text stringlengths 1 93.6k |
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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']
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args['bimodel'] = args['model']
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return iter_
|
def load_bert_irbicomp_dataset(args):
|
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
|
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,
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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()
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args['total_steps'] = len(data) * args['epoch'] / args['batch_size']
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args['bimodel'] = args['model']
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return iter_
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# ================================================================================ #
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def load_bert_ir_dataset(args):
|
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
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# path = f'data/{args["dataset"]}/LCCC-base.json'
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if args['mode'] in ['train', 'dev']:
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data = BERTIRDataset(path, mode=args['mode'], samples=1, max_len=args['src_len_size'], negative_aspect='coherence')
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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)
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else:
|
data = BERTIRDataset(path, mode=args['mode'], samples=9, max_len=args['src_len_size'], negative_aspect='coherence')
|
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=data.collate)
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if not os.path.exists(data.pp_path):
|
data.save_pickle()
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return iter_
|
def load_bert_ir_multiview_dataset(args):
|
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
|
if args['mode'] in ['train', 'dev']:
|
data = BERTIRDataset(path, mode=args['mode'], samples=1, 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=9, negative_aspect='hard')
|
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_pone_dataset(args):
|
path = f'data/{args["dataset"]}/{args["mode"]}_pone.txt'
|
if args['mode'] in ['train', 'dev']:
|
data = PONEDataset(path, mode=args['mode'], lang=args['lang'], samples=10, bert=False)
|
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=pone_train_collate_fn)
|
if not os.path.exists(data.pp_path):
|
data.save_pickle()
|
else:
|
paths = [f'data/annotator/{args["dataset"]}/sample-100.txt',
|
f'data/annotator/{args["dataset"]}/sample-100-tgt.txt',
|
f'data/annotator/{args["dataset"]}/pred.txt']
|
human_annotations = [
|
f'data/annotator/{args["dataset"]}/1/annotate.csv',
|
f'data/annotator/{args["dataset"]}/2/annotate.csv',
|
f'data/annotator/{args["dataset"]}/3/annotate.csv',
|
]
|
data = PONEDataset(
|
paths,
|
mode=args['mode'], lang=args['lang'], bert=False,
|
human_annotations=human_annotations)
|
iter_ = DataLoader(data, shuffle=False, batch_size=args['batch_size'], collate_fn=pone_test_collate_fn)
|
return iter_
|
def load_bert_logic_dataset(args):
|
path = f'data/{args["dataset"]}/{args["mode"]}.txt'
|
data = BERTLOGICDataset(path, mode=args['mode'], samples=9)
|
if args['mode'] in ['train', 'dev']:
|
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=bert_ir_train_collate_fn)
|
else:
|
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_nli_dataset(args):
|
path = f'data/{args["dataset"]}/{args["mode"]}.jsonl'
|
data = BERTNLIDataset(path)
|
# save preprocessed file
|
if not os.path.exists(data.pp_path):
|
data.save_pickle()
|
iter_ = DataLoader(data, shuffle=True, batch_size=args['batch_size'], collate_fn=nli_collate_fn)
|
return iter_
|
# <FILESEP>
|
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