "authors", "sequence": "string"}, {"name": "date", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18335490, "num_examples": 11887}], "download_size": 10050922, "dataset_size": 18335490}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-27T19:22:31+00:00
|
[] |
[
"da"
] |
TAGS
#size_categories-10K<n<100K #language-Danish #license-cc0-1.0 #region-us
|
# Dataset Card for URL Open
## Dataset Description
- Point of Contact: Dan Saattrup Nielsen
- Size of downloaded dataset files: 10.05 MB
- Size of the generated dataset: 18.34 MB
- Total amount of disk used: 28.39 MB
### Dataset Summary
This dataset consists of articles from the Danish encyclopedia URL.
Only the articles released with a permissive license are included here, which constitutes about 7.5% of the total amount of articles.
### Languages
The dataset is available in Danish ('da').
## Dataset Structure
### Data Instances
- Size of downloaded dataset files: 10.05 MB
- Size of the generated dataset: 18.34 MB
- Total amount of disk used: 28.39 MB
An example from the dataset looks as follows.
### Data Fields
The data fields are the same among all splits.
- 'url': a 'string' feature.
- 'title': a 'string' feature.
- 'clarification': a 'string' feature.
- 'authors': a 'list' feature.
- 'authors': a 'string' feature.
- 'license': a 'string' feature.
- 'text': a 'string' feature.
### Dataset Statistics
There are 11,887 samples in the dataset.
#### Article Length Distribution
!image/png
## Additional Information
### Dataset Curators
Dan Saattrup Nielsen from the The Alexandra
Institute built the dataset and uploaded it to the Hugging Face Hub.
### Licensing Information
The dataset is licensed under the CC0
license.
|
[
"# Dataset Card for URL Open",
"## Dataset Description\n\n- Point of Contact: Dan Saattrup Nielsen\n- Size of downloaded dataset files: 10.05 MB\n- Size of the generated dataset: 18.34 MB\n- Total amount of disk used: 28.39 MB",
"### Dataset Summary\n\nThis dataset consists of articles from the Danish encyclopedia URL. \n\nOnly the articles released with a permissive license are included here, which constitutes about 7.5% of the total amount of articles.",
"### Languages\n\nThe dataset is available in Danish ('da').",
"## Dataset Structure",
"### Data Instances\n\n- Size of downloaded dataset files: 10.05 MB\n- Size of the generated dataset: 18.34 MB\n- Total amount of disk used: 28.39 MB\n\nAn example from the dataset looks as follows.",
"### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'url': a 'string' feature.\n- 'title': a 'string' feature.\n- 'clarification': a 'string' feature.\n- 'authors': a 'list' feature.\n- 'authors': a 'string' feature.\n- 'license': a 'string' feature.\n- 'text': a 'string' feature.",
"### Dataset Statistics\n\nThere are 11,887 samples in the dataset.",
"#### Article Length Distribution\n\n!image/png",
"## Additional Information",
"### Dataset Curators\n\nDan Saattrup Nielsen from the The Alexandra\nInstitute built the dataset and uploaded it to the Hugging Face Hub.",
"### Licensing Information\n\nThe dataset is licensed under the CC0\nlicense."
] |
[
"TAGS\n#size_categories-10K<n<100K #language-Danish #license-cc0-1.0 #region-us \n",
"# Dataset Card for URL Open",
"## Dataset Description\n\n- Point of Contact: Dan Saattrup Nielsen\n- Size of downloaded dataset files: 10.05 MB\n- Size of the generated dataset: 18.34 MB\n- Total amount of disk used: 28.39 MB",
"### Dataset Summary\n\nThis dataset consists of articles from the Danish encyclopedia URL. \n\nOnly the articles released with a permissive license are included here, which constitutes about 7.5% of the total amount of articles.",
"### Languages\n\nThe dataset is available in Danish ('da').",
"## Dataset Structure",
"### Data Instances\n\n- Size of downloaded dataset files: 10.05 MB\n- Size of the generated dataset: 18.34 MB\n- Total amount of disk used: 28.39 MB\n\nAn example from the dataset looks as follows.",
"### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'url': a 'string' feature.\n- 'title': a 'string' feature.\n- 'clarification': a 'string' feature.\n- 'authors': a 'list' feature.\n- 'authors': a 'string' feature.\n- 'license': a 'string' feature.\n- 'text': a 'string' feature.",
"### Dataset Statistics\n\nThere are 11,887 samples in the dataset.",
"#### Article Length Distribution\n\n!image/png",
"## Additional Information",
"### Dataset Curators\n\nDan Saattrup Nielsen from the The Alexandra\nInstitute built the dataset and uploaded it to the Hugging Face Hub.",
"### Licensing Information\n\nThe dataset is licensed under the CC0\nlicense."
] |
[
31,
7,
48,
46,
16,
6,
51,
101,
18,
11,
5,
31,
18
] |
[
"passage: TAGS\n#size_categories-10K<n<100K #language-Danish #license-cc0-1.0 #region-us \n# Dataset Card for URL Open## Dataset Description\n\n- Point of Contact: Dan Saattrup Nielsen\n- Size of downloaded dataset files: 10.05 MB\n- Size of the generated dataset: 18.34 MB\n- Total amount of disk used: 28.39 MB### Dataset Summary\n\nThis dataset consists of articles from the Danish encyclopedia URL. \n\nOnly the articles released with a permissive license are included here, which constitutes about 7.5% of the total amount of articles.### Languages\n\nThe dataset is available in Danish ('da').## Dataset Structure### Data Instances\n\n- Size of downloaded dataset files: 10.05 MB\n- Size of the generated dataset: 18.34 MB\n- Total amount of disk used: 28.39 MB\n\nAn example from the dataset looks as follows.### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'url': a 'string' feature.\n- 'title': a 'string' feature.\n- 'clarification': a 'string' feature.\n- 'authors': a 'list' feature.\n- 'authors': a 'string' feature.\n- 'license': a 'string' feature.\n- 'text': a 'string' feature.### Dataset Statistics\n\nThere are 11,887 samples in the dataset.#### Article Length Distribution\n\n!image/png## Additional Information### Dataset Curators\n\nDan Saattrup Nielsen from the The Alexandra\nInstitute built the dataset and uploaded it to the Hugging Face Hub.### Licensing Information\n\nThe dataset is licensed under the CC0\nlicense."
