def train_snli_ex():
hp = hyperparams.HPBert()
hp.compare_deletion_num = 20
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.ex_val = False
e_config.num_epoch = 1
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert', 'cls_dense'] #, 'aux_conflict']
#explain_tag = 'match' # 'dontcare' 'match' 'mismatch'
#explain_tag = 'mismatch'
#explain_tag = 'conflict'
data_loader = nli.SNLIDataLoader(hp.seq_max, nli_setting.vocab_filename,
True)
#load_id = ("NLI_run_nli_warm", "model-97332")
#load_id = ("NLIEx_A", "model-16910")
#load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
#load_id = ("NLIEx_D", "model-1964")
#load_id = ("NLIEx_D", "model-1317")
load_id = ("SNLI_Only_A", 'model-0')
e.train_nli_any_way(nli_setting, e_config, data_loader, load_id)
def get_snli_data(hp, nli_setting):
data_loader = nli.SNLIDataLoader(hp.seq_max, nli_setting.vocab_filename,
True)
tokenizer = get_tokenizer()
CLS_ID = tokenizer.convert_tokens_to_ids(["[CLS]"])[0]
SEP_ID = tokenizer.convert_tokens_to_ids(["[SEP]"])[0]
data_loader.CLS_ID = CLS_ID
data_loader.SEP_ID = SEP_ID
cache_name = "snli_batch{}_seq{}".format(hp.batch_size, hp.seq_max)
data = load_cache(cache_name)
if data is None:
tf_logger.info("Encoding data from csv")
data = get_nli_batches_from_data_loader(data_loader, hp.batch_size)
save_to_pickle(data, cache_name)
return data
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 predict_lime_snli_continue():
hp = hyperparams.HPBert()
hp.batch_size = 512 + 256
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "SNLI_LIME_{}".format("eval")
e_config.load_names = ['bert', 'cls_dense']
data_loader = nli.SNLIDataLoader(hp.seq_max, nli_setting.vocab_filename,
True)
load_id = ("SNLI_Only_A", 'model-0')
e.predict_lime_snli_continue(nli_setting, e_config, data_loader, load_id,
"test")
def test_snli():
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_Test"
e_config.load_names = ['bert', 'cls_dense'] # , 'aux_conflict']
data_loader = nli.SNLIDataLoader(hp.seq_max, nli_setting.vocab_filename,
True)
todo = []
load_id = ("SNLI_Only_A", 'model-0')
todo.append(load_id)
todo.append(("SNLI_Only_1", 'model-0'))
for load_id in todo:
tf.reset_default_graph()
e.test_acc(nli_setting, e_config, data_loader, load_id)
def train_snli_on_bert():
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 = "SNLI_Only_{}".format("1")
e_config.num_epoch = 1
e_config.save_interval = 3 * 60 * 60 # 30 minutes
data_loader = nli.SNLIDataLoader(hp.seq_max, nli_setting.vocab_filename,
True)
#load_id = None
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,
f_train_ex=False)
def predict_lime_snli():
hp = hyperparams.HPBert()
hp.batch_size = 1024 + 512 + 256
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "SNLI_LIME_{}".format("eval")
e_config.load_names = ['bert', 'cls_dense']
start = int(sys.argv[1])
print("Begin", start)
sub_range = (start, start + 100)
data_loader = nli.SNLIDataLoader(hp.seq_max, nli_setting.vocab_filename,
True)
load_id = ("SNLI_Only_A", 'model-0')
e.predict_lime_snli(nli_setting, e_config, data_loader, load_id, "test",
sub_range)
def get_eval_params(load_type, model_path, data_type):
hp = hyperparams.HPSENLI3_eval()
hp.batch_size = 128
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
if data_type == "mnli":
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename,
True)
elif data_type == "snli":
data_loader = nli.SNLIDataLoader(hp.seq_max,
nli_setting.vocab_filename, True)
else:
assert False
dir_path, file_name = os.path.split(model_path)
run_name = os.path.split(dir_path)[1] + "/" + file_name
dev_batches = get_batches_ex(data_loader.get_dev_data(), hp.batch_size, 4)
if load_type == "v2":
load_fn = load_bert_v2
else:
load_fn = load_model
return dev_batches, hp, load_fn, nli_setting, run_name