def lm_tweets_train():
hp = HPTweets()
data = tweets.TweetLoader("atheism", hp.seq_max,
shared_setting.Tweets2Stance)
e_config = ExperimentConfig()
e_config.name = "LM_tweets"
e_config.num_epoch = 30
e_config.save_interval = 30 * 60 # 30 minutes
e = Experiment(hp)
e.train_lm_batch(e_config, data)
def stance_cold_start_simple():
hp = HPColdStart()
e = Experiment(hp)
topic = "atheism"
setting = shared_setting.SimpleTokner(topic)
stance_data = stance_detection.DataLoader(topic, hp.seq_max,
setting.vocab_filename)
voca_size = setting.vocab_size
e.train_stance(voca_size, stance_data)
def feature_svm():
hp = HPFineTunePair()
topic = "atheism"
e = Experiment(hp)
preload_id = ("LM_reserve/DLM_pair_tweets_atheism", 217246)
setting = shared_setting.TopicTweets2Stance(topic)
stance_data = stance_detection.FineLoader(topic, hp.seq_max,
setting.vocab_filename,
hp.sent_max)
e.feature_svm(setting.vocab_size, stance_data, preload_id)
def stance_cold_start():
hp = HPColdStart()
e = Experiment(hp)
topic = "hillary"
setting = shared_setting.TopicTweets2Stance(topic)
stance_data = stance_detection.DataLoader(topic, hp.seq_max,
setting.vocab_filename)
voca_size = setting.vocab_size
e.train_stance(voca_size, stance_data)
def lm_guardian_train():
hp = Hyperparams()
guardian_data = guardian.GuardianLoader("atheism", hp.seq_max,
shared_setting.Guardian2Stance)
e_config = ExperimentConfig()
e_config.name = "LM_guardian"
e_config.num_epoch = 30
e = Experiment(hp)
e.train_lm_batch(e_config, guardian_data)
def train_aux():
hp = HPFineTunePair()
hp2 = HPTiny()
e = Experiment(hp)
topic = "hillary"
preload_id = ("DLM_pair_tweets_hillary", 131200)
setting = shared_setting.TopicTweets2Stance(topic)
sentiment = stance_detection.SentimentLoader(topic, hp.seq_max,
setting.vocab_filename)
voca_size = setting.vocab_size
e.train_aux1(hp2, voca_size, sentiment, preload_id)
def pair_lm():
hp = HPPairTweet()
topic = "atheism"
setting = shared_setting.TopicTweets2Stance(topic)
tweet_group = tweet_reader.load_per_user(topic)
data = loader.PairDataLoader(hp.sent_max, setting, tweet_group)
e_config = ExperimentConfig()
e_config.name = "LM_pair_tweets_{}".format(topic)
e_config.num_epoch = 1
e_config.save_interval = 30 * 60 # 30 minutes
e = Experiment(hp)
e.train_pair_lm(e_config, data)
def test_ubuntu():
hp = hyperparams.HPUbuntu()
hp.batch_size = 16
e = Experiment(hp)
voca_setting = NLI()
voca_setting.vocab_size = 30522
voca_setting.vocab_filename = "bert_voca.txt"
data_loader = ubuntu.DataLoader(hp.seq_max, voca_setting.vocab_filename,
voca_setting.vocab_size, True)
e.test_valid_ubuntu(data_loader)
def gradient_rte_visulize():
hp = hyperparams.HPBert()
e = Experiment(hp)
vocab_filename = "bert_voca.txt"
load_id = loader.find_model_name("RTE_A")
e_config = ExperimentConfig()
e_config.name = "RTE_{}".format("visual")
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert', 'cls_dense']
data_loader = rte.DataLoader(hp.seq_max, vocab_filename, True)
e.rte_visualize(e_config, data_loader, load_id)
def train_aux_stance():
hp = HPColdStart()
hp2 = HPTiny()
topic = "hillary"
preload_id = ("after_aux", 234)
setting = shared_setting.TopicTweets2Stance(topic)
stance_data = stance_detection.DataLoader(topic, hp.seq_max,
setting.vocab_filename)
voca_size = setting.vocab_size
e = Experiment(hp)
e.train_aux_stance(hp2, voca_size, stance_data, preload_id)
def train_nil():
hp = hyperparams.HPBert()
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLI_only_{}".format("B")
e_config.num_epoch = 2
e_config.save_interval = 30 * 60 # 30 minutes
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
e.train_nli(nli_setting, e_config, data_loader)
def stance_with_consistency():
hp = HPStanceConsistency()
topic = "atheism"
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "stance_consistency_{}".format(topic)
setting = shared_setting.TopicTweets2Stance(topic)
stance_data = stance_detection.DataLoader(topic, hp.seq_max,
setting.vocab_filename)
tweet_group = tweet_reader.load_per_user(topic)
aux_data = AuxPairLoader(hp.seq_max, setting, tweet_group)
