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PALM

PaddlePaddle / PALM

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PALM
提交 e27ffafa 编写于 作者: X xixiaoyao
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fix bugs

上级 f6c68c85
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Showing with 3067 addition and 33 deletion
demo.py → demo1.py
浏览文件 @ e27ffafa
import paddlepalm as palm
if __name__ == '__main__':
controller = palm.Controller('config.yaml', task_dir='task_instance')
controller = palm.Controller('demo1_config.yaml', task_dir='demo1_tasks')
controller.load_pretrain('pretrain_model/ernie/params')
controller.train()
controller = palm.Controller(config='config.yaml', task_dir='task_instance', for_train=False)
controller = palm.Controller(config='demo1_config.yaml', task_dir='demo1_tasks', for_train=False)
controller.pred('mrqa', inference_model_dir='output_model/firstrun/infer_model')
demo1_config.yaml 0 → 100644
浏览文件 @ e27ffafa
task_instance: "mrqa"
target_tag: 1
mix_ratio: 1.0
save_path: "output_model/firstrun"
backbone: "ernie"
backbone_config_path: "pretrain_model/ernie/ernie_config.json"
vocab_path: "pretrain_model/ernie/vocab.txt"
do_lower_case: True
max_seq_len: 512
batch_size: 5
num_epochs: 2
optimizer: "adam"
learning_rate: 3e-5
warmup_proportion: 0.1
weight_decay: 0.1
demo2.py 0 → 100644
浏览文件 @ e27ffafa
import paddlepalm as palm
if __name__ == '__main__':
controller = palm.Controller('demo2_config.yaml', task_dir='demo2_tasks')
controller.load_pretrain('pretrain_model/ernie/params')
controller.train()
controller = palm.Controller(config='demo2_config.yaml', task_dir='demo2_tasks', for_train=False)
controller.pred('mrqa', inference_model_dir='output_model/secondrun/infer_model')
config.yaml → demo2_config.yaml
浏览文件 @ e27ffafa
......@@ -2,7 +2,7 @@ task_instance: "mrqa, match4mrqa"
target_tag: 1, 0
mix_ratio: 1.0, 0.5
save_path: "output_model/firstrun"
save_path: "output_model/secondrun"
backbone: "ernie"
backbone_config_path: "pretrain_model/ernie/ernie_config.json"
......
demo2_tasks/mrqa.yaml 0 → 100644
浏览文件 @ e27ffafa
train_file: data/mrqa/mrqa-combined.train.raw.json
pred_file: data/mrqa/mrqa-combined.dev.raw.json
pred_output_path: 'mrqa_output'
reader: mrc4ernie
paradigm: mrc
doc_stride: 128
max_query_len: 64
max_answer_len: 30
n_best_size: 20
null_score_diff_threshold: 0.0
verbose: False
mrqa_output/nbest_predictions.json 0 → 100644
浏览文件 @ e27ffafa
{
"3f02f171c82e49828580007a71eefc31": [
{
"text": "Ethan",
"probability": 0.07732352843766871,
"start_logit": 0.152540385723114,
"end_logit": 0.6237434148788452
},
{
"text": "in Vermont now\\\"'\",. + \"value\": \n \"$1000\",. + \"answer\": \"Ethan",
"probability": 0.07511850647910423,
"start_logit": 0.12360905110836029,
"end_logit": 0.6237434148788452
},
{
"text": "a Revolutionary War patriot and a prominent early \n citizen of Addison ..... Robert Cochran",
"probability": 0.07410608793522097,
"start_logit": 0.19223906099796295,
"end_logit": 0.5415441393852234
},
{
"text": "a prominent early \n citizen of Addison ..... Robert Cochran",
"probability": 0.07349076758603804,
"start_logit": 0.18390116095542908,
"end_logit": 0.5415441393852234
},
{
"text": "of Addison ..... Robert Cochran",
"probability": 0.07042416842745533,
"start_logit": 0.14127787947654724,
"end_logit": 0.5415441393852234
},
{
"text": "ethnic",
"probability": 0.06577677211975862,
"start_logit": 0.1134892925620079,
"end_logit": 0.5010629892349243
},
{
"text": "of Gettysburg In this park on July 22, 1863, Vermont's only ethnic",
"probability": 0.06485029593973589,
"start_logit": 0.09930399805307388,
"end_logit": 0.5010629892349243
},
{
"text": "Ethan Allen",
"probability": 0.06452661895461645,
"start_logit": 0.152540385723114,
"end_logit": 0.44282296299934387
},
{
"text": "'Revolutionary War hero: \\\"",
"probability": 0.06357011908654853,
"start_logit": 0.1426193118095398,
"end_logit": 0.4378097355365753
},
{
"text": "'Revolutionary War hero",
"probability": 0.06291976618591077,
"start_logit": 0.1426193118095398,
"end_logit": 0.4275265634059906
},
{
"text": "Allen",
"probability": 0.0628159589411946,
"start_logit": 0.12567171454429626,
"end_logit": 0.44282296299934387
},
{
"text": "in Vermont now\\\"'\",. + \"value\": \n \"$1000\",. + \"answer\": \"Ethan Allen\".",
"probability": 0.06268652429543975,
"start_logit": 0.12360905110836029,
"end_logit": 0.44282296299934387
},
{
"text": "War hero: \\\"",
"probability": 0.06116789090649983,
"start_logit": 0.10409817099571228,
"end_logit": 0.4378097355365753
},
{
"text": "a \n ... [SEP] [SEP] Vermont Historical Markers - The Historical Marker Database [SEP] General John Strong",
"probability": 0.06065506524916661,
"start_logit": 0.14022484421730042,
"end_logit": 0.3932638168334961
},
{
"text": "War hero",
"probability": 0.060567929455641545,
"start_logit": 0.10088147968053818,
"end_logit": 0.4311695694923401
}
],
"98d0b8ce19d1434abdb42aa01e83db61": [
{
"text": "MonetDB.R [SEP] I am trying to import a dataset of 217000 records (Jeopardy",
"probability": 0.055370047791018485,
"start_logit": 0.22277489304542542,
"end_logit": 0.45147573947906494
},
{
"text": "MonetDB.R [SEP] I am trying to import a dataset of 217000 records (Jeopardy Data",
"probability": 0.054396132669168484,
"start_logit": 0.22277489304542542,
"end_logit": 0.43373000621795654
},
{
"text": "McDonald's",
"probability": 0.05390215216130058,
"start_logit": 0.23751531541347504,
