datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
list | task_categories
list | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
list |
|---|---|---|---|---|---|---|---|---|---|
qbourbon/pb_valset-1
|
qbourbon
| 2023-11-25T12:38:19Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-25T12:38:12Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': 000_airplane
'1': 001_alarm_clock
'2': 002_angel
'3': 003_ant
'4': 004_apple
'5': 005_arm
'6': 006_armchair
'7': 007_ashtray
'8': 008_axe
'9': 009_backpack
'10': 010_banana
'11': 011_barn
'12': 012_baseball_bat
'13': 013_basket
'14': 014_bathtub
'15': 015_bear_(animal)
'16': 016_bed
'17': 017_bee
'18': 018_beer-mug
'19': 019_bell
'20': 020_bench
'21': 021_bicycle
'22': 022_binoculars
'23': 023_blimp
'24': 024_book
'25': 025_bookshelf
'26': 026_boomerang
'27': 027_bottle_opener
'28': 028_bowl
'29': 029_brain
'30': 030_bread
'31': 031_bridge
'32': 032_bulldozer
'33': 033_bus
'34': 034_bush
'35': 035_butterfly
'36': 036_cabinet
'37': 037_cactus
'38': 038_cake
'39': 039_calculator
'40': 040_camel
'41': 041_camera
'42': 042_candle
'43': 043_cannon
'44': 044_canoe
'45': 045_car_(sedan)
'46': 046_carrot
'47': 047_castle
'48': 048_cat
'49': 049_cell_phone
'50': 050_chair
'51': 051_chandelier
'52': 052_church
'53': 053_cigarette
'54': 054_cloud
'55': 055_comb
'56': 056_computer_monitor
'57': 057_computer-mouse
'58': 058_couch
'59': 059_cow
'60': 060_crab
'61': 061_crane_(machine)
'62': 062_crocodile
'63': 063_crown
'64': 064_cup
'65': 065_diamond
'66': 066_dog
'67': 067_dolphin
'68': 068_donut
'69': 069_door
'70': 070_door_handle
'71': 071_dragon
'72': 072_duck
'73': 073_ear
'74': 074_elephant
'75': 075_envelope
'76': 076_eye
'77': 077_eyeglasses
'78': 078_face
'79': 079_fan
'80': 080_feather
'81': 081_fire_hydrant
'82': 082_fish
'83': 083_flashlight
'84': 084_floor_lamp
'85': 085_flower_with_stem
'86': 086_flying_bird
'87': 087_flying_saucer
'88': 088_foot
'89': 089_fork
'90': 090_frog
'91': 091_frying-pan
'92': 092_giraffe
'93': 093_grapes
'94': 094_grenade
'95': 095_guitar
'96': 096_hamburger
'97': 097_hammer
'98': 098_hand
'99': 099_harp
'100': 100_hat
'101': 101_head
'102': 102_head-phones
'103': 103_hedgehog
'104': 104_helicopter
'105': 105_helmet
'106': 106_horse
'107': 107_hot_air_balloon
'108': 108_hot-dog
'109': 109_hourglass
'110': 110_house
'111': 111_human-skeleton
'112': 112_ice-cream-cone
'113': 113_ipod
'114': 114_kangaroo
'115': 115_key
'116': 116_keyboard
'117': 117_knife
'118': 118_ladder
'119': 119_laptop
'120': 120_leaf
'121': 121_lightbulb
'122': 122_lighter
'123': 123_lion
'124': 124_lobster
'125': 125_loudspeaker
'126': 126_mailbox
'127': 127_megaphone
'128': 128_mermaid
'129': 129_microphone
'130': 130_microscope
'131': 131_monkey
'132': 132_moon
'133': 133_mosquito
'134': 134_motorbike
'135': 135_mouse_(animal)
'136': 136_mouth
'137': 137_mug
'138': 138_mushroom
'139': 139_nose
'140': 140_octopus
'141': 141_owl
'142': 142_palm_tree
'143': 143_panda
'144': 144_paper_clip
'145': 145_parachute
'146': 146_parking_meter
'147': 147_parrot
'148': 148_pear
'149': 149_pen
'150': 150_penguin
'151': 151_person_sitting
'152': 152_person_walking
'153': 153_piano
'154': 154_pickup_truck
'155': 155_pig
'156': 156_pigeon
'157': 157_pineapple
'158': 158_pipe_(for_smoking)
'159': 159_pizza
'160': 160_potted_plant
'161': 161_power_outlet
'162': 162_present
'163': 163_pretzel
'164': 164_pumpkin
'165': 165_purse
'166': 166_rabbit
'167': 167_race_car
'168': 168_radio
'169': 169_rainbow
'170': 170_revolver
'171': 171_rifle
'172': 172_rollerblades
'173': 173_rooster
'174': 174_sailboat
'175': 175_santa_claus
'176': 176_satellite
'177': 177_satellite_dish
'178': 178_saxophone
'179': 179_scissors
'180': 180_scorpion
'181': 181_screwdriver
'182': 182_sea_turtle
'183': 183_seagull
'184': 184_shark
'185': 185_sheep
'186': 186_ship
'187': 187_shoe
'188': 188_shovel
'189': 189_skateboard
'190': 190_skull
'191': 191_skyscraper
'192': 192_snail
'193': 193_snake
'194': 194_snowboard
'195': 195_snowman
'196': 196_socks
'197': 197_space_shuttle
'198': 198_speed-boat
'199': 199_spider
'200': 200_sponge_bob
'201': 201_spoon
'202': 202_squirrel
'203': 203_standing_bird
'204': 204_stapler
'205': 205_strawberry
'206': 206_streetlight
'207': 207_submarine
'208': 208_suitcase
'209': 209_sun
'210': 210_suv
'211': 211_swan
'212': 212_sword
'213': 213_syringe
'214': 214_t-shirt
'215': 215_table
'216': 216_tablelamp
'217': 217_teacup
'218': 218_teapot
'219': 219_teddy-bear
'220': 220_telephone
'221': 221_tennis-racket
'222': 222_tent
'223': 223_tiger
'224': 224_tire
'225': 225_toilet
'226': 226_tomato
'227': 227_tooth
'228': 228_toothbrush
'229': 229_tractor
'230': 230_traffic_light
'231': 231_train
'232': 232_tree
'233': 233_trombone
'234': 234_trousers
'235': 235_truck
'236': 236_trumpet
'237': 237_tv
'238': 238_umbrella
'239': 239_van
'240': 240_vase
'241': 241_violin
'242': 242_walkie_talkie
'243': 243_wheel
'244': 244_wheelbarrow
'245': 245_windmill
'246': 246_wine-bottle
'247': 247_wineglass
'248': 248_wrist-watch
'249': 249_zebra
'250': mistery_category
splits:
- name: validation
num_bytes: 7002696.487999999
num_examples: 234
download_size: 6880189
dataset_size: 7002696.487999999
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
|
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astrosbd/fake-review
|
astrosbd
| 2023-11-25T12:45:47Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-25T12:45:45Z |
---
dataset_info:
features:
- name: cat
dtype: string
- name: note
dtype: float64
- name: label
dtype: string
- name: text
dtype: string
- name: instruction
dtype: string
- name: full_instruction
dtype: string
splits:
- name: train
num_bytes: 49524950
num_examples: 40432
download_size: 19234619
dataset_size: 49524950
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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Anwaarma/BP-balanced-I04
|
Anwaarma
| 2023-11-25T13:08:45Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-25T13:08:39Z |
---
dataset_info:
features:
- name: Target
dtype: int64
- name: PC
dtype: string
- name: GSHARE
dtype: string
- name: GA table
dtype: string
splits:
- name: train
num_bytes: 41004500
num_examples: 82009
- name: test
num_bytes: 10251500
num_examples: 20503
download_size: 2353976
dataset_size: 51256000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
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squarehead/IllyaV1RVC
|
squarehead
| 2023-11-25T13:21:16Z | 0 | 0 |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-11-25T13:19:17Z |
---
license: cc-by-nc-4.0
---
|
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qbourbon/pb_trainset-2
|
qbourbon
| 2023-11-25T13:24:31Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-25T13:24:08Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': 000_airplane
'1': 001_alarm_clock
'2': 002_angel
'3': 003_ant
'4': 004_apple
'5': 005_arm
'6': 006_armchair
'7': 007_ashtray
'8': 008_axe
'9': 009_backpack
'10': 010_banana
'11': 011_barn
'12': 012_baseball_bat
'13': 013_basket
'14': 014_bathtub
'15': 015_bear_(animal)
'16': 016_bed
'17': 017_bee
'18': 018_beer-mug
'19': 019_bell
'20': 020_bench
'21': 021_bicycle
'22': 022_binoculars
'23': 023_blimp
'24': 024_book
'25': 025_bookshelf
'26': 026_boomerang
'27': 027_bottle_opener
'28': 028_bowl
'29': 029_brain
'30': 030_bread
'31': 031_bridge
'32': 032_bulldozer
'33': 033_bus
'34': 034_bush
'35': 035_butterfly
'36': 036_cabinet
'37': 037_cactus
'38': 038_cake
'39': 039_calculator
'40': 040_camel
'41': 041_camera
'42': 042_candle
'43': 043_cannon
'44': 044_canoe
'45': 045_car_(sedan)
'46': 046_carrot
'47': 047_castle
'48': 048_cat
'49': 049_cell_phone
'50': 050_chair
'51': 051_chandelier
'52': 052_church
'53': 053_cigarette
'54': 054_cloud
'55': 055_comb
'56': 056_computer_monitor
'57': 057_computer-mouse
'58': 058_couch
'59': 059_cow
'60': 060_crab
'61': 061_crane_(machine)
'62': 062_crocodile
'63': 063_crown
'64': 064_cup
'65': 065_diamond
'66': 066_dog
'67': 067_dolphin
'68': 068_donut
'69': 069_door
'70': 070_door_handle
'71': 071_dragon
'72': 072_duck
'73': 073_ear
'74': 074_elephant
'75': 075_envelope
'76': 076_eye
'77': 077_eyeglasses
'78': 078_face
'79': 079_fan
'80': 080_feather
'81': 081_fire_hydrant
'82': 082_fish
'83': 083_flashlight
'84': 084_floor_lamp
'85': 085_flower_with_stem
'86': 086_flying_bird
'87': 087_flying_saucer
'88': 088_foot
'89': 089_fork
'90': 090_frog
'91': 091_frying-pan
