File size: 11,834 Bytes
06ba0d0
 
 
 
 
 
 
 
 
 
 
 
 
 
af9b304
06ba0d0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4e662f6
 
 
06ba0d0
 
4e662f6
 
 
 
06ba0d0
 
4e662f6
 
 
 
06ba0d0
 
 
 
4e662f6
 
 
06ba0d0
 
4e662f6
 
 
 
06ba0d0
 
4e662f6
 
 
 
06ba0d0
 
 
 
 
 
 
 
 
1a985a4
06ba0d0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c3576ab
06ba0d0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c3576ab
06ba0d0
 
 
 
 
 
 
 
c3576ab
06ba0d0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f6268c1
af9b304
06ba0d0
af9b304
 
 
 
06ba0d0
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""Data Loader for SIMPITIKI Dataset with challenge splits"""


import csv
import json
import os
import datasets
from lxml import etree

_CITATION = """\
@article{tonelli2016simpitiki,
  title={SIMPITIKI: a Simplification corpus for Italian},
  author={Tonelli, Sara and Aprosio, Alessio Palmero and Saltori, Francesca},
  journal={Proceedings of CLiC-it},
  year={2016}
}
"""

_DESCRIPTION = """\
SIMPITIKI is a Simplification corpus for Italian and it consists of two sets of simplified pairs: the first one is harvested from the Italian Wikipedia in a semi-automatic way; the second one is manually annotated sentence-by-sentence from documents in the administrative domain.
"""

_HOMEPAGE = "https://github.com/dhfbk/simpitiki"

_LICENSE = "CC-BY 4.0"

_URLs = {
        "v1":{
            "random": {
                "train":"./v1/random_split/train.jsonl",
                "val":"./v1/random_split/val.jsonl",
                "test":"./v1/random_split/test.jsonl"
                },
            "transformations": {
                "train": "./v1/transformations_split/train.jsonl",
                "val": "./v1/transformations_split/val.jsonl",
                "seen_transformations_test": "./v1/transformations_split/seen_transformations_test.jsonl",
                "unseen_transformations_test":"./v1/transformations_split/unseen_transformations_test.jsonl"
                },
            "source_dataset": {
                "itwiki_train":"./v1/source_dataset_split/itwiki_train.jsonl",
                "itwiki_val": "./v1/source_dataset_split/itwiki_val.jsonl", 
                "itwiki_test":"./v1/source_dataset_split/itwiki_test.jsonl",
                "tn_test":"./v1/source_dataset_split/tn_test.jsonl"
                }
            },
        "v2":{
            "random": {
                "train":"./v2/random_split/train.jsonl",
                "val":"./v2/random_split/val.jsonl",
                "test":"./v2/random_split/test.jsonl"
                },
            "transformations": {
                "train": "./v2/transformations_split/train.jsonl",
                "val": "./v2/transformations_split/val.jsonl",
                "seen_transformations_test": "./v2/transformations_split/seen_transformations_test.jsonl",
                "unseen_transformations_test":"./v2/transformations_split/unseen_transformations_test.jsonl"
                },
            "source_dataset": {
                "itwiki_train":"./v2/source_dataset_split/itwiki_train.jsonl",
                "itwiki_val": "./v2/source_dataset_split/itwiki_val.jsonl",
                "itwiki_test":"./v2/source_dataset_split/itwiki_test.jsonl",
                "tn_test":"./v2/source_dataset_split/tn_test.jsonl"
                }


            }

    }


class SIMPITIKI(datasets.GeneratorBasedBuilder):
    """SIMPITIKI is a dataset built for Sentence Simplification Task. It provides complex-to-simple sentence pairs."""

    VERSION_1 = datasets.Version("1.0.0")
    VERSION_2 = datasets.Version("2.0.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="v1", version=VERSION_1, description="First version"),
        datasets.BuilderConfig(name="v2", version=VERSION_2, description="Second version with better sentence boundaries."),
    ]

    DEFAULT_CONFIG_NAME = "v2"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        #  This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        features = datasets.Features(
            {
                "gem_id": datasets.Value("string"),
                "text": datasets.Value("string"),
                "simplified_text": datasets.Value("string"),
                "transformation_type":datasets.Value("string"),
                "source_dataset":datasets.Value("string")
                # These are the features of your dataset like images, labels ...
            }
        )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
   
        my_urls = _URLs[self.config.name]
        downloaded_files = dl_manager.download_and_extract(my_urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files['random']['train'],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files['random']['val'],
                    "split": "val"
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files['random']['test'],
                    "split": "test",
                },
            ),

            datasets.SplitGenerator(
                name='challenge_seen_transformations_train',
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files['transformations']['train'],
                    "split": "challenge_seen_transformations_train",
                },
            ),

            
            datasets.SplitGenerator(
                name='challenge_seen_transformations_val',
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files['transformations']['val'],
                    "split": "challenge_seen_transformations_val",
                },
            ),

            datasets.SplitGenerator(
                name='challenge_seen_transformations_test',
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files['transformations']['seen_transformations_test'],
                    "split": "challenge_seen_transformations_test",
                },
            ),

            datasets.SplitGenerator(
                name='challenge_unseen_transformations_test',
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files['transformations']['unseen_transformations_test'],
                    "split": "challenge_unseen_transformations_test",
                },
            ),

            datasets.SplitGenerator(
                name='challenge_itwiki_train',
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files['source_dataset']['itwiki_train'],
                    "split": "challenge_itwiki_train",
                },
            ),

            datasets.SplitGenerator(
                name='challenge_itwiki_val',
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files['source_dataset']['itwiki_val'],
                    "split": "challenge_itwiki_val",
                },
            ),

            datasets.SplitGenerator(
                name='challenge_itwiki_test',
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files['source_dataset']['itwiki_test'],
                    "split": "challenge_itwiki_test",
                },
            ),
            
            datasets.SplitGenerator(
                name='challenge_tn_test',
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_files['source_dataset']['tn_test'],
                    "split": "challenge_tn_test",
                },
            ),
        ]

    def _generate_examples(
        self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    ):
        """ Yields examples as (key, example) tuples. """
        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is here for legacy reason (tfds) and is not important in itself.

        with open(filepath, encoding="utf-8") as f:
            for id_, row in enumerate(f):
                data = json.loads(row) 
                yield id_, {
                        "text": data["text"],
                        "simplified_text": data["simplified_text"],
                        "transformation_type":data["transformation_type"],
                        "source_dataset": data["source_dataset"],
                        "gem_id": f"gem-SIMPITIKI-{split}-{id_}",
                        }