# coding=utf-8
# Copyright (c) 2020 Alibaba PAI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ...distillation.distill_dataset import DistillatoryBaseDataset
from ...fewshot_learning.fewshot_dataset import FewshotBaseDataset
from ...modelzoo import AutoTokenizer
from ...utils import io
from ..dataset import BaseDataset
[docs]class ClassificationDataset(BaseDataset):
"""
Classification Dataset
Args:
pretrained_model_name_or_path: for init tokenizer.
data_file: input data file.
max_seq_length: max sequence length of each input instance.
first_sequence: input text
label_name: label column name
second_sequence: set as None
label_enumerate_values: a list of label values
multi_label: set as True if perform multi-label classification, otherwise False
"""
def __init__(self,
pretrained_model_name_or_path,
data_file,
max_seq_length,
input_schema,
first_sequence,
label_name=None,
second_sequence=None,
label_enumerate_values=None,
multi_label=False,
*args,
**kwargs):
super().__init__(data_file,
input_schema=input_schema,
output_format="dict",
*args,
**kwargs)
# assert ".easynlp/modelzoo/" in pretrained_model_name_or_path
self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)
self.max_seq_length = max_seq_length
self.multi_label = multi_label
if label_enumerate_values is None:
self._label_enumerate_values = "0,1".split(",")
else:
if io.exists(label_enumerate_values):
with io.open(label_enumerate_values) as f:
self._label_enumerate_values = [line.strip() for line in f]
else:
self._label_enumerate_values = label_enumerate_values.split(",")
self.max_num_labels = len(self._label_enumerate_values)
assert first_sequence in self.column_names, \
"Column name %s needs to be included in columns" % first_sequence
self.first_sequence = first_sequence
if second_sequence:
assert second_sequence in self.column_names, \
"Column name %s needs to be included in columns" % second_sequence
self.second_sequence = second_sequence
else:
self.second_sequence = None
if label_name:
assert label_name in self.column_names, \
"Column name %s needs to be included in columns" % label_name
self.label_name = label_name
else:
self.label_name = None
self.label_map = dict({value: idx for idx, value in enumerate(self.label_enumerate_values)})
@property
def label_enumerate_values(self):
"""
Returns the label enumerate values.
"""
return self._label_enumerate_values
[docs] def convert_single_row_to_example(self, row):
"""Convert sample token to indices.
Args:
row: contains sequence and label.
text_a: the first sequence in row.
text_b: the second sequence in row if self.second_sequence is true.
label: label token if self.label_name is true.
Returns: sing example
encoding: an example contains token indices.
"""
text_a = row[self.first_sequence]
text_b = row[self.second_sequence] if self.second_sequence else None
label = row[self.label_name] if self.label_name else None
encoding = self.tokenizer(text_a,
text_b,
padding='max_length',
truncation=True,
max_length=self.max_seq_length)
if not self.multi_label:
encoding['label_ids'] = self.label_map[label]
else:
label_id = [self.label_map[x] for x in label.split(",") if x]
new_label_id = [0] * self.max_num_labels
for idx in label_id:
new_label_id[idx] = 1
encoding['label_ids'] = new_label_id
return encoding
[docs] def batch_fn(self, features):
"""
Divide examples into batches.
"""
return {k: torch.tensor([dic[k] for dic in features]) for k in features[0]}
[docs]class DistillatoryClassificationDataset(DistillatoryBaseDataset, ClassificationDataset):
pass
[docs]class FewshotSequenceClassificationDataset(FewshotBaseDataset):
pass