"end_logit": 0.40986692905426025
},
{
"text": "MonetDB",
"probability": 0.05251072046501029,
"start_logit": 0.2334740310907364,
"end_logit": 0.3877551555633545
},
{
"text": "McDonalds would be serving their 300 billionth burger (of any type). [SEP] [SEP] PPT McDonald",
"probability": 0.05226205000334954,
"start_logit": 0.0866357758641243,
"end_logit": 0.5298465490341187
},
{
"text": "MonetDB.R [SEP] I am trying to import a dataset of 217000 records (Jeopardy Dataset) into \n MonetDB",
"probability": 0.05195189581969177,
"start_logit": 0.22277489304542542,
"end_logit": 0.3877551555633545
},
{
"text": "etDB.R [SEP] I am trying to import a dataset of 217000 records (Jeopardy",
"probability": 0.05152757891069951,
"start_logit": 0.15085327625274658,
"end_logit": 0.45147573947906494
},
{
"text": "'Signer of the Dec. [SEP] [SEP] Unable to Bulk",
"probability": 0.05086033011715986,
"start_logit": 0.2018197774887085,
"end_logit": 0.38747531175613403
},
{
"text": "etDB.R [SEP] I am trying to import a dataset of 217000 records (Jeopardy Data",
"probability": 0.050621249761718896,
"start_logit": 0.15085327625274658,
"end_logit": 0.43373000621795654
},
{
"text": "monetdb",
"probability": 0.04989810750906267,
"start_logit": 0.15555588901042938,
"end_logit": 0.41463902592658997
},
{
"text": "'Signer of the Dec. [SEP] [SEP] Unable to Bulk Import Free flow text MonetDB",
"probability": 0.049782976261900654,
"start_logit": 0.2018197774887085,
"end_logit": 0.3660651445388794
},
{
"text": "'Signer of the Dec. [SEP] [SEP] Unable to Bulk Import Free flow",
"probability": 0.04891172363057805,
"start_logit": 0.2018197774887085,
"end_logit": 0.34840917587280273
},
{
"text": "this company served its billionth burger' ## 5 'Signer of the Dec. [SEP] [SEP] Unable to Bulk",
"probability": 0.04848235435447032,
"start_logit": 0.15393643081188202,
"end_logit": 0.38747531175613403
},
{
"text": "' ## 5 'Signer of the Dec. [SEP] [SEP] Unable to Bulk",
"probability": 0.04783199956096487,
"start_logit": 0.14043138921260834,
"end_logit": 0.38747531175613403
},
{
"text": "a company used another company's name \n in an advert so McDonald's ... Mixing",
"probability": 0.04771581046089063,
"start_logit": 0.141827791929245,
"end_logit": 0.3836468458175659
},
{
"text": "etDB",
"probability": 0.04730928527334633,
"start_logit": 0.15085327625274658,
"end_logit": 0.3660651445388794
},
{
"text": "McDonalds would be serving their 300 billionth burger (of any type). [SEP] [SEP] PPT McDonald PowerPoint",
"probability": 0.047222519450939804,
"start_logit": 0.0866357758641243,
"end_logit": 0.4284469485282898
},
{
"text": "' ## 5 'Signer of the Dec. [SEP] [SEP] Unable to Bulk Import Free flow text MonetDB",
"probability": 0.046818793610215924,
"start_logit": 0.14043138921260834,
"end_logit": 0.3660651445388794
},
{
"text": "this company served its billionth burger' ## 5 'Signer of the Dec. [SEP] [SEP] Unable to Bulk Import Free flow",
"probability": 0.046624855003556655,
"start_logit": 0.15393643081188202,
"end_logit": 0.34840917587280273
},
{
"text": "' ## 5 'Signer of the Dec. [SEP] [SEP] Unable to Bulk Import Free flow",
"probability": 0.045999417184956516,
"start_logit": 0.14043138921260834,
"end_logit": 0.34840917587280273
}
],
"f0bc45a4dd7a4d8abf91a5e4fb25fe57": [
{
"text": "James' epitaph contains this line: Murdered by a",
"probability": 0.07573938698026246,
"start_logit": 0.2192050814628601,
"end_logit": 0.7774304151535034
},
{
"text": "to",
"probability": 0.07411886460697045,
"start_logit": 0.24210990965366364,
"end_logit": 0.7328973412513733
},
{
"text": "to collect the bounty on Jesse's head. [SEP] [SEP] Crime History: Outlaw Jesse James",
"probability": 0.0735471421394526,
"start_logit": 0.3088628053665161,
"end_logit": 0.6584009528160095
},
{
"text": "outlaw Jesse James is shot to",
"probability": 0.07192927173041253,
"start_logit": 0.21212312579154968,
"end_logit": 0.7328973412513733
},
{
"text": "1882, outlaw Jesse James is shot to",
"probability": 0.06892948074666642,
"start_logit": 0.16952379047870636,
"end_logit": 0.7328973412513733
},
{
"text": "James' epitaph contains this line: Murdered by a traitor and a",
"probability": 0.06762480669066112,
"start_logit": 0.2192050814628601,
"end_logit": 0.6641069650650024
},
{
"text": "Outlaw Jesse James",
"probability": 0.06741632163767873,
"start_logit": 0.22182336449623108,
"end_logit": 0.6584009528160095
},
{
"text": "James",
"probability": 0.06616575517659858,
"start_logit": 0.21504205465316772,
"end_logit": 0.6464581489562988
},
{
"text": "James: \"Murdered by a",
"probability": 0.06527208812278226,
"start_logit": 0.1974855363368988,
"end_logit": 0.6504161357879639
},
{
"text": "outlaw Jesse James",
"probability": 0.06288839261302213,
"start_logit": 0.21212312579154968,
"end_logit": 0.5985756516456604
},
{
"text": "James: \"Murdered by a traitor and a coward whose name ... [SEP] In the Wild West, Jesse James",
"probability": 0.06257265831128382,
"start_logit": 0.1974855363368988,
"end_logit": 0.608180046081543
},
{
"text": "James: \"Murdered by a traitor and a",
"probability": 0.06150308608337056,
"start_logit": 0.1974855363368988,
"end_logit": 0.5909389853477478
},
{
"text": "' Robert Ford ... [SEP] Apr 2, 2009 ... On this day, April 3, 1882, outlaw Jesse James",
"probability": 0.061330010501629124,
"start_logit": 0.18703080713748932,
"end_logit": 0.5985756516456604
},
{