'92': 092_giraffe
'93': 093_grapes
'94': 094_grenade
'95': 095_guitar
'96': 096_hamburger
'97': 097_hammer
'98': 098_hand
'99': 099_harp
'100': 100_hat
'101': 101_head
'102': 102_head-phones
'103': 103_hedgehog
'104': 104_helicopter
'105': 105_helmet
'106': 106_horse
'107': 107_hot_air_balloon
'108': 108_hot-dog
'109': 109_hourglass
'110': 110_house
'111': 111_human-skeleton
'112': 112_ice-cream-cone
'113': 113_ipod
'114': 114_kangaroo
'115': 115_key
'116': 116_keyboard
'117': 117_knife
'118': 118_ladder
'119': 119_laptop
'120': 120_leaf
'121': 121_lightbulb
'122': 122_lighter
'123': 123_lion
'124': 124_lobster
'125': 125_loudspeaker
'126': 126_mailbox
'127': 127_megaphone
'128': 128_mermaid
'129': 129_microphone
'130': 130_microscope
'131': 131_monkey
'132': 132_moon
'133': 133_mosquito
'134': 134_motorbike
'135': 135_mouse_(animal)
'136': 136_mouth
'137': 137_mug
'138': 138_mushroom
'139': 139_nose
'140': 140_octopus
'141': 141_owl
'142': 142_palm_tree
'143': 143_panda
'144': 144_paper_clip
'145': 145_parachute
'146': 146_parking_meter
'147': 147_parrot
'148': 148_pear
'149': 149_pen
'150': 150_penguin
'151': 151_person_sitting
'152': 152_person_walking
'153': 153_piano
'154': 154_pickup_truck
'155': 155_pig
'156': 156_pigeon
'157': 157_pineapple
'158': 158_pipe_(for_smoking)
'159': 159_pizza
'160': 160_potted_plant
'161': 161_power_outlet
'162': 162_present
'163': 163_pretzel
'164': 164_pumpkin
'165': 165_purse
'166': 166_rabbit
'167': 167_race_car
'168': 168_radio
'169': 169_rainbow
'170': 170_revolver
'171': 171_rifle
'172': 172_rollerblades
'173': 173_rooster
'174': 174_sailboat
'175': 175_santa_claus
'176': 176_satellite
'177': 177_satellite_dish
'178': 178_saxophone
'179': 179_scissors
'180': 180_scorpion
'181': 181_screwdriver
'182': 182_sea_turtle
'183': 183_seagull
'184': 184_shark
'185': 185_sheep
'186': 186_ship
'187': 187_shoe
'188': 188_shovel
'189': 189_skateboard
'190': 190_skull
'191': 191_skyscraper
'192': 192_snail
'193': 193_snake
'194': 194_snowboard
'195': 195_snowman
'196': 196_socks
'197': 197_space_shuttle
'198': 198_speed-boat
'199': 199_spider
'200': 200_sponge_bob
'201': 201_spoon
'202': 202_squirrel
'203': 203_standing_bird
'204': 204_stapler
'205': 205_strawberry
'206': 206_streetlight
'207': 207_submarine
'208': 208_suitcase
'209': 209_sun
'210': 210_suv
'211': 211_swan
'212': 212_sword
'213': 213_syringe
'214': 214_t-shirt
'215': 215_table
'216': 216_tablelamp
'217': 217_teacup
'218': 218_teapot
'219': 219_teddy-bear
'220': 220_telephone
'221': 221_tennis-racket
'222': 222_tent
'223': 223_tiger
'224': 224_tire
'225': 225_toilet
'226': 226_tomato
'227': 227_tooth
'228': 228_toothbrush
'229': 229_tractor
'230': 230_traffic_light
'231': 231_train
'232': 232_tree
'233': 233_trombone
'234': 234_trousers
'235': 235_truck
'236': 236_trumpet
'237': 237_tv
'238': 238_umbrella
'239': 239_van
'240': 240_vase
'241': 241_violin
'242': 242_walkie_talkie
'243': 243_wheel
'244': 244_wheelbarrow
'245': 245_windmill
'246': 246_wine-bottle
'247': 247_wineglass
'248': 248_wrist-watch
'249': 249_zebra
'250': mistery_category
splits:
- name: train
num_bytes: 151506666.84822693
num_examples: 5136
download_size: 148171712
dataset_size: 151506666.84822693
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
qbourbon/pb_valset-2
|
qbourbon
| 2023-11-25T13:24:38Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-25T13:24:32Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': 000_airplane
'1': 001_alarm_clock
'2': 002_angel
'3': 003_ant
'4': 004_apple
'5': 005_arm
'6': 006_armchair
'7': 007_ashtray
'8': 008_axe
'9': 009_backpack
'10': 010_banana
'11': 011_barn
'12': 012_baseball_bat
'13': 013_basket
'14': 014_bathtub
'15': 015_bear_(animal)
'16': 016_bed
'17': 017_bee
'18': 018_beer-mug
'19': 019_bell
'20': 020_bench
'21': 021_bicycle
'22': 022_binoculars
'23': 023_blimp
'24': 024_book
'25': 025_bookshelf
'26': 026_boomerang
'27': 027_bottle_opener
'28': 028_bowl
'29': 029_brain
'30': 030_bread
'31': 031_bridge
'32': 032_bulldozer
'33': 033_bus
'34': 034_bush
'35': 035_butterfly
'36': 036_cabinet
'37': 037_cactus
'38': 038_cake
'39': 039_calculator
'40': 040_camel
'41': 041_camera
'42': 042_candle
'43': 043_cannon
'44': 044_canoe
'45': 045_car_(sedan)
'46': 046_carrot
'47': 047_castle
'48': 048_cat
'49': 049_cell_phone
'50': 050_chair
'51': 051_chandelier
'52': 052_church
'53': 053_cigarette
'54': 054_cloud
'55': 055_comb
'56': 056_computer_monitor
'57': 057_computer-mouse
'58': 058_couch
'59': 059_cow
'60': 060_crab
'61': 061_crane_(machine)
'62': 062_crocodile
'63': 063_crown
'64': 064_cup
'65': 065_diamond
'66': 066_dog
'67': 067_dolphin
'68': 068_donut
'69': 069_door
'70': 070_door_handle
'71': 071_dragon
'72': 072_duck
'73': 073_ear
'74': 074_elephant
'75': 075_envelope
'76': 076_eye
'77': 077_eyeglasses
'78': 078_face
'79': 079_fan
'80': 080_feather
'81': 081_fire_hydrant
'82': 082_fish
'83': 083_flashlight
'84': 084_floor_lamp
'85': 085_flower_with_stem
'86': 086_flying_bird
'87': 087_flying_saucer
'88': 088_foot
'89': 089_fork
'90': 090_frog
'91': 091_frying-pan
'92': 092_giraffe
'93': 093_grapes
'94': 094_grenade
'95': 095_guitar
'96': 096_hamburger
'97': 097_hammer
'98': 098_hand
'99': 099_harp
'100': 100_hat
'101': 101_head
'102': 102_head-phones
'103': 103_hedgehog
'104': 104_helicopter
'105': 105_helmet
'106': 106_horse
'107': 107_hot_air_balloon
'108': 108_hot-dog
'109': 109_hourglass
'110': 110_house
'111': 111_human-skeleton
'112': 112_ice-cream-cone
'113': 113_ipod
'114': 114_kangaroo
'115': 115_key
'116': 116_keyboard
'117': 117_knife
'118': 118_ladder
'119': 119_laptop
'120': 120_leaf
'121': 121_lightbulb
'122': 122_lighter
'123': 123_lion
'124': 124_lobster
'125': 125_loudspeaker
'126': 126_mailbox
'127': 127_megaphone
'128': 128_mermaid
'129': 129_microphone
'130': 130_microscope
'131': 131_monkey
'132': 132_moon
'133': 133_mosquito
'134': 134_motorbike
'135': 135_mouse_(animal)
'136': 136_mouth
'137': 137_mug
'138': 138_mushroom
'139': 139_nose
'140': 140_octopus
'141': 141_owl
'142': 142_palm_tree
'143': 143_panda
'144': 144_paper_clip
'145': 145_parachute
'146': 146_parking_meter
'147': 147_parrot
'148': 148_pear
'149': 149_pen
'150': 150_penguin
'151': 151_person_sitting
'152': 152_person_walking
'153': 153_piano
'154': 154_pickup_truck
'155': 155_pig
'156': 156_pigeon
'157': 157_pineapple
'158': 158_pipe_(for_smoking)
'159': 159_pizza
'160': 160_potted_plant
'161': 161_power_outlet
'162': 162_present
'163': 163_pretzel
'164': 164_pumpkin
'165': 165_purse
'166': 166_rabbit
'167': 167_race_car
'168': 168_radio
'169': 169_rainbow
'170': 170_revolver
'171': 171_rifle
'172': 172_rollerblades
'173': 173_rooster
'174': 174_sailboat
'175': 175_santa_claus
'176': 176_satellite
'177': 177_satellite_dish
'178': 178_saxophone
'179': 179_scissors
'180': 180_scorpion
'181': 181_screwdriver
'182': 182_sea_turtle
'183': 183_seagull
'184': 184_shark
'185': 185_sheep
'186': 186_ship
'187': 187_shoe
'188': 188_shovel
'189': 189_skateboard
'190': 190_skull
'191': 191_skyscraper
'192': 192_snail
'193': 193_snake
'194': 194_snowboard
'195': 195_snowman
'196': 196_socks
'197': 197_space_shuttle
'198': 198_speed-boat
'199': 199_spider
'200': 200_sponge_bob
'201': 201_spoon
'202': 202_squirrel
'203': 203_standing_bird
'204': 204_stapler
'205': 205_strawberry
'206': 206_streetlight
'207': 207_submarine
'208': 208_suitcase
'209': 209_sun
'210': 210_suv
'211': 211_swan
'212': 212_sword
'213': 213_syringe
'214': 214_t-shirt
'215': 215_table
'216': 216_tablelamp
'217': 217_teacup
'218': 218_teapot
'219': 219_teddy-bear
'220': 220_telephone
'221': 221_tennis-racket
'222': 222_tent
'223': 223_tiger
'224': 224_tire
'225': 225_toilet
'226': 226_tomato
'227': 227_tooth
'228': 228_toothbrush
'229': 229_tractor
'230': 230_traffic_light
'231': 231_train
'232': 232_tree
'233': 233_trombone
'234': 234_trousers
'235': 235_truck
'236': 236_trumpet
'237': 237_tv
'238': 238_umbrella
'239': 239_van
'240': 240_vase
'241': 241_violin
'242': 242_walkie_talkie
'243': 243_wheel
'244': 244_wheelbarrow
'245': 245_windmill
'246': 246_wine-bottle
'247': 247_wineglass
'248': 248_wrist-watch
'249': 249_zebra
'250': mistery_category
splits:
- name: validation
num_bytes: 28145920.816
num_examples: 963
download_size: 27662826
dataset_size: 28145920.816
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
|
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casualdatauser/neet-dataset-mini
