Create tydiqa-goldp.py

tydiqa-goldp.py

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+import json
+import textwrap
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+# TODO(tydiqa): BibTeX citation
+_CITATION = """\
+@article{tydiqa,
+title   = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
+author  = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
+year    = {2020},
+journal = {Transactions of the Association for Computational Linguistics}
+}
+"""
+
+# TODO(tydiqa):
+_DESCRIPTION = """\
+TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
+The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
+expresses -- such that we expect models performing well on this set to generalize across a large number of the languages
+in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic
+information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but
+don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without
+the use of translation (unlike MLQA and XQuAD).
+"""
+
+
+_LANG = ["arabic", "bengali", "english", "finnish", "indonesian", "japanese", "korean", "russoam", "swahili", "telugu", "thai"]
+#_URL = "https://raw.githubusercontent.com/cambridgeltl/xcopa/master/{subdir}/{language}/{split}.{language}.jsonl"
+_URL = "https://huggingface.co/datasets/khalidalt/tydiqa-goldp/resolve/main/{split}/{language}-{split}.jsonl"
+_VERSION = datasets.Version("1.1.0", "")
+
+
+class tydiqa_GoldP(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=lang,
+            description=f"tydiqa-GoldP language {lang}",
+            version=_VERSION,
+        )
+        for lang in _LANG
+    ]
+    BUILDER_CONFIGS += [
+        datasets.BuilderConfig(
+            name=f"translation-{lang}",
+            description=f"tydiqa-GoldP English translation for language {lang}",
+            version=_VERSION,
+        ) ]
+
+    def _info(self):
+        # TODO(tydiqa): Specifies the datasets.DatasetInfo object
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answers": datasets.features.Sequence(
+                        {
+                            "text": datasets.Value("string"),
+                            "answer_start": datasets.Value("int32"),
+                        }
+                    ),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://github.com/google-research-datasets/tydiqa",
+            citation=_CITATION,
+            task_templates=[
+                QuestionAnsweringExtractive(
+                    question_column="question", context_column="context", answers_column="answers"
+                )
+            ],
+        )
+        
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(tydiqa): Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        language = self.config.name
+        splits = {datasets.Split.TRAIN: "train", datasets.Split.VALIDATION: "dev"}
+        
+        data_urls = {
+            split: _URL.format(language=language, split=splits[split]) for split in splits
+        }
+        
+        dl_paths = dl_manager.download(data_urls)
+        return [
+            datasets.SplitGenerator(
+                name=split,
+                gen_kwargs={"filepath": dl_paths[split]},
+            )
+            for split in splits
+        ]
+        
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # TODO(tydiqa): Yields (key, example) tuples from the dataset
+        
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for article in data["data"]:
+                title = article.get("title", "").strip()
+                for paragraph in article["paragraphs"]:
+                    context = paragraph["context"].strip()
+                    for qa in paragraph["qas"]:
+                        question = qa["question"].strip()
+                        id_ = qa["id"]
+
+                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
+                        answers = [answer["text"].strip() for answer in qa["answers"]]
+
+                        # Features currently used are "context", "question", and "answers".
+                        # Others are extracted here for the ease of future expansions.
+                        yield id_, {
+                            "title": title,
+                            "context": context,
+                            "question": question,
+                            "id": id_,
+                            "answers": {
+                                "answer_start": answer_starts,
+                                "text": answers,
+                            },
+                        }