] |
4195123cd5963227993f224fc3c15174d587e69e
|
# Dataset Card for "keyword_use_unuse"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fiveflow/keyword_use_unuse
|
[
"region:us"
] |
2023-10-25T08:46:43+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 632930, "num_examples": 948}], "download_size": 177602, "dataset_size": 632930}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-25T12:39:44+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "keyword_use_unuse"
More Information needed
|
[
"# Dataset Card for \"keyword_use_unuse\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"keyword_use_unuse\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"keyword_use_unuse\"\n\nMore Information needed"
] |
46195dac8f30b87a81580a38c13c830cf20b2504
|
# Dataset Card for CodeSearchNet Files
|
andstor/code_search_net_files
|
[
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:code",
"license:other",
"region:us"
] |
2023-10-25T09:09:45+00:00
|
{"annotations_creators": ["no-annotation"], "language_creators": ["machine-generated"], "language": ["code"], "license": ["other"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M", "10K<n<100K", "1M<n<10M"], "source_datasets": ["original"], "dataset_info": [{"config_name": "all", "features": [{"name": "repository_name", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "contents", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4597566968, "num_examples": 481456}, {"name": "test", "num_bytes": 244185049, "num_examples": 27032}, {"name": "validation", "num_bytes": 208172780, "num_examples": 23262}], "download_size": 5328510079, "dataset_size": 5049924797}, {"config_name": "java", "features": [{"name": "repository_name", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "contents", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1508021640, "num_examples": 124201}, {"name": "test", "num_bytes": 83498294, "num_examples": 7672}, {"name": "validation", "num_bytes": 49495187, "num_examples": 4622}], "download_size": 1719645289, "dataset_size": 1641015121}, {"config_name": "go", "features": [{"name": "repository_name", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "contents", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 445935389, "num_examples": 52467}, {"name": "test", "num_bytes": 22662672, "num_examples": 2661}, {"name": "validation", "num_bytes": 14949171, "num_examples": 1991}], "download_size": 525469198, "dataset_size": 483547232}, {"config_name": "python", "features": [{"name": "repository_name", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "contents", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 966000949, "num_examples": 83739}, {"name": "test", "num_bytes": 53223810, "num_examples": 4989}, {"name": "validation", "num_bytes": 51072608, "num_examples": 4589}], "download_size": 1117084868, "dataset_size": 1070297367}, {"config_name": "javascript", "features": [{"name": "repository_name", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "contents", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 533231855, "num_examples": 40913}, {"name": "test", "num_bytes": 28571674, "num_examples": 2176}, {"name": "validation", "num_bytes": 36688764, "num_examples": 2586}], "download_size": 633172343, "dataset_size": 598492293}, {"config_name": "ruby", "features": [{"name": "repository_name", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "contents", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 132300659, "num_examples": 16932}, {"name": "test", "num_bytes": 5125555, "num_examples": 822}, {"name": "validation", "num_bytes": 4189342, "num_examples": 803}], "download_size": 147603200, "dataset_size": 141615556}, {"config_name": "php", "features": [{"name": "repository_name", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "contents", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1012076476, "num_examples": 163204}, {"name": "test", "num_bytes": 51103044, "num_examples": 8712}, {"name": "validation", "num_bytes": 51777708, "num_examples": 8671}], "download_size": 1185535181, "dataset_size": 1114957228}]}
|
2023-11-03T15:38:16+00:00
|
[] |
[
"code"
] |
TAGS
#annotations_creators-no-annotation #language_creators-machine-generated #multilinguality-multilingual #size_categories-100K<n<1M #size_categories-10K<n<100K #size_categories-1M<n<10M #source_datasets-original #language-code #license-other #region-us
|
# Dataset Card for CodeSearchNet Files
|
[
"# Dataset Card for CodeSearchNet Files"
] |
[
"TAGS\n#annotations_creators-no-annotation #language_creators-machine-generated #multilinguality-multilingual #size_categories-100K<n<1M #size_categories-10K<n<100K #size_categories-1M<n<10M #source_datasets-original #language-code #license-other #region-us \n",
"# Dataset Card for CodeSearchNet Files"
] |
[
91,
10
] |
[
"passage: TAGS\n#annotations_creators-no-annotation #language_creators-machine-generated #multilinguality-multilingual #size_categories-100K<n<1M #size_categories-10K<n<100K #size_categories-1M<n<10M #source_datasets-original #language-code #license-other #region-us \n# Dataset Card for CodeSearchNet Files"
] |
5c0b5391eadf2d146204bb43a42815ea223094c4
|
# Abalone
The [Medieval Latin](https://openportal.isti.cnr.it/doc?id=people______::37b90c87470ef85c78e72b8a3c753293) authorship attribution dataset.
Which Victorian author wrote the given text?
Note: Only epistolas are included in this version of the dataset.
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|---------------------------------------------------------------|
| authorship | Classification | Which Latin author wrote the given text?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/medieval_latin", "authorship")["train"]
```
# Features
|**Feature** |**Type** |
|-------------------|---------------|
| content | `[string]` |
| epistola | `[string]` |
| author | `[string]` |
# Citation
Cite this dataset as
```
@techreport{oai:it.cnr:prodotti:438795,
title = {MedLatin1 and MedLatin2: Two Datasets for the Computational Authorship Analysis of Medieval Latin Texts},
author = {Corbara S. and Moreo A. and Sebastiani F. and Tavoni M.},
institution = {Research report, 2020},
year = {2020}
}
```
|
mstz/medieval_latin
|
[
"task_categories:text-classification",
"size_categories:100<n<1K",
"language:en",
"medieval",
"latin",
"text-classification",
"region:us"
] |
2023-10-25T09:26:51+00:00
|
{"language": ["en"], "size_categories": ["100<n<1K"], "task_categories": ["text-classification"], "pretty_name": "Abalone", "tags": ["medieval", "latin", "text-classification"]}
|
2023-10-26T13:11:10+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-classification #size_categories-100<n<1K #language-English #medieval #latin #text-classification #region-us
|
Abalone
=======
The Medieval Latin authorship attribution dataset.