|
casualdatauser
| 2023-11-25T13:34:52Z | 0 | 0 |
[
"language:en",
"license:mit",
"region:us"
] | null | 2023-11-25T13:32:17Z |
---
license: mit
language:
- en
pretty_name: Mini NEET Dataset
---
#
|
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] |
Gabriel1322/jclindao
|
Gabriel1322
| 2023-11-25T13:34:40Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T13:32:58Z |
---
license: openrail
---
|
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Sakil/DocuBotMultiPDFConversationalAssistant
|
Sakil
| 2023-11-25T13:44:43Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-25T13:44:43Z |
---
license: apache-2.0
---
|
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sulenur/turkishReviews-ds-small
|
sulenur
| 2023-11-25T13:54:39Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-25T13:54:35Z |
---
dataset_info:
features:
- name: review
dtype: string
- name: review_length
dtype: int64
splits:
- name: train
num_bytes: 1253074.2290889719
num_examples: 3378
- name: validation
num_bytes: 139477.77091102823
num_examples: 376
download_size: 901581
dataset_size: 1392552.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
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] |
HuskyAI/pixel_game_icon
|
HuskyAI
| 2023-11-25T13:57:04Z | 0 | 1 |
[
"task_categories:text-to-image",
"license:apache-2.0",
"style",
"pixel",
"icon",
"region:us"
] |
[
"text-to-image"
] | 2023-11-25T13:55:54Z |
---
license: apache-2.0
task_categories:
- text-to-image
tags:
- style
- pixel
- icon
---
|
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Partha117/apache_bug_reports
|
Partha117
| 2023-11-25T19:08:08Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-25T14:34:01Z |
---
license: apache-2.0
dataset_info:
features:
- name: id
dtype: int64
- name: bug_id
dtype: int64
- name: summary
dtype: string
- name: description
dtype: string
- name: report_time
dtype: string
- name: report_timestamp
dtype: int64
- name: status
dtype: string
- name: commit
dtype: string
- name: commit_timestamp
dtype: int64
- name: files
dtype: string
- name: project_name
dtype: string
splits:
- name: train
num_bytes: 28828266
num_examples: 22747
download_size: 10160604
dataset_size: 28828266
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
todi1/pasmr1
|
todi1
| 2023-11-25T14:46:59Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T14:38:37Z |
---
license: openrail
---
|
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mangaphd/HausaLexicons
|
mangaphd
| 2023-11-25T15:00:35Z | 0 | 0 |
[
"license:ecl-2.0",
"doi:10.57967/hf/1390",
"region:us"
] | null | 2023-11-25T14:59:49Z |
---
license: ecl-2.0
---
|
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edsongomes0215/vozdeedmar
|
edsongomes0215
| 2023-11-25T15:01:42Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T15:01:22Z |
---
license: openrail
---
|
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] |
yuliano/RFC
|
yuliano
| 2023-11-25T15:07:17Z | 0 | 0 |
[
"task_categories:summarization",
"size_categories:n>1T",
"language:en",
"region:us"
] |
[
"summarization"
] | 2023-11-25T15:03:17Z |
---
task_categories:
- summarization
language:
- en
pretty_name: Summator 3000
size_categories:
- n>1T
---
|
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] |
todi1/jjasmr
|
todi1
| 2023-11-25T15:12:48Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T15:11:00Z |
---
license: openrail
---
|
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nlasso/anac-manuals-23
|
nlasso
| 2023-11-25T15:33:45Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-25T15:24:42Z |
---
license: mit
---
|
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mispeech/speechocean762
|
mispeech
| 2023-11-25T16:09:00Z | 0 | 0 |
[
"task_categories:automatic-speech-recognition",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"pronunciation-scoring",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2023-11-25T15:50:48Z |
---
license: apache-2.0
task_categories:
- automatic-speech-recognition
language:
- en
tags:
- pronunciation-scoring
pretty_name: speechocean762
size_categories:
- 1K<n<10K
---
# speechocean762: A non-native English corpus for pronunciation scoring task
## Introduction
Pronunciation scoring is a crucial technology in computer-assisted language learning (CALL) systems. The pronunciation quality scores might be given at phoneme-level, word-level, and sentence-level for a typical pronunciation scoring task.
This corpus aims to provide a free public dataset for the pronunciation scoring task.
Key features:
* It is available for free download for both commercial and non-commercial purposes.
* The speaker variety encompasses young children and adults.
* The manual annotations are in multiple aspects at sentence-level, word-level and phoneme-level.
This corpus consists of 5000 English sentences. All the speakers are non-native, and their mother tongue is Mandarin. Half of the speakers are Children, and the others are adults. The information of age and gender are provided.
Five experts made the scores. To avoid subjective bias, each expert scores independently under the same metric.
## The scoring metric
The experts score at three levels: phoneme-level, word-level, and sentence-level.
### Phoneme level
Score the pronunciation goodness of each phoneme within the words.
Score range: 0-2
* 2: pronunciation is correct
* 1: pronunciation is right but has a heavy accent
* 0: pronunciation is incorrect or missed
### Word level
Score the accuracy and stress of each word's pronunciation.
#### Accuracy
Score range: 0 - 10
* 10: The pronunciation of the word is perfect
* 7-9: Most phones in this word are pronounced correctly but have accents
* 4-6: Less than 30% of phones in this word are wrongly pronounced
* 2-3: More than 30% of phones in this word are wrongly pronounced. In another case, the word is mispronounced as some other word. For example, the student mispronounced the word "bag" as "bike"
* 1: The pronunciation is hard to distinguish
* 0: no voice
#### Stress
Score range: {5, 10}
* 10: The stress is correct, or this is a mono-syllable word
* 5: The stress is wrong
### Sentence level
Score the accuracy, fluency, completeness and prosodic at the sentence level.
#### Accuracy
Score range: 0 - 10
* 9-10: The overall pronunciation of the sentence is excellent, with accurate phonology and no obvious pronunciation mistakes
* 7-8: The overall pronunciation of the sentence is good, with a few pronunciation mistakes
* 5-6: The overall pronunciation of the sentence is understandable, with many pronunciation mistakes and accent, but it does not affect the understanding of basic meanings
* 3-4: Poor, clumsy and rigid pronunciation of the sentence as a whole, with serious pronunciation mistakes
* 0-2: Extremely poor pronunciation and only one or two words are recognizable
#### Completeness
Score range: 0.0 - 1.0
The percentage of the words with good pronunciation.
#### Fluency
Score range: 0 - 10
* 8-10: Fluent without noticeable pauses or stammering
* 6-7: Fluent in general, with a few pauses, repetition, and stammering
* 4-5: the speech is a little influent, with many pauses, repetition, and stammering
* 0-3: intermittent, very influent speech, with lots of pauses, repetition, and stammering
#### Prosodic
Score range: 0 - 10
* 9-10: Correct intonation at a stable speaking speed, speak with cadence, and can speak like a native
* 7-8: Nearly correct intonation at a stable speaking speed, nearly smooth and coherent, but with little stammering and few pauses
* 5-6: Unstable speech speed, many stammering and pauses with a poor sense of rhythm
* 3-4: Unstable speech speed, speak too fast or too slow, without the sense of rhythm
* 0-2: Poor intonation and lots of stammering and pauses, unable to read a complete sentence
## Data structure
The following tree shows the file structure of this corpus:
```
├── scores.json
├── scores-detail.json
├── train
│ ├── spk2age
│ ├── spk2gender
│ ├── spk2utt
│ ├── text
│ ├── utt2spk
│ └── wav.scp
├── test
│ ├── spk2age
│ ├── spk2gender
│ ├── spk2utt
│ ├── text
│ ├── utt2spk
│ └── wav.scp
└── WAVE
├── SPEAKER0001
│ ├── 000010011.WAV
│ ├── 000010035.WAV
│ ├── ...
│ └── 000010173.WAV
├── SPEAKER0003
│ ├── 000030012.WAV
│ ├── 000030024.WAV
│ ├── ...
│ └── 000030175.WAV
└── SPEAKER0005
├── 000050003.WAV
├── 000050010.WAV
├── ...
└── 000050175.WAV
```
There are two datasets: `train` and `test`, and both are in Kaldi's data directory style.
