Update tydiqa-primary.py

tydiqa-primary.py

@@ -0,0 +1,118 @@
+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", "russian", "swahili", "telugu", "thai"]
+
+_URL = "https://huggingface.co/datasets/khalidalt/tydiqa-goldp/resolve/main/primary_tasks/{split}/{language}-{split}.jsonl"
+_VERSION = datasets.Version("1.1.0", "")
+
+
+class tydiqa_GoldP(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=lang,
+            description=f"tydiqa-primary language {lang}",
+            version=_VERSION,
+        )
+        for lang in _LANG
+    ]
+
+
+    def _info(self):
+        # TODO(tydiqa): Specifies the datasets.DatasetInfo object
+
+        return datasets.DatasetInfo(
+                # This is the description that will appear on the datasets page.
+                description=_DESCRIPTION,
+                # datasets.features.FeatureConnectors
+                features=datasets.Features(
+                    {
+                        "passage_answer_candidates": datasets.features.Sequence(
+                            {
+                                "plaintext_start_byte": datasets.Value("int32"),
+                                "plaintext_end_byte": datasets.Value("int32"),
+                            }
+                        ),
+                        "question_text": datasets.Value("string"),
+                        "document_title": datasets.Value("string"),
+                        "language": datasets.Value("string"),
+                        "annotations": datasets.features.Sequence(
+                            {
+                                # 'annotation_id': datasets.Value('variant'),
+                                "passage_answer_candidate_index": datasets.Value("int32"),
+                                "minimal_answers_start_byte": datasets.Value("int32"),
+                                "minimal_answers_end_byte": datasets.Value("int32"),
+                                "yes_no_answer": datasets.Value("string"),
+                            }
+                        ),
+                        "document_plaintext": datasets.Value("string"),
+                        # 'example_id': datasets.Value('variant'),
+                        "document_url": datasets.Value("string")
+                        # These are the features of your dataset like images, labels ...
+                    }
+                ),
+                # 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="https://github.com/google-research-datasets/tydiqa",
+                citation=_CITATION,
+            )
+                    
+    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:
+            for _id,row in enumerate(f):
+                data = json.loads(row)
+                
+
+                yield _id, data