Which Victorian author wrote the given text?
Note: Only epistolas are included in this version of the dataset.
Configurations and tasks
========================
Configuration: authorship, Task: Classification, Description: Which Latin author wrote the given text?
Usage
=====
Features
========
Cite this dataset as
|
[] |
[
"TAGS\n#task_categories-text-classification #size_categories-100<n<1K #language-English #medieval #latin #text-classification #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-classification #size_categories-100<n<1K #language-English #medieval #latin #text-classification #region-us \n"
] |
4e40e6a45fd9c3a45badb682085553a72240dfe9
|
# Dataset Card for "reward_model_ranking_dataset_RLHF"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sayan1101/reward_model_ranking_dataset_RLHF
|
[
"region:us"
] |
2023-10-25T09:40:53+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "valid1", "path": "data/valid1-*"}, {"split": "valid2", "path": "data/valid2-*"}]}], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "summary_3", "dtype": "string"}, {"name": "summary_4", "dtype": "string"}, {"name": "summary_1", "dtype": "string"}, {"name": "summary_2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 260765, "num_examples": 350}, {"name": "test", "num_bytes": 260765, "num_examples": 350}, {"name": "valid1", "num_bytes": 260765, "num_examples": 350}, {"name": "valid2", "num_bytes": 260765, "num_examples": 350}], "download_size": 0, "dataset_size": 1043060}}
|
2023-10-25T10:04:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "reward_model_ranking_dataset_RLHF"
More Information needed
|
[
"# Dataset Card for \"reward_model_ranking_dataset_RLHF\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"reward_model_ranking_dataset_RLHF\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"reward_model_ranking_dataset_RLHF\"\n\nMore Information needed"
] |
719a1bbbcd79abe70fffcaaf280aedc717e8ae2b
|
# NOAA Buoy meterological data
NOAA Buoy Data was downloaded, processed, and cleaned for tasks pertaining to tabular data. The data consists of meteorological measurements. There are two datasets
1. From 1980 through 2022 (denoted with "years" in file names)
2. From Jan 2023 through end of Sept 2023 (denoted with "2023" in file names)
The original intended use is for anomaly detection in tabular data.
## Dataset Details
### Dataset Description
This dataset contains weather buoy data to be used in a tabular embedding scenarios.
Buoy 42002 was chosen because it had many years of historical data and was still actively collecting information
Here is the buoy's page and its historical data page:
- https://www.ndbc.noaa.gov/station_page.php?station=42002
- https://www.ndbc.noaa.gov/station_history.php?station=42002
Only standard meteorological data and ocean data was downloaded. Downloaded started at 1980, which is the first full year of collecting wave information.
### Data Fields
{'TSTMP': 'timestamp',
'#YY': '#yr',
' MM': 'mo',
'DD': 'dy',
'hh': 'hr',
'mm': 'mn',
'WDIR': 'degT',
'WSPD': 'm/s',
' GST': 'm/s',
' WVHT': 'm',
'DPD': 'sec',
'APD': 'sec',
'MWD ': 'degT',
'PRES': 'hPa',
' ATMP': 'degC',
' WTMP': 'degC'
}
## Dataset Creation
### Curation Rationale
The original data has inconsistent delimiters, different and inappropriate missing data values, and was not harmonized across years. Pre-2023 was edited in the same way as the previous data
but kept separate to allow for train and inference.
### Source Data
#### Initial Data Collection and Normalization
Data Downloaded on Oct 12 2023
All code used to transform the data can be found in the buoy-python directory. This is NOT production code and the focus was on correct results and minimizing time spent writing cleaning code.
1. #YY, MM, DD, hh, mm were concatenated to create a timestamp and stored in a new column.
2. From 1980 until 2005 there was no recording of minutes. Minutes for those years was set to 00.
3. All missing data was set to a blank value rather than an actual number
4. Remove all rows without wave data from all the data sets ( missing value in WVHT and DPD)
5. Columns MWD, DEWP, VIS, and TIDE were removed because of consistent missing values
6. From 2005 -> 2006 Wind direction goes from being called WD to WDIR
7. From 2006 -> 2007 Header goes from just 1 line with variable names to 2 lines with the second line being units.
These steps were used to create full_2023_remove_flawed_rows, the 2023 months, and full_years_remove_flawed_rows the previous data going back to 1980
Since the original purpose of this data was anomaly detection. The two data sets above received further processing.
1. All data values were converted to Z-scores (file named zscore_2023)
1. For 1980 - 2022, all rows with 2 or more fields with Z-scores > 2 were removed from the dataset (file named trimmed_zscores_years )
## Uses
### Direct Use
Primary use is working with tabular data and embeddings, particularly for anomaly detection
|
Qdrant/NOAA-Buoy
|
[
"task_categories:feature-extraction",
"task_categories:tabular-classification",
"task_categories:time-series-forecasting",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
2023-10-25T09:43:03+00:00
|
{"language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["feature-extraction", "tabular-classification", "time-series-forecasting"], "pretty_name": "NOAA Buoy meterological data", "tags": []}
|
2023-10-25T09:53:56+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-feature-extraction #task_categories-tabular-classification #task_categories-time-series-forecasting #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-4.0 #region-us
|
# NOAA Buoy meterological data
NOAA Buoy Data was downloaded, processed, and cleaned for tasks pertaining to tabular data. The data consists of meteorological measurements. There are two datasets
1. From 1980 through 2022 (denoted with "years" in file names)
2. From Jan 2023 through end of Sept 2023 (denoted with "2023" in file names)
The original intended use is for anomaly detection in tabular data.
## Dataset Details
### Dataset Description
This dataset contains weather buoy data to be used in a tabular embedding scenarios.
Buoy 42002 was chosen because it had many years of historical data and was still actively collecting information
Here is the buoy's page and its historical data page:
- URL
- URL
Only standard meteorological data and ocean data was downloaded. Downloaded started at 1980, which is the first full year of collecting wave information.