The scores are stored in `scores.json`. Here is an example:
```
{
"000010011": { # utt-id
"text": "WE CALL IT BEAR", # transcript text
"accuracy": 8, # sentence-level accuracy score
"completeness": 10.0, # sentence-level completeness score
"fluency": 9, # sentence-level fluency score
"prosodic": 9, # sentence-level prosodic score
"total": 8, # sentence-level total score
"words": [
{
"accuracy": 10, # word-level accuracy score
"stress": 10, # word-level stress score
"total": 10, # word-level total score
"text": "WE", # the word text
"phones": "W IY0", # phones of the word
"phones-accuracy": [2.0, 2.0] # phoneme-level accuracy score
},
{
"accuracy": 10,
"stress": 10,
"total": 10,
"text": "CALL",
"phones": "K AO0 L",
"phones-accuracy": [2.0, 1.8, 1.8]
},
{
"accuracy": 10,
"stress": 10,
"total": 10,
"text": "IT",
"phones": "IH0 T",
"phones-accuracy": [2.0, 2.0]
},
{
"accuracy": 6,
"stress": 10,
"total": 6,
"text": "BEAR",
"phones": "B EH0 R",
"phones-accuracy": [2.0, 1.0, 1.0]
}
]
},
...
}
```
For the phones with an accuracy score lower than 0.5, an extra "mispronunciations" block indicates which phoneme the current phone was actually pronounced.
An example:
```
{
"text": "LISA",
"accuracy": 5,
"phones": ["L", "IY1", "S", "AH0"],
"phones-accuracy": [0.4, 2, 2, 1.2],
"mispronunciations": [
{
"canonical-phone": "L",
"index": 0,
"pronounced-phone": "D"
}
],
"stress": 10,
"total": 6
}
```
The file `scores.json` is processed from `scores-detail.json`.
The two JSON files are almost the same, but `scores-detail.json` has the five experts' original scores, while the scores of scores.json were the average or median scores.
An example item in `scores-detail.json`:
```
{
"000010011": {
"text": "WE CALL IT BEAR",
"accuracy": [7.0, 9.0, 8.0, 8.0, 9.0],
"completeness": [1.0, 1.0, 1.0, 1.0, 1.0],
"fluency": [10.0, 9.0, 8.0, 8.0, 10.0],
"prosodic": [10.0, 9.0, 7.0, 8.0, 9.0],
"total": [7.6, 9.0, 7.9, 8.0, 9.1],
"words": [
{
"accuracy": [10.0, 10.0, 10.0, 10.0, 10.0],
"stress": [10.0, 10.0, 10.0, 10.0, 10.0],
"total": [10.0, 10.0, 10.0, 10.0, 10.0],
"text": "WE",
"ref-phones": "W IY0",
"phones": ["W IY0", "W IY0", "W IY0", "W IY0", "W IY0"]
},
{
"accuracy": [10.0, 8.0, 10.0, 10.0, 8.0],
"stress": [10.0, 10.0, 10.0, 10.0, 10.0],
"total": [10.0, 8.4, 10.0, 10.0, 8.4],
"text": "CALL",
"ref-phones": "K AO0 L",
"phones": ["K AO0 L", "K {AO0} L", "K AO0 L", "K AO0 L", "K AO0 {L}"],
},
{
"accuracy": [10.0, 10.0, 10.0, 10.0, 10.0],
"stress": [10.0, 10.0, 10.0, 10.0, 10.0],
"total": [10.0, 10.0, 10.0, 10.0, 10.0],
"text": "IT",
"ref-phones": "IH0 T",
"phones": ["IH0 T", "IH0 T", "IH0 T", "IH0 T", "IH0 T"]
},
{
"accuracy": [3.0, 7.0, 10.0, 2.0, 6.0],
"stress": [10.0, 10.0, 10.0, 10.0, 10.0],
"phones": ["B (EH0) (R)", "B {EH0} {R}", "B EH0 R", "B (EH0) (R)", "B EH0 [L] R"],
"total": [4.4, 7.6, 10.0, 3.6, 6.8],
"text": "BEAR",
"ref-phones": "B EH0 R"
}
],
},
...
}
```
In `scores-detail.json`, the phoneme-level scores are notated in the following convenient notation:
* for score 2, do not use any symbol
* for score 1, use "{}" symbol
* for score 0, use "()" symbol
* for the inserted phone, use the "[]" symbol
For example, "B (EH) R" means the score of EH is 0 while the scores of B and R are both 2,
"B EH [L] R" mean there is an unexpected phone "L" and the other phones are scored 2.
## Citation
Please cite our paper if you find this work useful:
```bibtext
@inproceedings{speechocean762,
title={speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment},
booktitle={Proc. Interspeech 2021},
year=2021,
author={Junbo Zhang, Zhiwen Zhang, Yongqing Wang, Zhiyong Yan, Qiong Song, Yukai Huang, Ke Li, Daniel Povey, Yujun Wang}
}
```
|
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] |
FpOliveira/TuPi-Portuguese-Hate-Speech-Dataset
|
FpOliveira
| 2023-12-04T13:49:51Z | 0 | 0 |
[
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"size_categories:10K<n<100K",
"source_datasets:crowdsourced",
"language:pt",
"license:mit",
"hate-speech-detection",
"brazilian-portuguese",
"arxiv:2010.04543",
"arxiv:2103.14972",
"doi:10.57967/hf/1419",
"region:us"
] |
[
"text-classification"
] | 2023-11-25T16:40:48Z |
---
pretty_name: TuPiHateSpeech
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license: mit
task_categories:
- text-classification
source_datasets:
- crowdsourced
language:
- pt
language_bcp47:
- pt-BR
size_categories:
- 10K<n<100K
tags:
- hate-speech-detection
- brazilian-portuguese
---
# TuPI dataset
The TuPi dataset stands as an extensive compilation of annotated texts meticulously crafted to discern hate speech across diverse social networks.
Comprising 10,000 unpublished documents sourced from Twitter, this repository offers a refined presentation of the TuPi
dataset tailored for adept handling in both binary and multiclass classification tasks.For additional insights into the dataset's construction, refer to the following section.
Table 1 provides a detailed breakdown of the dataset, delineating the volume of data based on the occurrence of aggressive speech and the manifestation of hate speech within the documents
**Table 1 - Count of documents for categories non-aggressive and aggressive.**
| Label | Count |
|----------------------|--------|
| Non-aggressive | 31121 |
| Aggressive - Not hate| 3180 |
| Aggressive - Hate | 9367 |
| Total | 43668 |
Table 2 provides a detailed analysis of the dataset, delineating the data volume in relation to the occurrence of distinct categories of hate speech.
**Table 2 - Count of documents for categories non-aggressive and aggressive.**
| Label | Count |
|--------------------------|-------|
| Ageism | 57 |
| Aporophobia | 66 |
| Body shame | 285 |
| Capacitism | 99 |
| LGBTphobia | 805 |
| Political | 1149 |
| Racism | 290 |
| Religious intolerance | 108 |
| Misogyny | 1675 |
| Xenophobia | 357 |
| Other | 4476 |
| Total | 9367 |
# Dataset creation
To overcome the notable shortcomings in existing Portuguese repositories of hate speech instances, we present the TuPI dataset.
Recognizing the importance of prior research in this domain and the absence of annotated datasets for automated hate speech detection,
we propose consolidating this comprehensive dataset by integrating the discoveries from [Fortuna et al. (2019)](https://aclanthology.org/W19-3510/); [Leite et al. (2020)](https://arxiv.org/abs/2010.04543); [Vargas et al. (2022)](https://arxiv.org/abs/2103.14972),
alongside a new, proprietary dataset.
Regarding the unpublished part of the TuPI dataset, we spent about seven months, from March 2023 to September 2023, building the corpus. We collaborated with a team of experts, including a linguist, a human rights lawyer, several behavior psychologists with master’s degrees, and NLP and machine learning researchers.
A framework inspired by [Vargas et al. (2022)](https://github.com/franciellevargas/HateBR/tree/main) and [Fortuna (2017)](https://github.com/paulafortuna/Portuguese-Hate-Speech-Dataset) was adhered to by establishing a stringent set of criteria for the selection of annotators, encompassing the following key attributes:
* Diverse political orientations, encompassing individuals from the right-wing, liberal, and far-left spectrums.
* A high level of academic attainment comprising individuals with master’s degrees, doctoral candidates, and holders of doctoral degrees.
* Expertise in fields of study closely aligned with the focus and objectives of our research.
The subsequent table provides a concise summary of the annotators' profiles and qualifications Table 3.
**Table 3 – Annotators’ profiles and qualifications.**
| Annotator | Gender | Education | Political | Color |
|--------------|--------|-----------------------------------------------|------------|--------|
| Annotator 1 | Female | Ph.D. Candidate in civil engineering | Far-left | White |
| Annotator 2 | Male | Master's candidate in human rights | Far-left | Black |
| Annotator 3 | Female | Master's degree in behavioral psychology | Liberal | White |
| Annotator 4 | Male | Master's degree in behavioral psychology | Right-wing | Black |
| Annotator 5 | Female | Ph.D. Candidate in behavioral psychology | Liberal | Black |
| Annotator 6 | Male | Ph.D. Candidate in linguistics | Far-left | White |
| Annotator 7 | Female | Ph.D. Candidate in civil engineering | Liberal | White |
| Annotator 8 | Male | Ph.D. Candidate in civil engineering | Liberal | Black |
| Annotator 9 | Male | Master's degree in behavioral psychology | Far-left | White |
To consolidate data from the prominent works in the domain of automatic hate speech detection in Portuguese, we established a database by merging labeled document sets from [Fortuna et al. (2019)](https://aclanthology.org/W19-3510/); [Leite et al. (2020)](https://arxiv.org/abs/2010.04543); [Vargas et al. (2022)](https://arxiv.org/abs/2103.14972). To ensure consistency and compatibility in our dataset, we applied the following guidelines for text integration:
* Fortuna et al. (2019) constructed a database comprising 5,670 tweets, each labeled by three distinct annotators, to determine the presence or absence of hate speech. To maintain consistency, we employed a simple majority-voting process for document classification.