"text": "'coward' Robert Ford ... [SEP] Apr 2, 2009 ... On this day, April 3, 1882, outlaw Jesse James",
"probability": 0.06069708559140021,
"start_logit": 0.17665719985961914,
"end_logit": 0.5985756516456604
},
{
"text": "1882, outlaw Jesse James",
"probability": 0.06026564906780889,
"start_logit": 0.16952379047870636,
"end_logit": 0.5985756516456604
}
],
"2dc90736586049d298a10ed93567f0db": [
{
"text": "Steve Tyler and Joe Perry at 2010 \n Aerosmith concert. [SEP] [SEP] The Yardbirds",
"probability": 0.05945435405859669,
"start_logit": 0.3523253798484802,
"end_logit": 0.7926364541053772
},
{
"text": "Steve Tyler and Joe Perry at 2010 \n Aerosmith concert. [SEP] [SEP] The Yardbirds - Wikipedia [SEP] The Yardbirds",
"probability": 0.05745985394105228,
"start_logit": 0.3523253798484802,
"end_logit": 0.7585141062736511
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{
"text": "Aerosmith concert. [SEP] [SEP] The Yardbirds",
"probability": 0.05424811622390354,
"start_logit": 0.2606847882270813,
"end_logit": 0.7926364541053772
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{
"text": "Tyler and Joe Perry at 2010 \n Aerosmith concert. [SEP] [SEP] The Yardbirds",
"probability": 0.05284278889192647,
"start_logit": 0.23443777859210968,
"end_logit": 0.7926364541053772
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{
"text": "Aerosmith concert. [SEP] [SEP] The Yardbirds - Wikipedia [SEP] The Yardbirds",
"probability": 0.05242826844490822,
"start_logit": 0.2606847882270813,
"end_logit": 0.7585141062736511
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{
"text": "his band first recorded \" \n The Train Kept A-Rollin'\"",
"probability": 0.05230385194198798,
"start_logit": 0.35080385208129883,
"end_logit": 0.6660191416740417
},
{
"text": "due ... Tiny Bradshaw",
"probability": 0.05145603260269473,
"start_logit": 0.25936242938041687,
"end_logit": 0.7411182522773743
},
{
"text": "Steven Tyler of this band lent his \n steamin' vocals to \"Train Kept A-Rollin'\",",
"probability": 0.05125052140407431,
"start_logit": 0.409344345331192,
"end_logit": 0.5871344208717346
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{
"text": "Tyler and Joe Perry at 2010 \n Aerosmith concert. [SEP] [SEP] The Yardbirds - Wikipedia [SEP] The Yardbirds",
"probability": 0.05107008527206296,
"start_logit": 0.23443777859210968,
"end_logit": 0.7585141062736511
},
{
"text": "Steven Tyler of this band lent his steamin' vocals to \" \n Train Kept A-Rollin'\",",
"probability": 0.05065796975545232,
"start_logit": 0.3716318905353546,
"end_logit": 0.6132176518440247
},
{
"text": "Yardbirds popularized the song as an early \n psychedelic blues rock song, due ... Tiny Bradshaw",
"probability": 0.05061668428242804,
"start_logit": 0.24291597306728363,
"end_logit": 0.7411182522773743
},
{
"text": "and his band first recorded \" \n The Train Kept A-Rollin'\"",
"probability": 0.05022107360288849,
"start_logit": 0.3101685643196106,
"end_logit": 0.6660191416740417
},
{
"text": "due ... Tiny Bradshaw and his band first recorded \" \n The Train Kept A-Rollin'\"",
"probability": 0.047733267972024374,
"start_logit": 0.25936242938041687,
"end_logit": 0.6660191416740417
},
{
"text": "Tyler of this band lent his \n steamin' vocals to \"Train Kept A-Rollin'\",",
"probability": 0.04697996431029144,
"start_logit": 0.3223397731781006,
"end_logit": 0.5871344208717346
},
{
"text": "Yardbirds popularized the song as an early \n psychedelic blues rock song, due ... Tiny Bradshaw and his band first recorded \" \n The Train Kept A-Rollin'\"",
"probability": 0.04695464520873227,
"start_logit": 0.24291597306728363,
"end_logit": 0.6660191416740417
},
{
"text": "A",
"probability": 0.04576853705450256,
"start_logit": 0.2173307240009308,
"end_logit": 0.6660191416740417
},
{
"text": "Steven Tyler of this band lent his steamin' vocals to \" \n Train Kept A-Rollin'\", first popularized by the Yardbirds",
"probability": 0.045259951562630615,
"start_logit": 0.3716318905353546,
"end_logit": 0.5005436539649963
},
{
"text": "Tyler of this band lent his steamin' vocals to \" \n Train Kept A-Rollin'\",",
"probability": 0.04443903508658541,
"start_logit": 0.24065357446670532,
"end_logit": 0.6132176518440247
},
{
"text": "Steve Tyler and guitarist Joe \n Perry .... \"Jaded\" before segueing into the powerhouse classic, \"Train Kept a",
"probability": 0.04442756473174001,
"start_logit": 0.26101070642471313,
"end_logit": 0.5926023721694946
},
{
"text": "Steven \n Tyler of Aerosmith was invited to sing the national anthem at the ..... Train Kept A-",
"probability": 0.04442743365151729,
"start_logit": 0.28928712010383606,
"end_logit": 0.5643230080604553
}
],
"9aa1a16d4d1c4d8c874dc8cad32d2c49": [
{
"text": "the Beyond. [SEP] [SEP] Authors Me and My Kindle [SEP] But if you read Irving",
"probability": 0.07276516095772034,
"start_logit": 0.343326210975647,
"end_logit": 0.5145772099494934
},
{
"text": "Malcolm Dresden's death is way beyond being suspicious. ..... and \n uncle,",
"probability": 0.07115748324135143,
"start_logit": 0.35107606649398804,
"end_logit": 0.48448556661605835
},
{
"text": "Horton",
"probability": 0.07005485137648604,
"start_logit": 0.24399875104427338,
"end_logit": 0.5759459137916565
},
{
"text": "ovoid",
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"start_logit": 0.22407807409763336,
"end_logit": 0.5947058200836182
},
{
"text": "ovo",
"probability": 0.06743703094069507,
"start_logit": 0.22407807409763336,