### Data Fields
{'TSTMP': 'timestamp',
'#YY': '#yr',
' MM': 'mo',
'DD': 'dy',
'hh': 'hr',
'mm': 'mn',
'WDIR': 'degT',
'WSPD': 'm/s',
' GST': 'm/s',
' WVHT': 'm',
'DPD': 'sec',
'APD': 'sec',
'MWD ': 'degT',
'PRES': 'hPa',
' ATMP': 'degC',
' WTMP': 'degC'
}
## Dataset Creation
### Curation Rationale
The original data has inconsistent delimiters, different and inappropriate missing data values, and was not harmonized across years. Pre-2023 was edited in the same way as the previous data
but kept separate to allow for train and inference.
### Source Data
#### Initial Data Collection and Normalization
Data Downloaded on Oct 12 2023
All code used to transform the data can be found in the buoy-python directory. This is NOT production code and the focus was on correct results and minimizing time spent writing cleaning code.
1. #YY, MM, DD, hh, mm were concatenated to create a timestamp and stored in a new column.
2. From 1980 until 2005 there was no recording of minutes. Minutes for those years was set to 00.
3. All missing data was set to a blank value rather than an actual number
4. Remove all rows without wave data from all the data sets ( missing value in WVHT and DPD)
5. Columns MWD, DEWP, VIS, and TIDE were removed because of consistent missing values
6. From 2005 -> 2006 Wind direction goes from being called WD to WDIR
7. From 2006 -> 2007 Header goes from just 1 line with variable names to 2 lines with the second line being units.
These steps were used to create full_2023_remove_flawed_rows, the 2023 months, and full_years_remove_flawed_rows the previous data going back to 1980
Since the original purpose of this data was anomaly detection. The two data sets above received further processing.
1. All data values were converted to Z-scores (file named zscore_2023)
1. For 1980 - 2022, all rows with 2 or more fields with Z-scores > 2 were removed from the dataset (file named trimmed_zscores_years )
## Uses
### Direct Use
Primary use is working with tabular data and embeddings, particularly for anomaly detection
|
[
"# NOAA Buoy meterological data\n\nNOAA Buoy Data was downloaded, processed, and cleaned for tasks pertaining to tabular data. The data consists of meteorological measurements. There are two datasets\n\n1. From 1980 through 2022 (denoted with \"years\" in file names)\n2. From Jan 2023 through end of Sept 2023 (denoted with \"2023\" in file names)\n\nThe original intended use is for anomaly detection in tabular data.",
"## Dataset Details",
"### Dataset Description\n\nThis dataset contains weather buoy data to be used in a tabular embedding scenarios. \nBuoy 42002 was chosen because it had many years of historical data and was still actively collecting information\n\nHere is the buoy's page and its historical data page:\n- URL\n- URL\n\nOnly standard meteorological data and ocean data was downloaded. Downloaded started at 1980, which is the first full year of collecting wave information.",
"### Data Fields\n\n{'TSTMP': 'timestamp',\n'#YY': '#yr',\n' MM': 'mo',\n'DD': 'dy',\n'hh': 'hr',\n'mm': 'mn',\n'WDIR': 'degT',\n'WSPD': 'm/s',\n' GST': 'm/s',\n' WVHT': 'm',\n'DPD': 'sec',\n'APD': 'sec',\n'MWD ': 'degT',\n'PRES': 'hPa',\n' ATMP': 'degC',\n' WTMP': 'degC'\n}",
"## Dataset Creation",
"### Curation Rationale\n\nThe original data has inconsistent delimiters, different and inappropriate missing data values, and was not harmonized across years. Pre-2023 was edited in the same way as the previous data \nbut kept separate to allow for train and inference.",
"### Source Data",
"#### Initial Data Collection and Normalization\nData Downloaded on Oct 12 2023\n\nAll code used to transform the data can be found in the buoy-python directory. This is NOT production code and the focus was on correct results and minimizing time spent writing cleaning code. \n\n1. #YY, MM, DD, hh, mm were concatenated to create a timestamp and stored in a new column. \n2. From 1980 until 2005 there was no recording of minutes. Minutes for those years was set to 00. \n3. All missing data was set to a blank value rather than an actual number\n4. Remove all rows without wave data from all the data sets ( missing value in WVHT and DPD)\n5. Columns MWD, DEWP, VIS, and TIDE were removed because of consistent missing values\n6. From 2005 -> 2006 Wind direction goes from being called WD to WDIR\n7. From 2006 -> 2007 Header goes from just 1 line with variable names to 2 lines with the second line being units.\n\nThese steps were used to create full_2023_remove_flawed_rows, the 2023 months, and full_years_remove_flawed_rows the previous data going back to 1980\n\nSince the original purpose of this data was anomaly detection. The two data sets above received further processing. \n\n1. All data values were converted to Z-scores (file named zscore_2023)\n1. For 1980 - 2022, all rows with 2 or more fields with Z-scores > 2 were removed from the dataset (file named trimmed_zscores_years )",
"## Uses",
"### Direct Use\n\nPrimary use is working with tabular data and embeddings, particularly for anomaly detection"
] |
[
"TAGS\n#task_categories-feature-extraction #task_categories-tabular-classification #task_categories-time-series-forecasting #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n",
"# NOAA Buoy meterological data\n\nNOAA Buoy Data was downloaded, processed, and cleaned for tasks pertaining to tabular data. The data consists of meteorological measurements. There are two datasets\n\n1. From 1980 through 2022 (denoted with \"years\" in file names)\n2. From Jan 2023 through end of Sept 2023 (denoted with \"2023\" in file names)\n\nThe original intended use is for anomaly detection in tabular data.",
"## Dataset Details",
"### Dataset Description\n\nThis dataset contains weather buoy data to be used in a tabular embedding scenarios. \nBuoy 42002 was chosen because it had many years of historical data and was still actively collecting information\n\nHere is the buoy's page and its historical data page:\n- URL\n- URL\n\nOnly standard meteorological data and ocean data was downloaded. Downloaded started at 1980, which is the first full year of collecting wave information.",