* The corpus compiled by Leite et al. (2020) consists of 21,000 tweets labeled by 129 volunteers, with each text assessed by three different evaluators. This study encompassed six types of toxic speech: homophobia, racism, xenophobia, offensive language, obscene language, and misogyny. Texts containing offensive and obscene language were excluded from the hate speech categorization. Following this criterion, we applied a straightforward majority-voting process for classification.
* Vargas et al. (2022) compiled a collection of 7,000 comments extracted from the Instagram platform, with three annotators labeled. These data had previously undergone a simple majority-voting process, eliminating the need for additional text classification procedures.
After completing the previous steps, the corpus was annotated using two different classification levels.
The initial level involves a binary classification, distinguishing between aggressive and non-aggressive language.
The second classification level involved assigning a hate speech category to each tweet previously marked as aggressive in the previous step.
The categories used included ageism, aporophobia, body shame, capacitism, LGBTphobia, political, racism, religious intolerance, misogyny, and xenophobia.
It is important to note that a single tweet could fall under one or more of these categories.
## References
[1] P. Fortuna, J. Rocha Da Silva, J. Soler-Company, L. Wanner, and S. Nunes, “A Hierarchically-Labeled Portuguese Hate Speech Dataset,” 2019. [Online]. Available: https://github.com/t-davidson/hate-s
[2] J. A. Leite, D. F. Silva, K. Bontcheva, and C. Scarton, “Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis,” Oct. 2020, [Online]. Available: http://arxiv.org/abs/2010.04543
[3] F. Vargas, I. Carvalho, F. Góes, T. A. S. Pardo, and F. Benevenuto, “HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection,” 2022. [Online]. Available: https://aclanthology.org/2022.lrec-1.777/
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LND-EDUCATION/Synthetic_audio_bambara
|
LND-EDUCATION
| 2023-11-25T16:57:22Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-25T16:51:23Z |
---
license: apache-2.0
---
|
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] |
Gus1993ever/Khrome1
|
Gus1993ever
| 2023-11-25T17:33:08Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T17:31:05Z |
---
license: openrail
---
|
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] |
Gabriel1322/jotac
|
Gabriel1322
| 2023-11-25T17:34:33Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T17:33:44Z |
---
license: openrail
---
|
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] |
stanmalkinson199/2Dphasetwo
|
stanmalkinson199
| 2023-11-25T17:36:22Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T17:35:45Z |
---
license: openrail
---
|
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drscotthawley/SignalTrain-LA2A
|
drscotthawley
| 2023-11-25T17:59:40Z | 0 | 0 |
[
"license:cc-by-4.0",
"arxiv:1905.11928",
"arxiv:2102.06200",
"arxiv:2006.05584",
"region:us"
] | null | 2023-11-25T17:48:25Z |
---
license: cc-by-4.0
---
# SignalTrain LA2A Dataset (v.1.1)
> Downloadable from https://zenodo.org/records/3824876
20 GB of audio in & audio out for a LA-2A compressor unit, conditioned on knob variations.
LA-2A Compressor data to accompany the paper "SignalTrain: Profiling Audio Compressors with Deep Neural Networks," 147th Audio Engineering Society Convention (AES), 2019. https://arxiv.org/abs/1905.11928
Accompanying computer code: https://github.com/drscotthawley/signaltrain
A collection of recorded data from an analog Teletronix LA-2A opto-electronic compressor, for various settings of the Peak Reduction knob. Other knobs were kept constant.
Audio samples present in these files are either 'randomly generated', or downloaded audio clips with Create Commons licenses, or are property of Scott Hawley freely distributed as part of this dataset.
Data taken by Ben Colburn, supervised by Scott Hawley
## Revisions in v.1.1 of dataset:
Made the following corrections to discrepancies in original dataset:
Only one of file: 235, 236
$ rm Train/target_235_LA2A_2c__0__70.wav
$ rm Val/input_236_.wav
In wrong directory: 245
$ mv Train/input_245_.wav Val/
Mismatched length and time alignment: 148, 148, 149, 150, 152
All were had targets delayed by 8583 samples relative to inputs, and were shorter.
Truncated beginning of inputs to make them the same as targets. Used new script check_dataset.py to fix & overwrite earlier version:
$ signaltrain/utils/check_dataset.py --fix SignalTrain_LA2A_Dataset/
## Papers dataset was used in:
"Efficient neural networks for real-time analog audio effect modeling" by C. Steinmetz & J. Reiss, 2021. https://arxiv.org/abs/2102.06200
“Exploring quality and generalizability in parameterized neural audio effects," by W. Mitchell and S. H. Hawley, 149th Audio Engineering Society Convention (AES), 2020. https://arxiv.org/abs/2006.05584
"SignalTrain: Profiling Audio Compressors with Deep Neural Networks," 147th Audio Engineering Society Convention (AES), 2019. https://arxiv.org/abs/1905.11928
|
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] |
Ichsan2895/DPO_ID-Wiki_10kTesting
|
Ichsan2895
| 2023-11-25T18:19:29Z | 0 | 0 |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-11-25T17:55:48Z |
---
license: cc-by-nc-sa-4.0
---
## HOW TO WRANGLING THIS DATASET TO DPO & CHATML FORMAT
```
def return_prompt_and_responses(samples) -> dict[str, str, str]:
return {
"prompt": [
"<|im_start|>user\n" + i + "<|im_end|>\n"
for i in samples["PROMPT"]
],
"chosen": [
"<|im_start|>assistant\n" + j + "<|im_end|>"
for j in samples["CHOSEN"]
],
"rejected": [
"<|im_start|>assistant\n" + k + "<|im_end|>"
for k in samples["REJECTED"]
],
}
dataset = load_dataset(
"Ichsan2895/DPO_ID-Wiki_10kTesting",
)
original_columns = dataset.column_names
dataset.map(
return_prompt_and_responses,
batched=True,
remove_columns=original_columns
)
```
## HOW TO USE DPO
```
dpo_trainer = DPOTrainer(
model, # base model from SFT pipeline
model_ref, # typically a copy of the SFT trained base model
beta=0.1, # temperature hyperparameter of DPO
train_dataset=dataset['train'], # dataset prepared above
tokenizer=tokenizer, # tokenizer
args=training_args, # training arguments e.g. batch size, lr, etc.
)
```
## CITATION
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
@misc{vonwerra2022trl,
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang},
title = {TRL: Transformer Reinforcement Learning},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
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] |
breno30/wandaRV
|
breno30
| 2023-11-25T18:03:17Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T18:02:13Z |
---
license: openrail
---
|
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] |
Gabriel1322/jotacee
|
Gabriel1322
| 2023-11-25T18:06:58Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T18:04:50Z |
---
license: openrail
---
|
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] |
Marcos7fytyg/Dataset.Pain
|
Marcos7fytyg
| 2023-11-25T18:13:56Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-25T18:08:55Z |
---
license: apache-2.0
---
|
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] |
Gabriel1322/joaocaetano
|
Gabriel1322
| 2023-11-25T18:19:55Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T18:19:10Z |
---
license: openrail
---
|
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Gabriel1322/joaocaetano22
|
Gabriel1322
| 2023-11-25T18:24:04Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T18:24:04Z |
---
license: openrail
---
|
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] |
tennant/C-VQA
|
tennant
| 2023-11-27T16:25:56Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-25T18:31:51Z |
---
configs:
- config_name: default
data_files:
- split: test
path: "C-VQA-Real_questions.csv"
---
The dataset repo contains the data for C-VQA-Real dataset, for complete data and evaluating your model on our dataset, please refer to https://github.com/Letian2003/C-VQA.
|
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chavanarvind/faces
|
chavanarvind
| 2023-11-25T18:43:19Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-25T18:43:19Z |
---
license: apache-2.0
---
|
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MJFMBR/MJ
|
MJFMBR
| 2023-11-25T19:02:17Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T19:01:43Z |
---
license: openrail
---
|
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lidiashishina/car-parts-segmentation-tck28
|
lidiashishina
| 2023-11-25T19:21:10Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-25T19:21:10Z |
---
license: apache-2.0
---
|
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harpreetsahota/elicit-bias-prompts
|
harpreetsahota
| 2023-11-29T19:20:01Z | 0 | 1 |
[
"arxiv:2202.03286",
"region:us"
] | null | 2023-11-25T19:42:28Z |
---
dataset_info:
features:
- name: Prompt
dtype: string
splits:
- name: train
num_bytes: 3851
num_examples: 64
download_size: 2447
dataset_size: 3851
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# 🕵️♂️🤖 Language Model Bias Exploration
## 🌐 Introduction
In this dataset, I've adopted the approach from ["Red Teaming Language Models with Language Models"](https://arxiv.org/abs/2202.03286) by Ethan Perez et al., focusing on exploring and understanding distributional bias in language models (LMs).
## 🎯 Purpose of the Prompts
The prompts in this repository are riffs on the prompts presented in by Table 12 and Tabel 13 in Perez et al.'s paper, serve a crucial role. They are designed to elicit responses from LMs that reveal how different groups are represented and discussed. These prompts help in identifying distributional biases - biases in the frequency and context in which LMs portray certain groups, which might be negative or stereotypical.
## 📊 Addressing Distributional Bias
Distributional bias is a subtle yet pervasive form of bias where certain groups are more often associated with negative contexts or sentiments. This project aims to uncover such biases in LMs by analyzing how these models respond to various group-related prompts.
## 📈 Dataset and Analysis
The dataset comprises variations of prompts used to test and analyze the responses of LMs. By examining these responses, I aim to shed light on the biases present in current language models, contributing to the field of AI ethics.
