Datasets:
jblitzar
/
github-python

Dataset card Data Studio Files
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datatypes = {
'createEvent':createEvent,
'selectEvent':selectEvent,
'addDomain':addDomain,
'addIP':addIP,
'addEmail':addEmail,
'addHash':addHash,
'addBIC':addBIC,
'addFullName':addFullName
}
if request in datatypes:
method = datatypes.get(request)
method(value)
else:
dataError(request)
if __name__ == '__main__':
main()
# <FILESEP>
from typing import List, Tuple
import argparse
from pathlib import Path
import torch
from torch.utils.data import TensorDataset
from tqdm import tqdm, trange
from module import Tokenizer
import logging
logging.basicConfig(level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class CoupletExample(object):
def __init__(self, seq: List[str], tag: List[str]):
assert len(seq) == len(tag)
self.seq = seq
self.tag = tag
class CoupletFeatures(object):
def __init__(self, input_ids: List[int], target_ids: List[int]):
self.input_ids = input_ids
self.target_ids = target_ids
def read_examples(fdir: Path):
seqs = []
tags = []
with open(fdir / "in.txt", 'r', encoding='utf-8') as f:
for line in f.readlines():
seqs.append(line.split())
with open(fdir / "out.txt", 'r', encoding='utf-8') as f:
for line in f.readlines():
tags.append(line.split())
examples = [CoupletExample(seq, tag) for seq, tag in zip(seqs, tags)]
return examples
def convert_examples_to_features(examples: List[CoupletExample], tokenizer: Tokenizer):
features = []
for example in tqdm(examples, desc="creating features"):
seq_ids = tokenizer.convert_tokens_to_ids(example.seq)
tag_ids = tokenizer.convert_tokens_to_ids(example.tag)
features.append(CoupletFeatures(seq_ids, tag_ids))
return features
def convert_features_to_tensors(features: List[CoupletFeatures], tokenizer: Tokenizer, max_seq_len: int):
total = len(features)
input_ids = torch.full((total, max_seq_len),
tokenizer.pad_id, dtype=torch.long)
target_ids = torch.full((total, max_seq_len),
tokenizer.pad_id, dtype=torch.long)
masks = torch.ones(total, max_seq_len, dtype=torch.bool)
lens = torch.zeros(total, dtype=torch.long)
for i, f in enumerate(tqdm(features, desc="creating tensors")):
real_len = min(len(f.input_ids), max_seq_len)
input_ids[i, :real_len] = torch.tensor(f.input_ids[:real_len])
target_ids[i, :real_len] = torch.tensor(f.target_ids[:real_len])
masks[i, :real_len] = 0
lens[i] = real_len
return input_ids, masks, lens, target_ids
def create_dataset(fdir: Path, tokenizer: Tokenizer, max_seq_len: int):
examples = read_examples(fdir)
features = convert_examples_to_features(examples, tokenizer)
tensors = convert_features_to_tensors(features, tokenizer, max_seq_len)
dataset = TensorDataset(*tensors)
return dataset
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--input", default='couplet', type=str)
parser.add_argument("--output", default='dataset', type=str)
parser.add_argument("--max_seq_len", default=32, type=int)
args = parser.parse_args()