"### Data Fields\n\n{'TSTMP': 'timestamp',\n'#YY': '#yr',\n' MM': 'mo',\n'DD': 'dy',\n'hh': 'hr',\n'mm': 'mn',\n'WDIR': 'degT',\n'WSPD': 'm/s',\n' GST': 'm/s',\n' WVHT': 'm',\n'DPD': 'sec',\n'APD': 'sec',\n'MWD ': 'degT',\n'PRES': 'hPa',\n' ATMP': 'degC',\n' WTMP': 'degC'\n}",
"## Dataset Creation",
"### Curation Rationale\n\nThe original data has inconsistent delimiters, different and inappropriate missing data values, and was not harmonized across years. Pre-2023 was edited in the same way as the previous data \nbut kept separate to allow for train and inference.",
"### Source Data",
"#### Initial Data Collection and Normalization\nData Downloaded on Oct 12 2023\n\nAll code used to transform the data can be found in the buoy-python directory. This is NOT production code and the focus was on correct results and minimizing time spent writing cleaning code. \n\n1. #YY, MM, DD, hh, mm were concatenated to create a timestamp and stored in a new column. \n2. From 1980 until 2005 there was no recording of minutes. Minutes for those years was set to 00. \n3. All missing data was set to a blank value rather than an actual number\n4. Remove all rows without wave data from all the data sets ( missing value in WVHT and DPD)\n5. Columns MWD, DEWP, VIS, and TIDE were removed because of consistent missing values\n6. From 2005 -> 2006 Wind direction goes from being called WD to WDIR\n7. From 2006 -> 2007 Header goes from just 1 line with variable names to 2 lines with the second line being units.\n\nThese steps were used to create full_2023_remove_flawed_rows, the 2023 months, and full_years_remove_flawed_rows the previous data going back to 1980\n\nSince the original purpose of this data was anomaly detection. The two data sets above received further processing. \n\n1. All data values were converted to Z-scores (file named zscore_2023)\n1. For 1980 - 2022, all rows with 2 or more fields with Z-scores > 2 were removed from the dataset (file named trimmed_zscores_years )",
"## Uses",
"### Direct Use\n\nPrimary use is working with tabular data and embeddings, particularly for anomaly detection"
] |
[
86,
108,
4,
102,
159,
5,
61,
4,
355,
3,
26
] |
[
"passage: TAGS\n#task_categories-feature-extraction #task_categories-tabular-classification #task_categories-time-series-forecasting #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n# NOAA Buoy meterological data\n\nNOAA Buoy Data was downloaded, processed, and cleaned for tasks pertaining to tabular data. The data consists of meteorological measurements. There are two datasets\n\n1. From 1980 through 2022 (denoted with \"years\" in file names)\n2. From Jan 2023 through end of Sept 2023 (denoted with \"2023\" in file names)\n\nThe original intended use is for anomaly detection in tabular data.## Dataset Details### Dataset Description\n\nThis dataset contains weather buoy data to be used in a tabular embedding scenarios. \nBuoy 42002 was chosen because it had many years of historical data and was still actively collecting information\n\nHere is the buoy's page and its historical data page:\n- URL\n- URL\n\nOnly standard meteorological data and ocean data was downloaded. Downloaded started at 1980, which is the first full year of collecting wave information.### Data Fields\n\n{'TSTMP': 'timestamp',\n'#YY': '#yr',\n' MM': 'mo',\n'DD': 'dy',\n'hh': 'hr',\n'mm': 'mn',\n'WDIR': 'degT',\n'WSPD': 'm/s',\n' GST': 'm/s',\n' WVHT': 'm',\n'DPD': 'sec',\n'APD': 'sec',\n'MWD ': 'degT',\n'PRES': 'hPa',\n' ATMP': 'degC',\n' WTMP': 'degC'\n}## Dataset Creation"
] |
cf6f06b37e691c4876e39245a1ed5700a4034fab
|
# OpenSubtitles Danish-Norwegian
Aligned sentences with heuristic-based filters from OpenSubtitles in Danish and in Norwegian.
The source code for producing the dataset is included in the repository.
The dataset was created to aid training sentence transformers in the Danish Foundation Models project.
|
kardosdrur/opensubtitles-no-da
|
[
"license:mit",
"region:us"
] |
2023-10-25T09:46:28+00:00
|
{"license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "link_id", "dtype": "string"}, {"name": "da", "dtype": "string"}, {"name": "no", "dtype": "string"}, {"name": "overlap", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 270499727.08648384, "num_examples": 1772983}, {"name": "test", "num_bytes": 67624969.91351616, "num_examples": 443246}], "download_size": 201396375, "dataset_size": 338124697.0}}
|
2023-10-26T06:09:53+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
# OpenSubtitles Danish-Norwegian
Aligned sentences with heuristic-based filters from OpenSubtitles in Danish and in Norwegian.
The source code for producing the dataset is included in the repository.
The dataset was created to aid training sentence transformers in the Danish Foundation Models project.
|
[
"# OpenSubtitles Danish-Norwegian\nAligned sentences with heuristic-based filters from OpenSubtitles in Danish and in Norwegian.\nThe source code for producing the dataset is included in the repository.\n\nThe dataset was created to aid training sentence transformers in the Danish Foundation Models project."
] |
[
"TAGS\n#license-mit #region-us \n",
"# OpenSubtitles Danish-Norwegian\nAligned sentences with heuristic-based filters from OpenSubtitles in Danish and in Norwegian.\nThe source code for producing the dataset is included in the repository.\n\nThe dataset was created to aid training sentence transformers in the Danish Foundation Models project."
] |
[
11,
70
] |
[
"passage: TAGS\n#license-mit #region-us \n# OpenSubtitles Danish-Norwegian\nAligned sentences with heuristic-based filters from OpenSubtitles in Danish and in Norwegian.\nThe source code for producing the dataset is included in the repository.\n\nThe dataset was created to aid training sentence transformers in the Danish Foundation Models project."