"end_logit": 0.5577823519706726
},
{
"text": "ovoid abandonment, beyond ovoid",
"probability": 0.06634282089190714,
"start_logit": 0.22407807409763336,
"end_logit": 0.5414236187934875
},
{
"text": "in hoops, football & lacrosse at Syracuse & if you think he couldn't act, \n ..... ovoid",
"probability": 0.06549834024533188,
"start_logit": 0.1917826235294342,
"end_logit": 0.560908317565918
},
{
"text": "ovoid abandonment, beyond ovo",
"probability": 0.0643422754202539,
"start_logit": 0.22407807409763336,
"end_logit": 0.5108049511909485
},
{
"text": "ovoid betrayal... you won't believe the ending \n when he \"\"Hatches the Egg\",\"Horton\".",
"probability": 0.0628650017214493,
"start_logit": 0.18807297945022583,
"end_logit": 0.5235827565193176
},
{
"text": "Horton Hatches the Egg \n and in ..... Seuss's greatest anti-alphabet",
"probability": 0.06254005657730952,
"start_logit": 0.24399875104427338,
"end_logit": 0.46247464418411255
},
{
"text": "Literature / Parental",
"probability": 0.06000938428462169,
"start_logit": 0.1949281096458435,
"end_logit": 0.47023898363113403
},
{
"text": "Parental",
"probability": 0.05949123203026497,
"start_logit": 0.18625609576702118,
"end_logit": 0.47023898363113403
},
{
"text": "uncle",
"probability": 0.05927068107696894,
"start_logit": 0.16829533874988556,
"end_logit": 0.48448556661605835
},
{
"text": "the Harvester",
"probability": 0.051762598191598265,
"start_logit": 0.13474130630493164,
"end_logit": 0.38259267807006836
},
{
"text": "a few others were too tired to flee and slept beyond the \n allowed ..... conclusion to the Harvester",
"probability": 0.049081252796797134,
"start_logit": 0.08155060559511185,
"end_logit": 0.38259267807006836
},
{
"text": "I ran out the other end of the row he would not follow,. [SEP] [SEP] The Motion Picture Production Code",
"probability": 0.04740824930915882,
"start_logit": 0.07281263172626495,
"end_logit": 0.3566497564315796
}
],
"4113be8423d14a4790a5c5e569d4595a": [
{
"text": "Bartholomew",
"probability": 0.06991069284533777,
"start_logit": 0.3132413923740387,
"end_logit": 0.48888105154037476
},
{
"text": "Hats",
"probability": 0.06782060236917804,
"start_logit": 0.1698736846446991,
"end_logit": 0.6018961668014526
},
{
"text": "Bartholomew Cubbins",
"probability": 0.06742978750443704,
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"text": "outofculture Pull Request #1 petekinnecom ... [SEP] + \"question\": \"'<a",
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"text": "Cleopatra and her father briefly and .... Mark \n Antony proclaimed Cleopatra",
"probability": 0.050870464105121235,
"start_logit": 0.27369633316993713,
"end_logit": 0.4469630718231201
},
{
"text": "Cleopatra was named the \"Queen of Queens\" and she distributed the... [SEP] [SEP] Full stop is a synonym",
"probability": 0.05086929220580813,
"start_logit": 0.2840363383293152,
"end_logit": 0.4366000294685364
},
{
"text": "Mark \n Antony",
"probability": 0.05010383157136342,
"start_logit": 0.18518564105033875,
"end_logit": 0.5202887654304504
},
{
"text": "Mark Antony called \n her The Queen of Queens # Quiz # Question. 0:29. Amazing... [SEP] [SEP] Cleopatra",
"probability": 0.04948009192019019,
"start_logit": 0.2599991261959076,
"end_logit": 0.43294820189476013
},
{
"text": "of 42 BC, led by Mark Antony and Octavian, also called Augustus. ... Mark \n Antony",
"probability": 0.04924051764351639,
"start_logit": 0.16780497133731842,
"end_logit": 0.5202887654304504
},
{
"text": "Mark \n Antony spent the winter of 41-40 BC with Cleopatra, enjoying himself and ... Days \n later, Cleopatra",
"probability": 0.048973885316374415,
"start_logit": 0.18518564105033875,
"end_logit": 0.4974784851074219
},
{
"text": "Antony",
"probability": 0.048641016484114744,
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"end_logit": 0.48216164112091064
},
{
"text": "Mark",
"probability": 0.048297298539485514,
"start_logit": 0.23393547534942627,
"end_logit": 0.4348170757293701
},
{
"text": "Cleopatra and Mark Antony - Ancient Egypt Online [SEP] Mark Anthony",
"probability": 0.04814199730753641,
"start_logit": 0.1800805628299713,
"end_logit": 0.48545128107070923
},
{
"text": "Antony called \n her The Queen of Queens # Quiz # Question. 0:29. Amazing... [SEP] [SEP] Cleopatra and Mark Antony",
"probability": 0.048099020830995806,
"start_logit": 0.19368241727352142,
"end_logit": 0.47095632553100586
},
{
"text": "Cleopatra and Mark Antony",
"probability": 0.047449214256383515,
"start_logit": 0.1800805628299713,
"end_logit": 0.47095632553100586
},
{
"text": "Antony - Ancient Egypt Online [SEP] Mark Anthony",
"probability": 0.04659372758171932,
"start_logit": 0.14739157259464264,
"end_logit": 0.48545128107070923
},
{
"text": "Mark Antony - Ancient Egypt Online [SEP] Mark Anthony",
"probability": 0.04645343013701027,
"start_logit": 0.14437595009803772,
"end_logit": 0.48545128107070923
}
],
"45c25b136e9947309d3f3199a5eac397": [
{
"text": "Appian Way - Wikipedia [SEP] The Appian",
"probability": 0.06498524913410973,
"start_logit": 0.1816360354423523,
"end_logit": 0.7420097589492798
},
{
"text": "Appian Way - Wikipedia [SEP] The App",
"probability": 0.06362508882943335,
"start_logit": 0.1816360354423523,
"end_logit": 0.7208573222160339
},
{
"text": "Rome. He was ..... External links[edit] \n ... [SEP] [SEP] The Appian",
"probability": 0.06229850170107775,
"start_logit": 0.16664592921733856,
"end_logit": 0.7147769331932068
},
{