## 🎖️ Goal
The ultimate goal of this exploration is to contribute towards more ethical and responsible AI development, ensuring that language models treat all groups with fairness and without bias.
|
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] |
xanetti/cars_dataset
|
xanetti
| 2023-11-25T19:46:11Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-25T19:46:11Z |
---
license: mit
---
|
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Pablao0948/Marina_Sena
|
Pablao0948
| 2023-11-25T19:51:50Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T19:50:23Z |
---
license: openrail
---
|
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] |
harpreetsahota/adversarial-prompts
|
harpreetsahota
| 2023-11-29T19:25:57Z | 0 | 1 |
[
"arxiv:2306.09442",
"region:us"
] | null | 2023-11-25T20:14:19Z |
---
dataset_info:
features:
- name: Prompt
dtype: string
splits:
- name: train
num_bytes: 2366
num_examples: 37
download_size: 2228
dataset_size: 2366
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Language Model Testing Dataset 📊🤖
## Introduction 🌐
This repository provides a dataset inspired by the paper ["Explore, Establish, Exploit: Red Teaming Language Models from Scratch"](https://arxiv.org/abs/2306.09442) It's designed for anyone interested in testing language models (LMs) for biases, toxicity, and misinformation.
## Dataset Origin 📝
The dataset is based on examples from Tables 7 and 8 of the paper, which illustrate how prompts can elicit not just biased but also toxic or nonsensical responses from LMs.
### Toxicity and Untruths 🤬
The prompts here, derived from red-teaming GPT-3-text-davinci-002 with classifiers trained on the CREAK dataset, are intended to elicit responses that can reveal tendencies towards toxicity or untruths.
### Nonsense Responses 🤪
Similarly, the prompts from Table 8 are structured to test LM responses for nonsensical or toxic content. These were initially used against GPT-3-text-davinci-002 with classifiers trained on ChatGPT-3.5-turbo labels.
## Purpose of the Dataset 🎯
This dataset is provided as a tool for researchers and developers to test their own LMs. It's particularly useful for evaluating how different models handle potentially problematic content, whether it's biased, toxic, or factually incorrect.
## Using the Dataset 🛠️
Feel free to use this dataset to assess the response patterns of any LM. It's a valuable resource for identifying areas where LMs might need improvement in handling sensitive or complex content.
## Goal 🎖️
The aim is to facilitate broader research into making LMs safer, more reliable, and ethically responsible by providing a ready-to-use dataset for testing and analysis.
|
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DataStudio/OCRNameEntityRed_part2
|
DataStudio
| 2023-11-25T20:15:41Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-25T20:15:38Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: '14'
num_bytes: 9326603.25
num_examples: 1150
- name: '30'
num_bytes: 6985940.25
num_examples: 1150
- name: Oxford
num_bytes: 6934480.25
num_examples: 1150
download_size: 23479178
dataset_size: 23247023.75
configs:
- config_name: default
data_files:
- split: '14'
path: data/14-*
- split: '30'
path: data/30-*
- split: Oxford
path: data/Oxford-*
---
|
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tonysun9/flickr-t5
|
tonysun9
| 2023-11-25T20:18:28Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-25T20:18:25Z |
---
license: apache-2.0
---
|
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Gabriel1322/joaocaetanodataset
|
Gabriel1322
| 2023-11-25T21:30:21Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T20:56:03Z |
---
license: openrail
---
|
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boborr/FLUTTER
|
boborr
| 2023-11-25T21:20:18Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T21:19:42Z |
---
license: openrail
---
|
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] |
Gabriel1322/jutalo
|
Gabriel1322
| 2023-11-25T21:53:16Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T21:51:13Z |
---
license: openrail
---
|
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] |
Gabriel1322/austin
|
Gabriel1322
| 2023-11-25T22:48:45Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T22:33:47Z |
---
license: openrail
---
|
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] |
yerkekz/mini-platypus
|
yerkekz
| 2023-11-25T22:36:52Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-25T22:36:51Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4186564
num_examples: 1000
download_size: 2245924
dataset_size: 4186564
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
hlapp/ubergraph
|
hlapp
| 2023-11-25T23:29:34Z | 0 | 0 |
[
"license:bsd-3-clause",
"region:us"
] | null | 2023-11-25T23:14:00Z |
---
license: bsd-3-clause
---
|
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patrickocal/gov-report-kgs
|
patrickocal
| 2023-11-26T00:58:25Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-25T23:15:16Z |
---
license: mit
---
|
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] |
Pablao0948/Austin_Mahone
|
Pablao0948
| 2023-11-25T23:36:19Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T23:33:21Z |
---
license: openrail
---
|
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] |
Pablao0948/Austin_Mahone2
|
Pablao0948
| 2023-11-25T23:41:23Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-25T23:40:46Z |
---
license: openrail
---
|
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Gabriel1322/lucas
|
Gabriel1322
| 2023-11-26T01:13:44Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-26T01:13:23Z |
---
license: openrail
---
|
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OpenGVLab/SA-Med2D-20M
|
OpenGVLab
| 2023-12-04T00:50:56Z | 0 | 0 |
[
"license:cc-by-nc-sa-4.0",
"arxiv:2311.11969",
"arxiv:2308.16184",
"region:us"
] | null | 2023-11-26T01:24:54Z |
---
license: cc-by-nc-sa-4.0
---
# [SA-Med2D-20M](https://arxiv.org/abs/2311.11969)
The largest benchmark dataset for segmentation in the field of medical imaging.
As is well known, the emergence of ImageNet has greatly propelled the development of AI, especially deep learning. It has provided massive data and powerful baseline models for the computer vision community, enabling researchers to achieve breakthroughs in tasks such as natural image classification, segmentation, and detection. However, in the medical image realm, there lack of such a large dataset for developing powerful medical models.
To address the gap in the medical field, we are introducing the largest benchmark dataset for medical image segmentation. This initiative aims to drive the rapid development of AI in healthcare and accelerate the transformation of computational medicine towards a more inclusive direction.
Please visit the [GitHub](https://github.com/OpenGVLab/SAM-Med2D) page and further exploit the dataset!
Due to data privacy and ethical requirements, we currently only provide access to a 16M dataset. We will keep updating and maintaining this database. Please stay tuned for further updates from us.
## 👉 Filesystem Hierarchy
```bash
~/SAM-Med2D-20M
├── images
| ├── mr_00--ACDC--patient001_frame01--x_0006.png
| ├── mr_t1--BraTS2021--BraTS2021_00218--z_0141.png
| ├── ...
| ├── ct_00--CAD_PE--001--x_0125.png
| ├── x_ray--covid_19_ct_cxr--16660_5_1--2d_none.png
|
├── masks
| ├── mr_00--ACDC--patient001_frame01--x_0006--0000_000.png
| ├── mr_t1--BraTS2021--BraTS2021_00218--z_0141--0011_000.png
| ├── ...
| ├── ct_00--CAD_PE--001--x_0125--0000_002.png
| ├── x_ray--covid_19_ct_cxr--16660_5_1--2d_none--0000_001.png
|
├── SAMed2D_v1_class_mapping_id.json
|
├── SAMed2D_v1.json
```
The SA-Med2D-20M dataset is named following the convention below:
```bash
-images
-{modality_sub-modality}--{dataset name}--{ori name}--{dimension_slice}.png
-masks
-{modality_sub-modality}--{dataset name}--{ori name}--{dimension_slice}--{class instance_id}.png
```
Note: "sub-modality" applies only to 3D data, and when "sub-modality" is "00," it indicates either the absence of a sub-modality or an unknown sub-modality type. "dataset name" refers to the specific dataset name that the case is from. "ori name" is the original case name in its dataset. "dimension slice", e.g., "x_100", indicates the dimension along which we split a 3D case as well as the slice ID in this dimension. If we split a 3D case with axis x and the current slice is 100, then the term can be "x_0100". For 2D datasets, the "dimension_slice id" is uniformly set to "2d_none". "class instance_id", unique to masks, encapsulates both category information and instance id, and the detailed information is stored in the "SAMed2D_v1_class_mapping_id.json" file. For instance, if the category "liver" is assigned the ID "0003" and there is only one instance of this category in the case, the "class instance_id" can be denoted as "0003_000". Besides, the category "liver" in the "SAMed2D_v1_class_mapping_id.json" file is formulated as key-value pair with _python-dict_ format: \{"liver": "0003"\}.
The file "SAMed2D_v1_class_mapping_id.json" stores the information for converting class instances. The file "SAMed2D_v1.json" contains the path information for all image and mask pairs.
## 👉 Unzipping split zip files
Windows:
decompress SA-Med2D-16M.zip to automatically extract the other volumes together.
Linux:
1. zip SA-Med2D-16M.zip SA-Med2D-16M.z0* SA-Med2D-16M.z10 -s=0 --out {full}.zip
2. unzip {full}.zip
## 🤝 免责声明
- SA-Med2D-20M是由多个公开的数据集组成，旨在取之于社区，回馈于社区，为研究人员和开发者提供一个用于学术和技术研究的资源。使用本数据集的任何个人或组织（以下统称为“使用者”）需遵守以下免责声明：
1. 数据集来源：本数据集由多个公开的数据集组成，这些数据集的来源已在预印版论文中明确标明。使用者应当遵守原始数据集的相关许可和使用条款。
2. 数据准确性：尽管我们已经努力确保数据集的准确性和完整性，但无法对数据集的准确性作出保证。使用者应自行承担使用数据集可能带来的风险和责任。
3. 责任限制：在任何情况下，数据集的提供者及相关贡献者均不对使用者的任何行为或结果承担责任。
4. 使用约束：使用者在使用本数据集时，应遵守适用的法律法规和伦理规范。使用者不得将本数据集用于非法、侵犯隐私、诽谤、歧视或其他违法或不道德的目的。
5. 知识产权：本数据集的知识产权归原始数据集的相关权利人所有，使用者不得以任何方式侵犯数据集的知识产权。
- 作为非盈利机构，团队倡导和谐友好的开源交流环境，若在开源数据集内发现有侵犯您合法权益的内容，可发送邮件至([email protected], [email protected])，邮件中请写明侵权相关事实的详细描述并向我们提供相关的权属证明资料。我们将于3个工作日内启动调查处理机制，并采取必要的措施进行处置(如下架相关数据)。但应确保您投诉的真实性，否则采取措施后所产生的不利后果应由您独立承担。
- 通过下载、复制、访问或使用本数据集，即表示使用者已阅读、理解并同意遵守本免责声明中的所有条款和条件。如果使用者无法接受本免责声明的任何部分，请勿使用本数据集。
## 🤝 Disclaimer
- SA-Med2D-20M is composed of multiple publicly available datasets and aims to provide a resource for academic and technical research to researchers and developers. Any individual or organization (hereinafter referred to as "User") using this dataset must comply with the following disclaimer:
1. Dataset Source: SA-Med2D-20M is composed of multiple publicly available datasets, and the sources of these datasets have been clearly indicated in the preprint paper. Users should adhere to the relevant licenses and terms of use of the original datasets.