voca_size = setting.vocab_size
e.train_stance_consistency(voca_size, stance_data, aux_data)
def train_nli_with_reinforce_old():
hp = hyperparams.HPNLI2()
e = Experiment(hp)
nli_setting = NLI()
e_config = ExperimentConfig()
e_config.name = "NLI_run_{}".format("retest")
e_config.num_epoch = 4
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert', 'dense_cls'] #, 'aux_conflict']
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename)
load_id = ("interval", "model-48040")
e.train_nli_ex_0(nli_setting, e_config, data_loader, load_id, True)
def protest_bert():
hp = hyperparams.HPBert()
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "protest"
e_config.num_epoch = 1
e_config.save_interval = 1 * 60 # 1 minutes
e_config.load_names = ['bert']
vocab_size = 30522
vocab_filename = "bert_voca.txt"
data_loader = protest.DataLoader(hp.seq_max, vocab_filename, vocab_size)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.train_protest(e_config, data_loader, load_id)
def crs_stance_baseline():
hp = hyperparams.HPCRS()
hp.batch_size = 16
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "CRS_{}".format("baseline")
e_config.num_epoch = 4
e_config.save_interval = 10 * 60 # 60 minutes
e_config.load_names = ['bert'] #, 'reg_dense']
e_config.voca_size = 30522
data_loader = DataGenerator()
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.train_crs_classify(e_config, data_loader, load_id)
def pred_mnli_anyway():
hp = hyperparams.HPBert()
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLIEx_AnyA"
e_config.load_names = ['bert', 'cls_dense', 'aux_conflict']
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
target_label = 'mismatch'
data_id = "{}_1000".format(target_label)
load_id = ("NLIEx_AnyA", 'model-2785')
e.predict_rf(nli_setting, e_config, data_loader, load_id, data_id)
def pred_snli_ex():
hp = hyperparams.HPBert()
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "SNLIEx_B"
e_config.load_names = ['bert', 'cls_dense', 'aux_conflict']
data_loader = nli.SNLIDataLoader(hp.seq_max, nli_setting.vocab_filename,
True)
load_id = ("SNLIEx_B", 'model-10275')
e.predict_rf(nli_setting, e_config, data_loader, load_id, "test")
def train_nil():
hp = HP()
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLI_only_{}".format("512")
e_config.num_epoch = 2
e_config.save_interval = 30 * 60 # 30 minutes
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.train_nli_ex_0(nli_setting, e_config, data_loader, load_id, False)
def train_adhoc_with_reinforce():
hp = hyperparams.HPAdhoc()
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Adhoc_{}".format("E")
e_config.num_epoch = 4
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert']
vocab_size = 30522
vocab_filename = "bert_voca.txt"
data_loader = ws.DataLoader(hp.seq_max, vocab_filename, vocab_size)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.train_adhoc(e_config, data_loader, load_id)
def train_nil_cold():
print('train_nil_cold')
hp = hyperparams.HPBert()
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLI_Cold"
e_config.num_epoch = 2
e_config.save_interval = 30 * 60 # 30 minutes
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
saved = e.train_nli_ex_0(nli_setting, e_config, data_loader, None, False)
e.test_acc2(nli_setting, e_config, data_loader, saved)
def train_rte():
hp = hyperparams.HPBert()
e = Experiment(hp)
vocab_filename = "bert_voca.txt"
#load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
load_id = ("tlm_simple", "model.ckpt-15000")
e_config = ExperimentConfig()
e_config.name = "RTE_{}".format("tlm_simple_15000")
e_config.num_epoch = 10
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert']
data_loader = rte.DataLoader(hp.seq_max, vocab_filename, True)
e.train_rte(e_config, data_loader, load_id)
def train_nli_with_premade(explain_tag):
hp = hyperparams.HPBert()
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLIEx_{}".format("Premade_"+explain_tag)
e_config.num_epoch = 1
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert'] #, 'cls_dense'] #, 'aux_conflict']