"text": "Via App",
"probability": 0.061701191296424596,
"start_logit": 0.1889210194349289,
"end_logit": 0.682867705821991
},
{
"text": "Rome's Appian Way: The Perfect Springtime Stroll - Revealed Rome [SEP] Apr 4, 2012 ... The App",
"probability": 0.06045574143866952,
"start_logit": 0.16276127099990845,
"end_logit": 0.6886357665061951
},
{
"text": "Rome's Appian Way: The Perfect Springtime Stroll - Revealed Rome [SEP] Apr 4, 2012 ... The Appian",
"probability": 0.05890740014329066,
"start_logit": 0.16276127099990845,
"end_logit": 0.6626909375190735
},
{
"text": "Rome's App",
"probability": 0.05794122732229763,
"start_logit": 0.16276127099990845,
"end_logit": 0.6461533904075623
},
{
"text": "App",
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"start_logit": 0.1816360354423523,
"end_logit": 0.6253989338874817
},
{
"text": "Rome's Appian",
"probability": 0.05752340904554832,
"start_logit": 0.16276127099990845,
"end_logit": 0.6389161944389343
},
{
"text": "Rome to Brindisi, Puglia, southern \n Italy. .... The Appian",
"probability": 0.05742067274225134,
"start_logit": 0.14919377863407135,
"end_logit": 0.6506960988044739
},
{
"text": "Appian",
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"start_logit": 0.1816360354423523,
"end_logit": 0.602915346622467
},
{
"text": "Appian Way - Wikipedia [SEP] The Appian Way",
"probability": 0.054387658779203624,
"start_logit": 0.1816360354423523,
"end_logit": 0.5639867186546326
},
{
"text": "Via Appia",
"probability": 0.05303741894724785,
"start_logit": 0.1889210194349289,
"end_logit": 0.5315621495246887
},
{
"text": "Aqua App",
"probability": 0.0514328800178427,
"start_logit": 0.17995156347751617,
"end_logit": 0.5098115801811218
},
{
"text": "Rome's Appian Way: The Perfect Springtime Stroll - Revealed Rome [SEP] Apr 4, 2012 ... The Appian Way",
"probability": 0.05080104323422342,
"start_logit": 0.16276127099990845,
"end_logit": 0.5146411061286926
},
{
"text": "Roman Republic, by Appius Claudius Caecus. \n ..... (today's Ponte Cestio)",
"probability": 0.04676002376782812,
"start_logit": 0.15236109495162964,
"end_logit": 0.44215303659439087
},
{
"text": "s Ponte Cesti",
"probability": 0.044794470494048805,
"start_logit": 0.10832606256008148,
"end_logit": 0.4432441294193268
},
{
"text": "s Pont",
"probability": 0.03954895887371016,
"start_logit": 0.10832606256008148,
"end_logit": 0.31869879364967346
}
],
"2a26ba2446794ed38d4633091a001a4d": [
{
"text": "s",
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"start_logit": 0.24171993136405945,
"end_logit": 0.6774384379386902
},
{
"text": "Mars, Jupiter, and Saturn. [SEP] [SEP] Asteroids.htm - Cosmic Elk [SEP] Since it",
"probability": 0.055755409303064205,
"start_logit": 0.20855093002319336,
"end_logit": 0.7056299448013306
},
{
"text": "Mercury",
"probability": 0.05422267877315368,
"start_logit": 0.24312657117843628,
"end_logit": 0.6431791186332703
},
{
"text": "of Mercury's",
"probability": 0.05370241105780673,
"start_logit": 0.19922590255737305,
"end_logit": 0.6774384379386902
},
{
"text": "the",
"probability": 0.052498230502419266,
"start_logit": 0.27828890085220337,
"end_logit": 0.5756970047950745
},
{
"text": "orbit, the 19th-century French mathematician Urbain Le Verrier",
"probability": 0.05111998249093096,
"start_logit": 0.25541990995407104,
"end_logit": 0.5719619989395142
},
{
"text": "orbit has a semi-major axis... [SEP] [SEP] Mercury Facts: Interesting Facts about the Planet Mercury",
"probability": 0.05074141346141706,
"start_logit": 0.24726808071136475,
"end_logit": 0.5726807713508606
},
{
"text": "Mercury's \n orbit, the 19th-century French mathematician Urbain Le Verrier",
"probability": 0.05042441533205764,
"start_logit": 0.24171993136405945,
"end_logit": 0.5719619989395142
},
{
"text": "the 19th-century French mathematician Urbain Le Verrier",
"probability": 0.050167372616810604,
"start_logit": 0.2366093099117279,
"end_logit": 0.5719619989395142
},
{
"text": "planet, which he named \"Vulcan\". ... Other than \n Mercury,",
"probability": 0.04989848217295514,
"start_logit": 0.16001790761947632,
"end_logit": 0.6431791186332703
},
{
"text": "to the Sun and is also the smallest of the eight \n planets ... years or so, Mercury",
"probability": 0.049475705625793114,
"start_logit": 0.2852575480937958,
"end_logit": 0.5094306468963623
},
{
"text": "of Mercury's \n orbit, the 19th-century French mathematician Urbain Le Verrier",
"probability": 0.04832656745012718,
"start_logit": 0.19922590255737305,
"end_logit": 0.5719619989395142
},
{
"text": "Mercury, asteroid 2007 EB26",
"probability": 0.04772624006734547,
"start_logit": 0.24312657117843628,
"end_logit": 0.5155612230300903
},
{
"text": "orbit, the 19th-century French mathematician Urbain Le",
"probability": 0.04771975457930445,
"start_logit": 0.25541990995407104,
"end_logit": 0.5031319856643677
},
{
"text": "orbit, the 19th-century French mathematician Urbain Le Verrier hypothesized that \n they were the result of",
"probability": 0.047401065157191315,
"start_logit": 0.25541990995407104,
"end_logit": 0.49643123149871826
},
{
"text": "orbit",
"probability": 0.04716225366313339,
"start_logit": 0.24726808071136475,
"end_logit": 0.49953222274780273
},
{
"text": "Mercury's \n orbit, the 19th-century French mathematician Urbain Le",
"probability": 0.0470704528288442,
"start_logit": 0.24171993136405945,
"end_logit": 0.5031319856643677
},
{
"text": "Mercury, asteroid 2007 EB26, whose orbit",