] |
01a3b3672121ff8d034236dccd261c28ffc7c82e
|
# Dataset Card for "edgar_all_4-simple-no-valid-roberta-base"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lltala/edgar_all_4-simple-no-valid-roberta-base
|
[
"region:us"
] |
2023-10-25T09:58:05+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "doc_id", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "I-PER", "2": "I-ORG", "3": "I-LOC"}}}}, {"name": "tokens", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 7056516, "num_examples": 930}, {"name": "validation", "num_bytes": 676038, "num_examples": 90}], "download_size": 910413, "dataset_size": 7732554}}
|
2023-10-25T09:58:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "edgar_all_4-simple-no-valid-roberta-base"
More Information needed
|
[
"# Dataset Card for \"edgar_all_4-simple-no-valid-roberta-base\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"edgar_all_4-simple-no-valid-roberta-base\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"edgar_all_4-simple-no-valid-roberta-base\"\n\nMore Information needed"
] |
459a9a0720d3830f4d29d53d960b0402708d80b0
|
# Dataset Card for "Malayalam_MSA_Chunked"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
eswardivi/Malayalam_MSA_Chunked
|
[
"region:us"
] |
2023-10-25T10:07:21+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Neutral", "2": "Positive"}}}}], "splits": [{"name": "train", "num_bytes": 426015037.0, "num_examples": 161}], "download_size": 424859122, "dataset_size": 426015037.0}}
|
2023-10-25T10:07:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Malayalam_MSA_Chunked"
More Information needed
|
[
"# Dataset Card for \"Malayalam_MSA_Chunked\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Malayalam_MSA_Chunked\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Malayalam_MSA_Chunked\"\n\nMore Information needed"
] |
d794b8972b31f509f478c4b9fad9c32a15d1030c
|
# Dataset Card for Geo Coordinate Augmented Google-Landmarks
Geo coordinates were added as data to a tar file's worth of images from the [Google Landmark V2](https://github.com/cvdfoundation/google-landmark). Not all of the
images could be geo-tagged due to lack of coordinates on the image's wikimedia page.
## Dataset Details
### Dataset Description
Geo coordinates were added as data to a tar file's worth of images from the [Google Landmark V2](https://github.com/cvdfoundation/google-landmark). There were many more images that could have
been downloaded but this dataset was found to be a good balance of data size and sample size.
The intended use for the dataset was to demonstrate using a geo-filter in Qdrant along with a image similarity search.
Not all of the images couuld be geo-tagged due to lack of coordinates on the image's wikimedia page.
We provide the raw geotagged file as a geojson document, train_attribution_geo.json.
We also provide a json file that includes the data above along with embedding vectors for the images, id_payload_vector.json.
Thingsvision was used as the library for creating the image embeddings with the following ThingVision model:
```python
model_name = 'clip'
model_parameters = {
'variant': 'ViT-B/32'
}
```
The code directory contains the Python code used to geotag the images as well as generated the vectors. It can also be used to upload the embeddings to a Qdrant DB instance. This code is NOT for production and was
more focused on quickly and correctly get the coordinates and embed the images.
The license for this data and code match the license of the original Google Landmarks V2 Dataset: CC BY 4.0 license.
## Uses
### Direct Use
The primary use is case is image similarity search with geographic filtering.
|
Qdrant/google-landmark-geo
|
[
"task_categories:image-classification",
"size_categories:< 50K",
"source_datasets:Google Landmarks V2",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
2023-10-25T10:10:09+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["< 50K"], "source_datasets": ["Google Landmarks V2"], "task_categories": ["image-classification"], "pretty_name": "Geo Coordinate Augmented Google-Landmarks"}
|
2023-10-25T10:12:02+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-image-classification #size_categories-< 50K #source_datasets-Google Landmarks V2 #language-English #license-cc-by-4.0 #region-us
|
# Dataset Card for Geo Coordinate Augmented Google-Landmarks
Geo coordinates were added as data to a tar file's worth of images from the Google Landmark V2. Not all of the
images could be geo-tagged due to lack of coordinates on the image's wikimedia page.
## Dataset Details
### Dataset Description
Geo coordinates were added as data to a tar file's worth of images from the Google Landmark V2. There were many more images that could have
been downloaded but this dataset was found to be a good balance of data size and sample size.
The intended use for the dataset was to demonstrate using a geo-filter in Qdrant along with a image similarity search.
Not all of the images couuld be geo-tagged due to lack of coordinates on the image's wikimedia page.
We provide the raw geotagged file as a geojson document, train_attribution_geo.json.
We also provide a json file that includes the data above along with embedding vectors for the images, id_payload_vector.json.
Thingsvision was used as the library for creating the image embeddings with the following ThingVision model:
The code directory contains the Python code used to geotag the images as well as generated the vectors. It can also be used to upload the embeddings to a Qdrant DB instance. This code is NOT for production and was
more focused on quickly and correctly get the coordinates and embed the images.
The license for this data and code match the license of the original Google Landmarks V2 Dataset: CC BY 4.0 license.
## Uses
### Direct Use
The primary use is case is image similarity search with geographic filtering.
|
[
"# Dataset Card for Geo Coordinate Augmented Google-Landmarks\n\nGeo coordinates were added as data to a tar file's worth of images from the Google Landmark V2. Not all of the \nimages could be geo-tagged due to lack of coordinates on the image's wikimedia page.",
"## Dataset Details",
"### Dataset Description\n\nGeo coordinates were added as data to a tar file's worth of images from the Google Landmark V2. There were many more images that could have\nbeen downloaded but this dataset was found to be a good balance of data size and sample size. \n\nThe intended use for the dataset was to demonstrate using a geo-filter in Qdrant along with a image similarity search. \n\nNot all of the images couuld be geo-tagged due to lack of coordinates on the image's wikimedia page.\nWe provide the raw geotagged file as a geojson document, train_attribution_geo.json. \nWe also provide a json file that includes the data above along with embedding vectors for the images, id_payload_vector.json. \n\nThingsvision was used as the library for creating the image embeddings with the following ThingVision model: \n\n\n\nThe code directory contains the Python code used to geotag the images as well as generated the vectors. It can also be used to upload the embeddings to a Qdrant DB instance. This code is NOT for production and was \nmore focused on quickly and correctly get the coordinates and embed the images. \n\nThe license for this data and code match the license of the original Google Landmarks V2 Dataset: CC BY 4.0 license.",
"## Uses",
"### Direct Use\n\nThe primary use is case is image similarity search with geographic filtering."
