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jblitzar
/
github-python-corpus

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def load_multigpt2_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.csv'
if args['mode'] in ['train', 'dev']:
data = MultiGPT2Dataset(path, mode=args['mode'], src_len_size=args['src_len_size'], tgt_len_size=args['tgt_len_size'])
args['total_steps'] = len(data) * args['epoch'] / args['batch_size']
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=multigpt2_train_collate_fn)
else:
data = MultiGPT2Dataset(path, mode=args['mode'], src_len_size=args['src_len_size'], tgt_len_size=args['tgt_len_size'])
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=multigpt2_test_collate_fn)
return iter_
def load_ir_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
if args['mode'] in ['train', 'dev']:
data = BERTIRDataset(path, mode=args['mode'], samples=1, max_len=512, negative_aspect='overall')
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=bert_ir_train_collate_fn)
else:
data = BERTIRDataset(path, mode=args['mode'], samples=1, max_len=512, negative_aspect='overall')
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=bert_ir_test_collate_fn)
if not os.path.exists(data.pp_path):
data.save_pickle()
return iter_
def load_bert_ir_dis_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
if args['mode'] in ['train', 'dev']:
data = BERTIRDISDataset(path, mode=args['mode'], samples=1, max_len=512)
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=bert_ir_dis_train_collate_fn)
else:
data = BERTIRDISDataset(path, mode=args['mode'], samples=9, max_len=512)
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=bert_ir_test_collate_fn)
if not os.path.exists(data.pp_path):
data.save_pickle()
return iter_
def load_bert_ir_mc_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
samples = 1 if args['mode'] == 'train' else 9
data = BERTMCDataset(path, mode=args['mode'], samples=samples, max_len=512, harder=False)
if args['mode'] in ['train', 'dev']:
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=bert_ir_mc_collate_fn)
else:
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=bert_ir_mc_test_collate_fn)
if not os.path.exists(data.pp_path):
data.save_pickle()
return iter_
def load_bert_ir_multi_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
data = BERTIRMultiDataset(path, max_len=512, mode=args['mode'])
iter_ = BERTIRMultiDataLoader(data, shuffle=True, batch_size=args['batch_size'])
if not os.path.exists(data.pp_path):
data.save_pickle()
return iter_
def load_bert_ir_cl_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
args['curriculum'] = True
if args['mode'] in ['train', 'dev']:
data = BERTIRCLDataset(path, mode=args['mode'], samples=1)
T = int(len(data) * args['epoch'] / args['batch_size']) + 1
iter_ = BERTIRCLDataLoader(data, T, batch_size=args['batch_size'])
else:
data = BERTIRDataset(path, mode=args['mode'], samples=9, negative_aspect='overall')
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=bert_ir_test_collate_fn)
if not os.path.exists(data.pp_path):
data.save_pickle()
return iter_
def load_rubert_irbi_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
data = RURetrievalDataset(path, mode=args['mode'], max_len=50, max_turn_size=args['max_turn_size'])
if args['mode'] in ['train', 'dev']:
train_sampler = torch.utils.data.distributed.DistributedSampler(data)
iter_ = DataLoader(
data, shuffle=False, batch_size=args['batch_size'], collate_fn=data.collate,
sampler=train_sampler
)
else:
iter_ = DataLoader(
data, shuffle=False, batch_size=args['batch_size'], collate_fn=data.collate,
)
if not os.path.exists(data.pp_path):
data.save_pickle()
args['total_steps'] = len(data) * args['epoch'] / args['batch_size']
args['bimodel'] = 'ru-no-compare'
return iter_
# ================================================================================ #
def load_bert_irbi_dataset(args):
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
# data = BERTIRBIDataset(path, mode=args['mode'], max_len=args['src_len_size'])
data = RetrievalDataset(path, mode=args['mode'], max_len=args['src_len_size'], lang=args['lang'])
if args['mode'] in ['train', 'dev']:
train_sampler = torch.utils.data.distributed.DistributedSampler(data)
iter_ = DataLoader(
data, shuffle=False, batch_size=args['batch_size'], collate_fn=data.collate,
sampler=train_sampler
)