"probability": 0.046967334648078445,
"start_logit": 0.24312657117843628,
"end_logit": 0.49953222274780273
},
{
"text": "the 19th-century French mathematician Urbain Le",
"probability": 0.0468305072206828,
"start_logit": 0.2366093099117279,
"end_logit": 0.5031319856643677
},
{
"text": "Mercury's \n orbit, the 19th-century French mathematician Urbain Le Verrier hypothesized that \n they were the result of",
"probability": 0.046756099673785564,
"start_logit": 0.24171993136405945,
"end_logit": 0.49643123149871826
}
],
"4bce3bce06ad4b0d85022ac9e7d25bed": [
{
"text": "Airlines on flights between \n Newark Liberty",
"probability": 0.09717124672685205,
"start_logit": 0.2897067070007324,
"end_logit": 0.5396001935005188
},
{
"text": "Liberty",
"probability": 0.08888066974139895,
"start_logit": 0.20052653551101685,
"end_logit": 0.5396001935005188
},
{
"text": "carrier - Wikipedia [SEP] A low-cost carrier",
"probability": 0.08825830242560341,
"start_logit": 0.11849063634872437,
"end_logit": 0.6146091818809509
},
{
"text": "carrier",
"probability": 0.08788459340441332,
"start_logit": 0.11849063634872437,
"end_logit": 0.6103659272193909
},
{
"text": "airlines",
"probability": 0.08389526508050368,
"start_logit": 0.20379792153835297,
"end_logit": 0.4786033034324646
},
{
"text": "Ted",
"probability": 0.08349611880284105,
"start_logit": 0.13961921632289886,
"end_logit": 0.5380129814147949
},
{
"text": "Newark Liberty",
"probability": 0.08275862976598054,
"start_logit": 0.12916015088558197,
"end_logit": 0.5396001935005188
},
{
"text": "Airlines, Inc., commonly referred to as United",
"probability": 0.08270963198825818,
"start_logit": 0.20777808129787445,
"end_logit": 0.46039003133773804
},
{
"text": "Airlines - Wikipedia [SEP] United Airlines, Inc., commonly referred to as United",
"probability": 0.08195171124044896,
"start_logit": 0.19857220351696014,
"end_logit": 0.46039003133773804
},
{
"text": "airlines head on in more key markets: Song",
"probability": 0.07678801057100369,
"start_logit": 0.14659357070922852,
"end_logit": 0.447286993265152
},
{
"text": "airlines head on in more key markets: Song against",
"probability": 0.07314977313559663,
"start_logit": 0.14659357070922852,
"end_logit": 0.3987475037574768
},
{
"text": "Airlines [SEP] PAL",
"probability": 0.07305604711709969,
"start_logit": 0.10789944231510162,
"end_logit": 0.43615952134132385
}
],
"483db64145af4cf2955ec689c62d2ab5": [
{
"text": "Vain",
"probability": 0.0609186573348107,
"start_logit": 0.3017842769622803,
"end_logit": 0.7326527237892151
},
{
"text": "a",
"probability": 0.05620059115154899,
"start_logit": 0.2960411608219147,
"end_logit": 0.6577836275100708
},
{
"text": "Vain - Wikipedia [SEP] \"Train in Vain",
"probability": 0.05607631825150643,
"start_logit": 0.21895837783813477,
"end_logit": 0.7326527237892151
},
{
"text": "Vain - Rock Music Wiki - Wikia [SEP] \"Train in Vain",
"probability": 0.0537475005908554,
"start_logit": 0.21654149889945984,
"end_logit": 0.6926531791687012
},
{
"text": "the Clash . ... \n :91 The Clash arrived at Vanilla",
"probability": 0.05274587325002547,
"start_logit": 0.1721421331167221,
"end_logit": 0.7182409167289734
},
{
"text": "Vain (Stand By Me) by The Clash Songfacts [SEP] On the original vinyl copy of the album \"Train Is Vain",
"probability": 0.05135081418622595,
"start_logit": 0.20391695201396942,
"end_logit": 0.6596613526344299
},
{
"text": "Vain - Revolvy [SEP] \" Train in Vain",
"probability": 0.05089358536775425,
"start_logit": 0.19144541025161743,
"end_logit": 0.6631889939308167
},
{
"text": "Vain \" is a song by the British punk rock band The Clash",
"probability": 0.05070327484237464,
"start_logit": 0.3476383090019226,
"end_logit": 0.5032497048377991
},
{
"text": "a ... Train in Vain",
"probability": 0.05033514021475688,
"start_logit": 0.2960411608219147,
"end_logit": 0.5475597977638245
},
{
"text": "a top ten album in the UK, and its lead \n single \"London .... \"Train in Vain\",",
"probability": 0.04986914172745973,
"start_logit": 0.16384249925613403,
"end_logit": 0.6704574227333069
},
{
"text": "Calling was a top ten album in the UK, and its lead \n single \"London .... \"Train in Vain\",",
"probability": 0.04885774343384801,
"start_logit": 0.14335297048091888,
"end_logit": 0.6704574227333069
},
{
"text": "in the UK, and its lead \n single \"London .... \"Train in Vain\",",
"probability": 0.048570202110219195,
"start_logit": 0.13745030760765076,
"end_logit": 0.6704574227333069
},
{
"text": "Vain\", was originally excluded from the back cover's \n track listing. ... \"London Calling\"",
"probability": 0.04812438108638222,
"start_logit": 0.2700446844100952,
"end_logit": 0.5286417603492737
},
{
"text": "Vain (Stand by Me)\", as the words \"stand by me\" dominate the chorus. [SEP] [SEP] London Calling",
"probability": 0.04797122705352834,
"start_logit": 0.261879026889801,
"end_logit": 0.5336198806762695
},
{
"text": "Vain ... a hidden \n track because it was not listed on the original album sleeve. [SEP] [SEP] Train in Vain",
"probability": 0.047528664478523795,
"start_logit": 0.17823177576065063,
"end_logit": 0.6079987287521362
},
{
"text": "in Vain",
"probability": 0.046319140587011906,
"start_logit": 0.16027770936489105,
"end_logit": 0.6001750826835632
},
{
"text": "Vain - Wikipedia [SEP] \"Train in",
"probability": 0.046041139983209245,
"start_logit": 0.21895837783813477,
"end_logit": 0.5354744791984558
},
{