] |
[
"TAGS\n#task_categories-image-classification #size_categories-< 50K #source_datasets-Google Landmarks V2 #language-English #license-cc-by-4.0 #region-us \n",
"# Dataset Card for Geo Coordinate Augmented Google-Landmarks\n\nGeo coordinates were added as data to a tar file's worth of images from the Google Landmark V2. Not all of the \nimages could be geo-tagged due to lack of coordinates on the image's wikimedia page.",
"## Dataset Details",
"### Dataset Description\n\nGeo coordinates were added as data to a tar file's worth of images from the Google Landmark V2. There were many more images that could have\nbeen downloaded but this dataset was found to be a good balance of data size and sample size. \n\nThe intended use for the dataset was to demonstrate using a geo-filter in Qdrant along with a image similarity search. \n\nNot all of the images couuld be geo-tagged due to lack of coordinates on the image's wikimedia page.\nWe provide the raw geotagged file as a geojson document, train_attribution_geo.json. \nWe also provide a json file that includes the data above along with embedding vectors for the images, id_payload_vector.json. \n\nThingsvision was used as the library for creating the image embeddings with the following ThingVision model: \n\n\n\nThe code directory contains the Python code used to geotag the images as well as generated the vectors. It can also be used to upload the embeddings to a Qdrant DB instance. This code is NOT for production and was \nmore focused on quickly and correctly get the coordinates and embed the images. \n\nThe license for this data and code match the license of the original Google Landmarks V2 Dataset: CC BY 4.0 license.",
"## Uses",
"### Direct Use\n\nThe primary use is case is image similarity search with geographic filtering."
] |
[
51,
65,
4,
291,
3,
20
] |
[
"passage: TAGS\n#task_categories-image-classification #size_categories-< 50K #source_datasets-Google Landmarks V2 #language-English #license-cc-by-4.0 #region-us \n# Dataset Card for Geo Coordinate Augmented Google-Landmarks\n\nGeo coordinates were added as data to a tar file's worth of images from the Google Landmark V2. Not all of the \nimages could be geo-tagged due to lack of coordinates on the image's wikimedia page.## Dataset Details### Dataset Description\n\nGeo coordinates were added as data to a tar file's worth of images from the Google Landmark V2. There were many more images that could have\nbeen downloaded but this dataset was found to be a good balance of data size and sample size. \n\nThe intended use for the dataset was to demonstrate using a geo-filter in Qdrant along with a image similarity search. \n\nNot all of the images couuld be geo-tagged due to lack of coordinates on the image's wikimedia page.\nWe provide the raw geotagged file as a geojson document, train_attribution_geo.json. \nWe also provide a json file that includes the data above along with embedding vectors for the images, id_payload_vector.json. \n\nThingsvision was used as the library for creating the image embeddings with the following ThingVision model: \n\n\n\nThe code directory contains the Python code used to geotag the images as well as generated the vectors. It can also be used to upload the embeddings to a Qdrant DB instance. This code is NOT for production and was \nmore focused on quickly and correctly get the coordinates and embed the images. \n\nThe license for this data and code match the license of the original Google Landmarks V2 Dataset: CC BY 4.0 license.## Uses### Direct Use\n\nThe primary use is case is image similarity search with geographic filtering."
] |
f31814e91a500b2fc0f884e31aaf32f3858b5bcb
|
# Dataset Card for "book_corpus-input_ids-valid-len256"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tuanio/book_corpus-input_ids-valid-len256
|
[
"region:us"
] |
2023-10-25T10:18:04+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 6319319328, "num_examples": 6156107}], "download_size": 2939435774, "dataset_size": 6319319328}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-26T07:47:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "book_corpus-input_ids-valid-len256"
More Information needed
|
[
"# Dataset Card for \"book_corpus-input_ids-valid-len256\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"book_corpus-input_ids-valid-len256\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"book_corpus-input_ids-valid-len256\"\n\nMore Information needed"
] |
d64f2db841e65b67a8408d3de6ec749686f23581
|
# Dataset Card for "wikipedie_20230601"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
simecek/wikipedie_20230601
|
[
"region:us"
] |
2023-10-25T10:18:05+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1529727976, "num_examples": 525205}], "download_size": 965130292, "dataset_size": 1529727976}}
|
2023-10-25T10:18:44+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "wikipedie_20230601"
More Information needed
|
[
"# Dataset Card for \"wikipedie_20230601\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"wikipedie_20230601\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"wikipedie_20230601\"\n\nMore Information needed"
] |
9861169f31c6cd64abe0289f4560413f8019f9c9
|
## Dataset Details
### Dataset Description
This dataset is the combination of the [Food101](https://huggingface.co/datasets/food101), [Indian Food Classification](https://www.kaggle.com/datasets/l33tc0d3r/indian-food-classification) and [The-massive-Indian-Food-Dataset](https://www.kaggle.com/datasets/anshulmehtakaggl/themassiveindianfooddataset) datasets.
This Dataset aims to be a viable dataset for Image Classification of Foods with an added Indian context. This dataset has 121 classes with each class having 800 images in the train split and 200 images in the test split. Maximum resolution of images is 512*512.
The [Food121-224](https://huggingface.co/datasets/ItsNotRohit/Food121-224) dataset has all images downscaled to a maximum resolution of 224*224.