2. Data Accuracy: While efforts have been made to ensure the accuracy and completeness of the dataset, no guarantee can be given regarding its accuracy. Users assume all risks and liabilities associated with the use of the dataset.
3. Limitation of Liability: Under no circumstances shall the dataset providers or contributors be held liable for any actions or outcomes of the Users.
4. Usage Constraints: Users must comply with applicable laws, regulations, and ethical norms when using this dataset. The dataset must not be used for illegal, privacy-infringing, defamatory, discriminatory, or other unlawful or unethical purposes.
5. Intellectual Property: The intellectual property rights of this dataset belong to the relevant rights holders of the original datasets. Users must not infringe upon the intellectual property rights of the dataset in any way.
- As a non-profit organization, we advocate for a harmonious and friendly open-source communication environment. If any content in the open dataset is found to infringe upon your legitimate rights and interests, you can send an email to ([email protected], [email protected]) with a detailed description of the infringement and provide relevant ownership proof materials. We will initiate an investigation and handling mechanism within three working days and take necessary measures (such as removing relevant data) if warranted. However, the authenticity of your complaint must be ensured, as any adverse consequences resulting from the measures taken shall be borne solely by you.
- By downloading, copying, accessing, or using this dataset, the User indicates that they have read, understood, and agreed to comply with all the terms and conditions of this disclaimer. If the User cannot accept any part of this disclaimer, please refrain from using this dataset.
## 🤝 Acknowledgement
- We thank all medical workers and dataset owners for making public datasets available to the community. If you find that your dataset is included in our SA-Med2D-20M but you do not want us to do so, please contact us to remove it.
## 👋 Hiring & Global Collaboration
- **Hiring:** We are hiring researchers, engineers, and interns in General Vision Group, Shanghai AI Lab. If you are interested in Medical Foundation Models and General Medical AI, including designing benchmark datasets, general models, evaluation systems, and efficient tools, please contact us.
- **Global Collaboration:** We're on a mission to redefine medical research, aiming for a more universally adaptable model. Our passionate team is delving into foundational healthcare models, promoting the development of the medical community. Collaborate with us to increase competitiveness, reduce risk, and expand markets.
- **Contact:** Junjun He([email protected]), Jin Ye([email protected]), and Tianbin Li ([email protected]).
## 👉 Typos of paper
1. Formula (1) is incorrect, after correction: <img src="https://i.postimg.cc/sXRK4MKh/20231123001020.png" alt="alt text" width="202" height="50">
## Reference
```
@misc{ye2023samed2d20m,
title={SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks},
author={Jin Ye and Junlong Cheng and Jianpin Chen and Zhongying Deng and Tianbin Li and Haoyu Wang and Yanzhou Su and Ziyan Huang and Jilong Chen and Lei Jiang and Hui Sun and Min Zhu and Shaoting Zhang and Junjun He and Yu Qiao},
year={2023},
eprint={2311.11969},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
@misc{cheng2023sammed2d,
title={SAM-Med2D},
author={Junlong Cheng and Jin Ye and Zhongying Deng and Jianpin Chen and Tianbin Li and Haoyu Wang and Yanzhou Su and
Ziyan Huang and Jilong Chen and Lei Jiangand Hui Sun and Junjun He and Shaoting Zhang and Min Zhu and Yu Qiao},
year={2023},
eprint={2308.16184},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
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] |
kelen0102/Enrico_Pucci
|
kelen0102
| 2023-11-26T01:51:08Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-26T01:50:43Z |
---
license: openrail
---
|
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Bluebomber182/Ariel-From-The-Little-Mermaid
|
Bluebomber182
| 2023-11-26T02:40:07Z | 0 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-26T02:38:10Z |
---
license: unknown
---
|
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yuyijiong/Multi-doc-QA-CommonCrawl
|
yuyijiong
| 2023-12-04T11:37:12Z | 0 | 1 |
[
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-11-26T04:04:20Z |
---
license: cc-by-nc-4.0
size_categories:
- 10K<n<100K
language:
- en
---
* Update on December 24, 2023: Improve the format of answers： force all answers to be provide the refered original text first.
* English multi document Q&A data created using RedPajamaCommonCrawl data as reference text
* In the Raw dataset, each sample contains <font
color=red> one reference document, 199 irrelevant documents, and a Q-A pair based on the reference document</font>. It can be used to train models to extract the target information from a large number of documents.
* After filtering, integrating, and transforming the raw data into chatml format instruction fine-tuning data, each sample contains approximately 30 reference documents and 5 corresponding QA pairs.
<br/>
* 2023.12.4更新：改进答案的格式，强制所有答案在回答时必须先给出原文。
* 以RedPajamaCommonCrawl数据为参考文本，制作的英文多文档问答数据
* 原始数据中，每个样本包含 <font color=red> 一个参考文档、199个无关文档、一个基于参考文档的问答对</font>。可以训练模型从大量文档中抽取关键信息的能力。
* 原始数据经过筛选、整合转化为chatml形式的指令微调数据后，每条数据大约包含30个参考文档，以及5个对应的问答对。
* dataset size: 11k
|
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pykeio/librivox-tracks
|
pykeio
| 2023-11-26T04:25:32Z | 0 | 0 |
[
"task_categories:text-to-speech",
"task_categories:automatic-speech-recognition",
"size_categories:100K<n<1M",
"license:cc-by-4.0",
"region:us"
] |
[
"text-to-speech",
"automatic-speech-recognition"
] | 2023-11-26T04:21:02Z |
---
license: cc-by-4.0
task_categories:
- text-to-speech
- automatic-speech-recognition
pretty_name: LibriVox Tracks
size_categories:
- 100K<n<1M
---
A dataset of all audio files uploaded to LibriVox before 26th September 2023.
|
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dread1900/DMZ-V2
|
dread1900
| 2023-11-26T04:49:19Z | 0 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-26T04:44:58Z |
---
license: unknown
---
|
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] |
xinyu1205/recognize-anything-dataset
|
xinyu1205
| 2023-11-28T09:03:12Z | 0 | 0 |
[
"task_categories:zero-shot-classification",
"size_categories:10M<n<100M",
"language:en",
"license:apache-2.0",
"image recognition",
"region:us"
] |
[
"zero-shot-classification"
] | 2023-11-26T07:39:53Z |
---
license: apache-2.0
language:
- en
tags:
- image recognition
task_categories:
- zero-shot-classification
size_categories:
- 10M<n<100M
---
# Recognize Anything Dataset Card
## Dataset details
**Dataset type:**
These annotation files come from the Recognize Anything Model (RAM). RAM propose an automatic data engine to generate substantial image tags from image-text pairs.
**Dataset date:**
Recognize Anything Dataset was collected in April 2023, by an automatic data engine proposed by RAM.
**Paper or resources for more information:**
https://github.com/xinyu1205/recognize-anything
**Where to send questions or comments about the model:**
https://github.com/xinyu1205/recognize-anything/issues
## Intended use
**Primary intended uses:**
The primary use of Recognize Anything is research on fundamental image recognition models.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
|
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] |
miittnnss/testdataset
|
miittnnss
| 2023-11-26T08:39:42Z | 0 | 0 |
[
"pytorch",
"region:us"
] | null | 2023-11-26T08:12:20Z |
---
pretty_name: TestDataset
tags:
- pytorch
---
|
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satware/yggdrasil
|
satware
| 2023-11-26T09:52:04Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-26T09:52:04Z |
---
license: mit
---
|
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] |
code-philia/mtpnet
|
code-philia
| 2023-11-26T09:58:18Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-26T09:58:18Z |
---
license: mit
---
|
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jinghan23/DatasetofPEMCompostition
|
jinghan23
| 2023-11-26T10:12:50Z | 0 | 1 |
[
"license:cc-by-nc-4.0",
"arxiv:2306.14870",
"region:us"
] | null | 2023-11-26T10:03:37Z |
---
license: cc-by-nc-4.0
---
Training dataset for Alpaca-LoRA negation of [PEM composition](https://arxiv.org/abs/2306.14870).
Instructions for model evaluation on helpfulness and toxicity.
More concise interpretation can be found at [Git repo](https://github.com/hkust-nlp/PEM_composition).
**Toxic datasets abusement is dangerous for AI community, so this repo is gated and users have to request for approval.**
|
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] |
nookbe/Buergerliches_Gesetzbuch_BGB
|
nookbe
| 2023-11-26T11:44:58Z | 0 | 0 |
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:de",
"license:mit",
"legal",
"region:us"
] |
[
"text-classification"
] | 2023-11-26T10:15:25Z |
---
license: mit
task_categories:
- text-classification
language:
- de
tags:
- legal
pretty_name: BGB
size_categories:
- 1K<n<10K
---
# German BGB Law Dataset (Bürgerliches Gesetzbuch)
## Dataset Description
- **Date of Last Paragraph Update:** April 2023
- **Dataset Guarantee:** The dataset is provided "as is," and there is no guarantee for the correctness or completeness of the data.
### Dataset Summary
The BGB Law Dataset contains legal text from the German Civil Code (Bürgerliches Gesetzbuch - BGB). It focuses on the general principles of German civil law, and the dataset is designed for tasks related to legal text analysis.
