Create squad_tr.py

squad_tr.py

@@ -0,0 +1,206 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+#
+# This file is based off of the dataset loader script for the original SQuAD2.0
+# dataset.
+#
+#     https://huggingface.co/datasets/squad_v2
+
+# Lint as: python3
+"""SQuAD-TR Dataset"""
+
+
+import itertools
+import json
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+
+logger = datasets.logging.get_logger(__name__)
+
+_HOMEPAGE = "https://github.com/boun-tabi/squad-tr"
+
+_CITATION = """\
+@article{
+  budur2023squadtr,
+  title={Building Efficient and Effective OpenQA Systems for Low-Resource Languages},
+  author={todo},
+  journal={todo},
+  year={2023}
+}
+"""
+
+_DESCRIPTION = """\
+SQuAD-TR is a machine translated version of the original SQuAD2.0 dataset into
+Turkish.
+"""
+
+_VERSION = "1.0.0"
+
+_DATA_URL = _HOMEPAGE + "/raw/beta/data"
+_DATA_URLS = {
+    "default": {
+        "train": f"{_DATA_URL}/squad-tr-train-v{_VERSION}.json.gz",
+        "dev": f"{_DATA_URL}/squad-tr-dev-v{_VERSION}.json.gz",
+    },
+    "excluded": {
+        "train": f"{_DATA_URL}/squad-tr-train-v{_VERSION}-excluded.json.gz",
+        "dev": f"{_DATA_URL}/squad-tr-dev-v{_VERSION}-excluded.json.gz",
+    }
+}
+
+
+class SquadTRConfig(datasets.BuilderConfig):
+    """BuilderConfig for SQuAD-TR."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for SQuAD-TR.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SquadTRConfig, self).__init__(**kwargs)
+
+
+class SquadTR(datasets.GeneratorBasedBuilder):
+    """SQuAD-TR: Machine translated version of the original SQuAD2.0 dataset into Turkish."""
+
+    VERSION = datasets.Version(_VERSION)
+
+    BUILDER_CONFIGS = [
+        SquadTRConfig(
+            name="default",
+            version=datasets.Version(_VERSION),
+            description="SQuAD-TR default version.",
+        ),
+        SquadTRConfig(
+            name="excluded",
+            version=datasets.Version(_VERSION),
+            description="SQuAD-TR excluded version.",
+        ),
+        SquadTRConfig(
+            name="openqa",
+            version=datasets.Version(_VERSION),
+            description="SQuAD-TR OpenQA version.",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "default"
+
+    def _info(self):
+
+        # We change the contents of the "answers" field based on the
+        # configuration selected. Specifically, we are excluding the
+        # "answer_start" field for the "excluded" and "openqa" configurations.
+        if self.config.name in ["excluded", "openqa"]:
+            answers_feature = datasets.features.Sequence({
+                "text": datasets.Value("string"),
+            })
+        else:
+            answers_feature = datasets.features.Sequence({
+                "text": datasets.Value("string"),
+                "answer_start": datasets.Value("int32"),
+            })
+  
+        # Constructing our dataset features.
+        features = datasets.Features({
+            "id": datasets.Value("string"),
+            "title": datasets.Value("string"),
+            "context": datasets.Value("string"),
+            "question": datasets.Value("string"),
+            "answers": answers_feature
+        })
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            task_templates=[
+                QuestionAnsweringExtractive(question_column="question", context_column="context", answers_column="answers")
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+
+        # If the configuration selected is "default" or "excluded", we directly
+        # load the files from the URLs in _DATA_URLS. For the "openqa"
+        # configuration, we combine the datapints from the two different files
+        # used in the "default" and "excluded" configurations.
+        if self.config.name == "openqa":
+            default_files = dl_manager.download_and_extract(_DATA_URLS["default"])
+            excluded_files = dl_manager.download_and_extract(_DATA_URLS["excluded"])
+            train_file_paths = [default_files["train"], excluded_files["train"]]
+            dev_file_paths = [default_files["dev"], excluded_files["dev"]]
+
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath_list": train_file_paths}),
+                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath_list": dev_file_paths}),
+            ]
+        else: 
+            config_urls = _DATA_URLS[self.config.name]
+            downloaded_files = dl_manager.download_and_extract(config_urls)
+
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            ]
+
+
+    def _generate_examples(self, filepath=None, filepath_list=None):
+        """This function returns the examples in the raw (text) form."""
+        assert filepath or filepath_list
+        if filepath:
+            filepath_list = [filepath]
+
+        # Combining the generators for the different filepaths
+        generators = [self._generate_examples_from_filepath(f) for f in filepath_list]
+        for generator in generators:
+            for element in generator:
+                yield element
+
+    def _generate_examples_from_filepath(self, filepath):
+        logger.info("generating examples from = %s", filepath)
+        key = 0
+        with open(filepath, encoding="utf-8") as f:
+            squad = json.load(f)
+            for article in squad["data"]:
+                title = article.get("title", "")
+                for paragraph in article["paragraphs"]:
+                    context = paragraph["context"]  # Do not strip leading blank spaces GH-2585
+                    for qa in paragraph["qas"]:
+                        # Constructing our answers dictonary. Note that the
+                        # answers_dictionary won't include the answer_start
+                        # field in the "excluded" and "openqa" modes.
+                        answers_dictionary = {
+                            "text": [answer["text"] for answer in qa["answers"]],
+                        }
+                        if self.config.name not in ["excluded", "openqa"]:
+                            answers_dictionary["answer_start"] = [answer["answer_start"] for answer in qa["answers"]]
+
+                        # Constructing our datapoint
+                        datapoint = {
+                            "title": title,
+                            "context": context,
+                            "question": qa["question"],
+                            "id": qa["id"],
+                            "answers": answers_dictionary,
+                        }
+
+                        # Features currently used are "context", "question", and "answers".
+                        # Others are extracted here for the ease of future expansions.
+                        yield key, datapoint
+                        key += 1