mirror of https://github.com/doccano/doccano.git
pythondatasetsactive-learningtext-annotationdatasetnatural-language-processingdata-labelingmachine-learningannotation-tool
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
133 lines
3.7 KiB
133 lines
3.7 KiB
import csv
|
|
import json
|
|
from typing import Any, Dict, Iterator, List, Optional
|
|
|
|
import pyexcel
|
|
|
|
|
|
class Record:
|
|
|
|
def __init__(self,
|
|
filename: str,
|
|
data: str = '',
|
|
label: Any = None,
|
|
metadata: Optional[Dict] = None):
|
|
if metadata is None:
|
|
metadata = {}
|
|
self.filename = filename
|
|
self.data = data
|
|
self.label = label
|
|
self.metadata = metadata
|
|
|
|
|
|
class Dataset:
|
|
|
|
def __init__(self,
|
|
filenames: List[str],
|
|
column_data: str = 'text',
|
|
column_label: str = 'label',
|
|
**kwargs):
|
|
self.filenames = filenames
|
|
self.column_data = column_data
|
|
self.column_label = column_label
|
|
self.kwargs = kwargs
|
|
|
|
def __iter__(self) -> Iterator[Record]:
|
|
for filename in self.filenames:
|
|
yield from self.load(filename)
|
|
|
|
def load(self, filename: str) -> Iterator[Record]:
|
|
"""Loads a file content."""
|
|
raise NotImplementedError()
|
|
|
|
def from_row(self, filename: str, row: Dict) -> Record:
|
|
data = row.pop(self.column_data)
|
|
label = row.pop(self.column_label)
|
|
record = Record(filename=filename, data=data, label=label, metadata=row)
|
|
return record
|
|
|
|
|
|
class FileBaseDataset(Dataset):
|
|
|
|
def load(self, filename: str) -> Iterator[Record]:
|
|
record = Record(filename=filename, data=filename)
|
|
yield record
|
|
|
|
|
|
class TextFileDataset(Dataset):
|
|
|
|
def load(self, filename: str) -> Iterator[Record]:
|
|
with open(filename) as f:
|
|
record = Record(filename=filename, data=f.read())
|
|
yield record
|
|
|
|
|
|
class TextLineDataset(Dataset):
|
|
|
|
def load(self, filename: str) -> Iterator[Record]:
|
|
with open(filename) as f:
|
|
for line in f:
|
|
record = Record(filename=filename, data=line.rstrip())
|
|
yield record
|
|
|
|
|
|
class CsvDataset(Dataset):
|
|
|
|
def load(self, filename: str) -> Iterator[Record]:
|
|
with open(filename) as f:
|
|
delimiter = self.kwargs.get('delimiter', ',')
|
|
reader = csv.reader(f, delimiter=delimiter)
|
|
header = next(reader)
|
|
for row in reader:
|
|
row = dict(zip(header, row))
|
|
yield self.from_row(filename, row)
|
|
|
|
|
|
class JSONDataset(Dataset):
|
|
|
|
def load(self, filename: str) -> Iterator[Record]:
|
|
with open(filename) as f:
|
|
dataset = json.load(f)
|
|
for row in dataset:
|
|
yield self.from_row(filename, row)
|
|
|
|
|
|
class JSONLDataset(Dataset):
|
|
|
|
def load(self, filename: str) -> Iterator[Record]:
|
|
with open(filename) as f:
|
|
for line in f:
|
|
row = json.loads(line)
|
|
yield self.from_row(filename, row)
|
|
|
|
|
|
class ExcelDataset(Dataset):
|
|
|
|
def load(self, filename: str) -> Iterator[Record]:
|
|
records = pyexcel.iget_records(filename)
|
|
for row in records:
|
|
yield self.from_row(filename, row)
|
|
|
|
|
|
class FastTextDataset(Dataset):
|
|
|
|
def load(self, filename: str) -> Iterator[Record]:
|
|
with open(filename) as f:
|
|
for i, line in enumerate(f, start=1):
|
|
labels = []
|
|
tokens = []
|
|
for token in line.rstrip().split(' '):
|
|
if token.startswith('__label__'):
|
|
labels.append(token[len('__label__'):])
|
|
else:
|
|
tokens.append(token)
|
|
data = ' '.join(tokens)
|
|
record = Record(filename=filename, data=data, label=labels)
|
|
yield record
|
|
|
|
|
|
class ConllDataset(Dataset):
|
|
|
|
def load(self, filename: str) -> Iterator[Record]:
|
|
with open(filename) as f:
|
|
pass
|