### Dataset Sources
- **Food101:** https://huggingface.co/datasets/food101
- **Indian Food Classification:** https://www.kaggle.com/datasets/l33tc0d3r/indian-food-classification
- **The-massive-Indian-Food-Dataset:** https://www.kaggle.com/datasets/anshulmehtakaggl/themassiveindianfooddataset
|
ItsNotRohit/Food121
|
[
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"food101",
"image classification",
"region:us"
] |
2023-10-25T10:20:10+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["image-classification"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "apple_pie", "1": "baby_back_ribs", "2": "baklava", "3": "beef_carpaccio", "4": "beef_tartare", "5": "beet_salad", "6": "beignets", "7": "bibimbap", "8": "biryani", "9": "bread_pudding", "10": "breakfast_burrito", "11": "bruschetta", "12": "caesar_salad", "13": "cannoli", "14": "caprese_salad", "15": "carrot_cake", "16": "ceviche", "17": "chai", "18": "chapati", "19": "cheese_plate", "20": "cheesecake", "21": "chicken_curry", "22": "chicken_quesadilla", "23": "chicken_wings", "24": "chocolate_cake", "25": "chocolate_mousse", "26": "chole_bhature", "27": "churros", "28": "clam_chowder", "29": "club_sandwich", "30": "crab_cakes", "31": "creme_brulee", "32": "croque_madame", "33": "cup_cakes", "34": "dabeli", "35": "dal", "36": "deviled_eggs", "37": "dhokla", "38": "donuts", "39": "dosa", "40": "dumplings", "41": "edamame", "42": "eggs_benedict", "43": "escargots", "44": "falafel", "45": "filet_mignon", "46": "fish_and_chips", "47": "foie_gras", "48": "french_fries", "49": "french_onion_soup", "50": "french_toast", "51": "fried_calamari", "52": "fried_rice", "53": "frozen_yogurt", "54": "garlic_bread", "55": "gnocchi", "56": "greek_salad", "57": "grilled_cheese_sandwich", "58": "grilled_salmon", "59": "guacamole", "60": "gyoza", "61": "hamburger", "62": "hot_and_sour_soup", "63": "hot_dog", "64": "huevos_rancheros", "65": "hummus", "66": "ice_cream", "67": "idli", "68": "jalebi", "69": "kathi_rolls", "70": "kofta", "71": "kulfi", "72": "lasagna", "73": "lobster_bisque", "74": "lobster_roll_sandwich", "75": "macaroni_and_cheese", "76": "macarons", "77": "miso_soup", "78": "momos", "79": "mussels", "80": "naan", "81": "nachos", "82": "omelette", "83": "onion_rings", "84": "oysters", "85": "pad_thai", "86": "paella", "87": "pakoda", "88": "pancakes", "89": "pani_puri", "90": "panna_cotta", "91": "panner_butter_masala", "92": "pav_bhaji", "93": "peking_duck", "94": "pho", "95": "pizza", "96": "pork_chop", "97": "poutine", "98": "prime_rib", "99": "pulled_pork_sandwich", "100": "ramen", "101": "ravioli", "102": "red_velvet_cake", "103": "risotto", "104": "samosa", "105": "sashimi", "106": "scallops", "107": "seaweed_salad", "108": "shrimp_and_grits", "109": "spaghetti_bolognese", "110": "spaghetti_carbonara", "111": "spring_rolls", "112": "steak", "113": "strawberry_shortcake", "114": "sushi", "115": "tacos", "116": "takoyaki", "117": "tiramisu", "118": "tuna_tartare", "119": "vadapav", "120": "waffles"}}}}], "splits": [{"name": "train", "num_bytes": 7060443242, "num_examples": 96800}, {"name": "test", "num_bytes": 1821009841.2, "num_examples": 24200}], "download_size": 9888605889, "dataset_size": 8881453083.2}, "tags": ["food101", "image classification"]}
|
2023-10-28T06:05:03+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #food101 #image classification #region-us
|
## Dataset Details
### Dataset Description
This dataset is the combination of the Food101, Indian Food Classification and The-massive-Indian-Food-Dataset datasets.
This Dataset aims to be a viable dataset for Image Classification of Foods with an added Indian context. This dataset has 121 classes with each class having 800 images in the train split and 200 images in the test split. Maximum resolution of images is 512*512.
The Food121-224 dataset has all images downscaled to a maximum resolution of 224*224.
### Dataset Sources
- Food101: URL
- Indian Food Classification: URL
- The-massive-Indian-Food-Dataset: URL
|
[
"## Dataset Details",
"### Dataset Description\n\nThis dataset is the combination of the Food101, Indian Food Classification and The-massive-Indian-Food-Dataset datasets.\nThis Dataset aims to be a viable dataset for Image Classification of Foods with an added Indian context. This dataset has 121 classes with each class having 800 images in the train split and 200 images in the test split. Maximum resolution of images is 512*512.\n\n\nThe Food121-224 dataset has all images downscaled to a maximum resolution of 224*224.",
"### Dataset Sources\n\n- Food101: URL\n- Indian Food Classification: URL\n- The-massive-Indian-Food-Dataset: URL"
] |
[
"TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #food101 #image classification #region-us \n",
"## Dataset Details",
"### Dataset Description\n\nThis dataset is the combination of the Food101, Indian Food Classification and The-massive-Indian-Food-Dataset datasets.\nThis Dataset aims to be a viable dataset for Image Classification of Foods with an added Indian context. This dataset has 121 classes with each class having 800 images in the train split and 200 images in the test split. Maximum resolution of images is 512*512.\n\n\nThe Food121-224 dataset has all images downscaled to a maximum resolution of 224*224.",
"### Dataset Sources\n\n- Food101: URL\n- Indian Food Classification: URL\n- The-massive-Indian-Food-Dataset: URL"
] |
[
45,
4,
117,
33
] |
[
"passage: TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #food101 #image classification #region-us \n## Dataset Details### Dataset Description\n\nThis dataset is the combination of the Food101, Indian Food Classification and The-massive-Indian-Food-Dataset datasets.\nThis Dataset aims to be a viable dataset for Image Classification of Foods with an added Indian context. This dataset has 121 classes with each class having 800 images in the train split and 200 images in the test split. Maximum resolution of images is 512*512.\n\n\nThe Food121-224 dataset has all images downscaled to a maximum resolution of 224*224.### Dataset Sources\n\n- Food101: URL\n- Indian Food Classification: URL\n- The-massive-Indian-Food-Dataset: URL"
] |
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