## Dataset Structure
### Data Instances
A typical data point in the dataset comprises a legal paragraph and its corresponding text. For example:
```json
{
'paragraph': '§ 1 Beginn der Rechtsfähigkeit',
'text': 'Die Rechtsfähigkeit des Menschen beginnt mit der Vollendung der Geburt.'
}
```
|
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wrrdhfj/BanGDream
|
wrrdhfj
| 2023-11-26T15:11:55Z | 0 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-26T10:41:25Z |
---
license: unknown
---
|
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washimneupane/stackCluster10
|
washimneupane
| 2023-11-26T10:43:37Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-26T10:43:37Z |
---
license: mit
---
|
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luxunlab/lu-xun-texts
|
luxunlab
| 2023-11-26T11:02:43Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-26T10:58:44Z |
---
license: mit
---
|
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maywell/hh-rlhf-harmyes
|
maywell
| 2023-11-26T22:41:29Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-26T11:37:39Z |
---
dataset_info:
features:
- name: rejected
dtype: string
- name: chosen
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 19681345
num_examples: 42537
download_size: 0
dataset_size: 19681345
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hh-rlhf-harmyes"
Original Dataset: https://huggingface.co/datasets/Anthropic/hh-rlhf
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
jedwang/bert-base-split-chinese
|
jedwang
| 2023-11-26T11:38:47Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-26T11:38:22Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 596090928
num_examples: 160030
download_size: 121094285
dataset_size: 596090928
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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nookbe/Handelsgesetzbuch_HGB
|
nookbe
| 2023-11-26T11:53:18Z | 0 | 0 |
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:de",
"license:mit",
"legal",
"region:us"
] |
[
"text-classification"
] | 2023-11-26T11:48:25Z |
---
license: mit
task_categories:
- text-classification
language:
- de
tags:
- legal
pretty_name: HGB
size_categories:
- 1K<n<10K
---
license: mit
task_categories:
- text-classification
language:
- de
tags:
- legal
pretty_name: HGB
size_categories:
- 1K<n<10K
---
# German HGB Law Dataset (Handelsgesetzbuch)
## Dataset Description
- **Date of Last Paragraph Update:** April 2023
- **Dataset Guarantee:** The dataset is provided "as is," and there is no guarantee for the correctness or completeness of the data.
### Dataset Summary
The HGB Law Dataset contains legal text from the German Commercial Code (Handelsgesetzbuch - HGB). It focuses on the general principles of German commercial law, and the dataset is designed for tasks related to legal text analysis.
## Dataset Structure
### Data Instances
A typical data point in the dataset comprises a legal paragraph and its corresponding text. For example:
```json
{
'paragraph': '§ 1 Handelsstand',
'text': 'Wer ein Handelsgewerbe betreibt, ist Kaufmann.'
}
```
|
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] |
laiyer/image-prompt-injection
|
laiyer
| 2023-11-26T11:52:01Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-26T11:52:01Z |
---
license: apache-2.0
---
|
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] |
JestemKamil/NexiaBot-Dataset
|
JestemKamil
| 2023-11-26T12:33:09Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-26T12:33:08Z |
---
dataset_info:
features:
- name: conversation
struct:
- name: conversationId
dtype: int64
- name: conversationName
dtype: string
- name: messages
list:
- name: role
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 43467
num_examples: 144
download_size: 22650
dataset_size: 43467
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
yasosa/testulan
|
yasosa
| 2023-11-26T12:34:25Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-26T12:33:54Z |
---
license: apache-2.0
---
|
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trungnguyentran/OpenSubtitles
|
trungnguyentran
| 2023-11-26T13:12:24Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-26T13:12:18Z |
Invalid username or password.
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] |
hajar817/CV_13_0_fa_pseudo_labelled
|
hajar817
| 2023-11-26T21:20:39Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-26T14:28:04Z |
---
dataset_info:
config_name: fa
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: variant
dtype: string
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 785772315.776
num_examples: 28024
- name: validation
num_bytes: 361940801.52
num_examples: 10440
- name: test
num_bytes: 424908955.64
num_examples: 10440
download_size: 1342676850
dataset_size: 1572622072.9359999
configs:
- config_name: fa
data_files:
- split: train
path: fa/train-*
- split: validation
path: fa/validation-*
- split: test
path: fa/test-*
---
|
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] |
KiramekiSunnyPro/aina
|
KiramekiSunnyPro
| 2023-11-26T15:16:20Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-26T15:15:23Z |
---
license: openrail
---
|
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] |
Gabriel1322/jotase
|
Gabriel1322
| 2023-11-26T15:58:33Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-26T15:55:24Z |
---
license: openrail
---
|
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chrominancedesign/hf
|
chrominancedesign
| 2023-11-26T16:14:06Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-26T16:14:06Z |
---
license: apache-2.0
---
|
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] |
Pablao0948/Leozinho
|
Pablao0948
| 2023-11-26T16:58:00Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-26T16:57:07Z |
---
license: openrail
---
|
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Kauasido/PACKMCDALESTE
|
Kauasido
| 2023-11-26T17:49:21Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-26T17:47:37Z |
---
license: openrail
---
|
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] |
isabelabr/raffamoreira
|
isabelabr
| 2023-11-26T18:00:35Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-26T18:00:01Z |
---
license: openrail
---
|
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onlycaps/flickr8k-sau-pace-annotated
|
onlycaps
| 2023-11-26T18:30:31Z | 0 | 0 |
[
"task_categories:image-classification",
"license:mit",
"region:us"
] |
[
"image-classification"
] | 2023-11-26T18:20:45Z |
---
license: mit
task_categories:
- image-classification
---
# Annotation
Annotated this [dataset](https://www.kaggle.com/datasets/srbhshinde/flickr8k-sau) by clasifying the images into slow, medium or fast depending on the suitable paced background music.
|
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] |
Rishabh621/Rishabh_voicewover
|
Rishabh621
| 2023-11-26T18:34:18Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-26T18:34:18Z |
---
license: apache-2.0
---
|
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] |
isabelabr/bcraffthebox
|
isabelabr
| 2023-11-26T18:39:43Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-26T18:39:24Z |
---
license: openrail
---
|
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] |
Vergilzx8/mmmmmm
|
Vergilzx8
| 2023-11-26T18:48:27Z | 0 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-26T18:48:27Z |
---
license: unknown
---
|
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mespinosami/elephants
|
mespinosami
| 2023-11-26T19:16:36Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-26T18:53:42Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': eles
'1': no_eles
splits:
- name: train
num_bytes: 14514122908.549025
num_examples: 159283
- name: test
num_bytes: 3662606794.077976
num_examples: 39821
download_size: 17959333418
dataset_size: 18176729702.627
---
# Dataset Card for "elephants"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Trollator/mcigu
|
Trollator
| 2023-11-26T18:57:56Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-26T18:57:22Z |
---
license: openrail
---
|
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Pablao0948/Austin_Mahonne
|
Pablao0948
| 2023-11-26T19:20:29Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-26T19:19:55Z |
---
license: openrail
---
|
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aatherton2024/ds_attemp_many
|
aatherton2024
| 2023-11-26T19:24:00Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-26T19:23:29Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: classification
dtype: string
splits:
- name: train
num_bytes: 2560750.0
num_examples: 5
download_size: 1700353
dataset_size: 2560750.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
Hidromaniaco/hwei
|
Hidromaniaco
| 2023-11-26T19:36:30Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-26T19:29:53Z |
---
license: openrail
---
|
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AppleHarem/ch_en_arknights
|
AppleHarem
| 2023-11-26T20:30:48Z | 0 | 0 |
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
[
"text-to-image"
] | 2023-11-26T20:29:58Z |
---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of ch'en (Arknights)
This is the dataset of ch'en (Arknights), containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).([LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI))
| Name | Images | Download | Description |
|:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------|
| raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 490 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 605 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 490 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 490 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-p512-640 | 339 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. |
| stage3-eyes-640 | 605 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 605 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
|
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] |
HossainRabby/UpdatedDataset
|
HossainRabby
| 2023-11-28T18:25:11Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-26T20:30:35Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 72929903.08721887
num_examples: 14766
- name: test
num_bytes: 8104968.91278113
num_examples: 1641
download_size: 26912462
dataset_size: 81034872.0
---
# Dataset Card for "hii"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Gabriel1322/lucasdataset
|
Gabriel1322
| 2023-11-26T20:42:42Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-26T20:42:07Z |
---
license: openrail
---
|
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] |
Tony-Yuan/TheElements
|
Tony-Yuan
| 2023-11-27T03:05:55Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-26T20:52:53Z |
---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path:
- "train.csv"
---
This is a GPT-4 generated question-and-answer dataset based on [The Elements](https://www.amazon.com/Elements-Visual-Exploration-Every-Universe/dp/1579128955/) by [Theodore Gray](https://home.theodoregray.com/).
|
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VictorHProtogen/Amigos
|
VictorHProtogen
| 2023-11-26T22:08:30Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-26T20:56:53Z |
---
license: openrail
---
|
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Erynan/1k_deon_util
|
Erynan
| 2023-11-26T21:22:55Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-26T21:17:35Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1499818
num_examples: 2000
download_size: 259937
dataset_size: 1499818
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
DZN222/lucasdataset
|
DZN222
| 2023-11-26T21:21:20Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-26T21:21:02Z |
---
license: openrail
---
|
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Erynan/1k_deon_util_shuffled
|
Erynan
| 2023-11-26T21:23:07Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-26T21:23:05Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1499818
num_examples: 2000
download_size: 259937
dataset_size: 1499818
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
DZN222/morador
|
DZN222
| 2023-11-26T21:33:49Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-26T21:33:35Z |
---
license: openrail
---
|
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tasksource/feasibilityQA
|
tasksource
| 2023-11-26T21:48:54Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-26T21:46:10Z |
---
license: mit
---
|
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DZN222/lucas
|
DZN222
| 2023-11-26T21:56:12Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-26T21:55:11Z |
---
license: openrail
---
|
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