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.train_nli_ex_with_premade_data(nli_setting, e_config, data_loader, load_id, explain_tag)
def wikicont_bert():
hp = hyperparams.HPBert()
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "WikiContrv2009_only_wiki"
e_config.num_epoch = 1
e_config.save_interval = 60 * 60 # 1 minutes
e_config.load_names = ['bert']
e_config.valid_freq = 100
vocab_size = 30522
vocab_filename = "bert_voca.txt"
data_loader = Ams18.DataLoader(hp.seq_max, vocab_filename, vocab_size)
data_loader.source_collection.collection_type = 'wiki'
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.train_wiki_contrv(e_config, data_loader, load_id)
def bert_lm_test():
hp = hyperparams.HPQL()
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Contrv_{}".format("B")
e_config.num_epoch = 4
e_config.save_interval = 30 * 60 # 30 minuteslm_protest
e_config.load_names = ['bert', 'cls']
vocab_size = 30522
vocab_filename = "bert_voca.txt"
data_loader = ws.DataLoader(hp.seq_max, vocab_filename, vocab_size)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.bert_lm_pos_neg(e_config, data_loader, load_id)
def train_adhoc_fad():
hp = hyperparams.HPFAD()
hp.batch_size = 16
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Adhoc_{}".format("FAD")
e_config.num_epoch = 4
e_config.save_interval = 10 * 60 # 60 minutes
e_config.load_names = ['bert'] #, 'reg_dense']
vocab_size = 30522
data_loader = data_sampler.DataLoaderFromFile(hp.batch_size, vocab_size)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
#load_id = ("Adhoc_I2", 'model-290')
e.train_adhoc2(e_config, data_loader, load_id)
def test_ql():
hp = hyperparams.HPAdhoc()
hp.batch_size = 512
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Adhoc_{}".format("C")
e_config.num_epoch = 4
e_config.load_names = ['bert', 'cls']
vocab_size = 30522
vocab_filename = "bert_voca.txt"
data_loader = ws.DataLoader(hp.seq_max, vocab_filename, vocab_size)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.test_ql(e_config, data_loader, load_id)
def run_ql_rank():
hp = hyperparams.HPQL()
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Adhoc_{}".format("D")
e_config.num_epoch = 4
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert', 'cls']
vocab_size = 30522
vocab_filename = "bert_voca.txt"
data_loader = ws.DataLoader(hp.seq_max, vocab_filename, vocab_size)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.rank_ql(e_config, data_loader, load_id)
def train_mscore_regression():
hp = hyperparams.HPMscore()
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Contrv_{}".format("C")
e_config.num_epoch = 4
e_config.save_interval = 10 * 60 # 10 minutes
e_config.load_names = ['bert']
vocab_size = 30522
vocab_filename = "bert_voca.txt"
data_loader = mscore.DataLoader(hp.seq_max, vocab_filename, vocab_size)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.train_controversy_classification(e_config, data_loader, load_id)
def test_nli():
hp = hyperparams.HPBert()
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLI_400k_tlm_simple_wo_hint"
e_config.num_epoch = 2
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert', 'cls_dense'] # , 'aux_conflict']
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
#saved = "/mnt/scratch/youngwookim/Chair/output/model/runs/NLI_Cold/model-0"
saved = "/mnt/scratch/youngwookim/Chair/output/model/runs/NLI_400k_tlm_wo_hint/model-0"
saved = '/mnt/scratch/youngwookim/Chair/output/model/runs/NLI_400k_tlm_simple_hint/model-0'
print(saved)
e.test_acc2(nli_setting, e_config, data_loader, saved)
def run_adhoc_rank_on_robust():
hp = hyperparams.HPAdhoc()
hp.batch_size = 512
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Adhoc_{}_eval".format("F")
e_config.num_epoch = 4
e_config.load_names = ['bert', 'reg_dense']
vocab_size = 30522
vocab_filename = "bert_voca.txt"
data_loader = data_sampler.DataLoaderFromFile(hp.batch_size, vocab_size)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
load_id = ("Adhoc_E", 'model-58338')
e.rank_adhoc(e_config, data_loader, load_id)