"text": "Vain - Rock Music Wiki - Wikia [SEP] \"Train in",
"probability": 0.045270830576477517,
"start_logit": 0.21654149889945984,
"end_logit": 0.521018922328949
},
{
"text": "The Clash",
"probability": 0.04510448807396384,
"start_logit": 0.230629563331604,
"end_logit": 0.5032497048377991
},
{
"text": "Vain - Revolvy [SEP] \" Train in Vain \" is a song by the British punk rock band The Clash",
"probability": 0.04337128569951761,
"start_logit": 0.19144541025161743,
"end_logit": 0.5032497048377991
}
]
}
mrqa_output/predictions.json 0 → 100644
浏览文件 @ e27ffafa
{
"3f02f171c82e49828580007a71eefc31": "Ethan",
"98d0b8ce19d1434abdb42aa01e83db61": "MonetDB.R [SEP] I am trying to import a dataset of 217000 records (Jeopardy",
"f0bc45a4dd7a4d8abf91a5e4fb25fe57": "James' epitaph contains this line: Murdered by a",
"2dc90736586049d298a10ed93567f0db": "Steve Tyler and Joe Perry at 2010 \n Aerosmith concert. [SEP] [SEP] The Yardbirds",
"9aa1a16d4d1c4d8c874dc8cad32d2c49": "the Beyond. [SEP] [SEP] Authors Me and My Kindle [SEP] But if you read Irving",
"4113be8423d14a4790a5c5e569d4595a": "Bartholomew",
"4b7a7b560b094e309c10e8dd11e9c6fc": "Am & Qantas in the late '70s, it was \n basically a roped-off part of the economy cabin with free drinks. business class.",
"1426088cb6494263906e1111064d5d72": "a nova srie nasceu na cabea de Barbra Streisan",
"3e24b66b256a4e28ae00280d8960803a": "Jim",
"fda746982dd848b89c40b3fc8a8e56d2": "Jackie Glea",
"aa0179e972f94530a8125180d25aa3b4": "Cows Eat Grass",
"b5da96ef279448aca61b049042467a4a": "periods",
"cc15c92b096a4be898611ed8d3869b4e": "Galileo \n was under house arrest for espousing this man's",
"43ee9fcf44a148348923d4d3f22d7d98": "Bulova",
"8824fe46b8b442699c7b8ef0b25ef995": "Sheen",
"e04ef70e4d034c2e9d56dc96d662d9cb": "her tragic death. ... prime \n minister Tony Blair dubbed Diana",
"b354d2f970eb457bb5a305d94ba1a777": "John Spencer Of 'West Wing",
"40b52c7c4f6b46ecb65a0541a69e92a6": "Langham of Veronica's... [SEP] [SEP] Daydream Believers: The Monkees",
"10abdca0abb04882a56e94745a587f28": "John Adams",
"4a360242cbd7421aab8ab86a30b9766b": "of revolutionary scientific works. The Origin of",
"670dc24a0fb04455bbb889733bf002a1": "Hume",
"9f294e3878034f48bd421ed0eb96f2a3": "Mount Rainier",
"beb0368980824527ac58e7f983bca97f": "it is to merge with KLM, the Dutch airline. ... The \n takeover will create Europe's largest airline,",
"90c42d76f3dd44cf86ac3d5b945acee1": "Cleopatra",
"45c25b136e9947309d3f3199a5eac397": "Appian Way - Wikipedia [SEP] The Appian",
"2a26ba2446794ed38d4633091a001a4d": "s",
"4bce3bce06ad4b0d85022ac9e7d25bed": "Airlines on flights between \n Newark Liberty",
"483db64145af4cf2955ec689c62d2ab5": "Vain"
}
paddlepalm/mtl_controller.py
浏览文件 @ e27ffafa
......@@ -422,7 +422,7 @@ class Controller(object):
prefixes.append(inst.name)
mrs.append(inst.mix_ratio)
joint_iterator_fn = create_joint_iterator_fn(iterators, prefixes, joint_shape_and_dtypes, mrs, name_to_position, dev_count=dev_count, verbose=VERBOSE)
joint_iterator_fn = create_joint_iterator_fn(iterators, prefixes, joint_shape_and_dtypes, mrs, name_to_position, dev_count=dev_count, verbose=VERBOSE, batch_size=main_conf['batch_size'])
input_attrs = [[i, j, k] for i, (j,k) in zip(joint_input_names, joint_shape_and_dtypes)]
pred_input_attrs = [[i, j, k] for i, (j,k) in zip(pred_joint_input_names, pred_joint_shape_and_dtypes)]
......@@ -488,10 +488,9 @@ class Controller(object):
bb_fetches = {k: v.name for k,v in bb_output_vars.items()}
task_fetches = {k: v.name for k,v in task_output_vars.items()}
old = len(bb_fetches)+len(task_fetches) # for debug
fetches = bb_fetches.copy()
fetches.update(task_fetches)
assert len(fetches) == old # for debug
# fetches = bb_fetches.copy() # 注意！框架在多卡时无法fetch变长维度的tensor，这里加入bb的out后会挂
# fetches.update(task_fetches)
fetches = task_fetches
fetches['__task_id'] = net_inputs['__task_id'].name
# compute loss
......@@ -505,6 +504,7 @@ class Controller(object):
num_examples = main_reader.num_examples
for inst in instances:
max_train_steps = int(main_conf['num_epochs']* inst.mix_ratio * num_examples) // main_conf['batch_size'] // dev_count
if inst.is_target:
print('{}: expected train steps {}.'.format(inst.name, max_train_steps))
inst.steps_pur_epoch = inst.reader['train'].num_examples // main_conf['batch_size'] // dev_count
inst.expected_train_steps = max_train_steps
......@@ -622,12 +622,11 @@ class Controller(object):
epoch = 0
time_begin = time.time()
backbone_buffer = []
task_buffer = [[]] * num_instances
while not train_finish():
rt_outputs = self.exe.run(train_program, fetch_list=fetch_list)
rt_outputs = {k:v for k,v in zip(fetch_names, rt_outputs)}
rt_task_id = np.squeeze(rt_outputs['__task_id']).tolist()
assert (not isinstance(rt_task_id, list)) or len(set(rt_task_id)) == 1
assert (not isinstance(rt_task_id, list)) or len(set(rt_task_id)) == 1, rt_task_id
rt_task_id = rt_task_id[0] if isinstance(rt_task_id, list) else rt_task_id
cur_task = instances[rt_task_id]
......@@ -635,8 +634,7 @@ class Controller(object):
backbone_buffer.append(backbone.postprocess(backbone_rt_outputs))
task_rt_outputs = {k[len(cur_task.name+'/'):]: v for k,v in rt_outputs.items() if k.startswith(cur_task.name+'/')}
temp = instances[rt_task_id].task_layer['train'].postprocess(task_rt_outputs)
task_buffer[rt_task_id].append(temp)
instances[rt_task_id].task_layer['train'].postprocess(task_rt_outputs)
global_step += 1
# if cur_task.is_target:
......
paddlepalm/reader/mrc4ernie.py
浏览文件 @ e27ffafa
......@@ -30,6 +30,7 @@ class Reader(reader):
max_seq_len=config['max_seq_len'],
do_lower_case=config.get('do_lower_case', False),
tokenizer='FullTokenizer',
for_cn=config.get('for_cn', False),
doc_stride=config['doc_stride'],
max_query_length=config['max_query_len'],
random_seed=config.get('seed', None))
......
paddlepalm/task_paradigm/match.py
浏览文件 @ e27ffafa
......@@ -42,6 +42,7 @@ class TaskParadigm(task_paradigm):
return {"logits": [[-1, 1], 'float32']}
def build(self, inputs):
if self._is_training:
labels = inputs["reader"]["label_ids"]
cls_feats = inputs["backbone"]["sentence_pair_embedding"]
......@@ -58,11 +59,11 @@ class TaskParadigm(task_paradigm):
bias_attr=fluid.ParamAttr(
name="cls_out_b",
initializer=fluid.initializer.Constant(0.)))
if self._is_training:
ce_loss, probs = fluid.layers.softmax_with_cross_entropy(
logits=logits, label=labels, return_softmax=True)
loss = fluid.layers.mean(x=ce_loss)
if self._is_training:
return {'loss': loss}
else:
return {'logits': logits}
......
paddlepalm/task_paradigm/mrc.py
浏览文件 @ e27ffafa
......@@ -65,9 +65,7 @@ class TaskParadigm(task_paradigm):
@property
def outputs_attr(self):
if self._is_training:
return {'start_logits': [[-1, -1, 1], 'float32'],
'end_logits': [[-1, -1, 1], 'float32'],
'loss': [[1], 'float32']}
return {'loss': [[1], 'float32']}
else:
return {'start_logits': [[-1, -1, 1], 'float32'],
'end_logits': [[-1, -1, 1], 'float32'],
......@@ -106,9 +104,7 @@ class TaskParadigm(task_paradigm):
start_loss = _compute_single_loss(start_logits, start_positions)
end_loss = _compute_single_loss(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2.0
return {'start_logits': start_logits,
'end_logits': end_logits,
'loss': total_loss}
return {'loss': total_loss}
else:
return {'start_logits': start_logits,
'end_logits': end_logits,
......
paddlepalm/utils/reader_helper.py
浏览文件 @ e27ffafa
......@@ -48,6 +48,20 @@ def _zero_batch(attrs):
return [np.zeros(shape=shape, dtype=dtype) for shape, dtype in pos_attrs]
def _zero_batch_x(attrs, batch_size):
pos_attrs = []
for shape, dtype in attrs:
# pos_shape = [size if size and size > 0 else 5 for size in shape]
pos_shape = [size for size in shape]
if pos_shape[0] == -1:
pos_shape[0] = batch_size
if pos_shape[1] == -1:
pos_shape[1] = 512 # max seq len
pos_attrs.append([pos_shape, dtype])
return [np.zeros(shape=shape, dtype=dtype) for shape, dtype in pos_attrs]
def create_net_inputs(input_attrs, async=False, iterator_fn=None, dev_count=1, n_prefetch=1):
inputs = []
ret = {}
......@@ -92,10 +106,11 @@ def create_iterator_fn(iterator, iterator_prefix, shape_and_dtypes, outname_to_p
return iterator
def create_joint_iterator_fn(iterators, iterator_prefixes, joint_shape_and_dtypes, mrs, outname_to_pos, dev_count=1, keep_one_task=True, verbose=0):
def create_joint_iterator_fn(iterators, iterator_prefixes, joint_shape_and_dtypes, mrs, outname_to_pos, dev_count=1, keep_one_task=True, verbose=0, batch_size=None):
"""
joint_shape_and_dtypes: 本质上是根据bb和parad的attr设定的，并且由reader中的attr自动填充-1（可变）维度得到，因此通过与iterator的校验可以完成runtime的batch正确性检查
"""
task_ids = range(len(iterators))
weights = [mr / float(sum(mrs)) for mr in mrs]
if not keep_one_task:
......@@ -129,7 +144,6 @@ def create_joint_iterator_fn(iterators, iterator_prefixes, joint_shape_and_dtype
v = verbose
while True:
id = np.random.choice(task_ids, p=weights)
# results = _zero_batch(joint_shape_and_dtypes)
results = fake_batch
if v > 0:
print('----- debug joint iterator -----')
......@@ -138,6 +152,8 @@ def create_joint_iterator_fn(iterators, iterator_prefixes, joint_shape_and_dtype
results[0] = task_id_tensor
for i in range(dev_count):
# results = _zero_batch(joint_shape_and_dtypes, batch_size=batch_size)
# results[0] = task_id_tensor
if id in outbuf:
outputs = outbuf[id]
del outbuf[id]
......
run_demo.sh → run_demo1.sh
浏览文件 @ e27ffafa
export CUDA_VISIBLE_DEVICES=0
export CUDA_VISIBLE_DEVICES=0,1,2,3
export FLAGS_fraction_of_gpu_memory_to_use=0.1
export FLAGS_eager_delete_tensor_gb=0
python demo.py
python demo1.py
run_demo2.sh 0 → 100755
浏览文件 @ e27ffafa
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export FLAGS_fraction_of_gpu_memory_to_use=0.1
export FLAGS_eager_delete_tensor_gb=0
python demo2.py
task_instance/mlm4mrqa.yaml 已删除 100644 → 0
浏览文件 @ f6c68c85
train_file: "data/mlm4mrqa"
mix_ratio: 0.4
batch_size: 4
in_tokens: False
generate_neg_sample: False
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