update dataset

.gitignore

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+notebooks
+
+**/.DS_Store
+**/*.ipynb*

README.md

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+---
+pretty_name: TyDiQA-GoldP-Th
+languages: 
+- th
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: tydiqa.goldp.th.train.json
+  - split: dev
+    path: tydiqa.goldp.th.dev.json
+---
 # TyDiQA-GoldP-Th
 This dataset contains a removed Thai TyDiQA dataset obtained from [Khalidalt's TyDiQA Dataset](https://huggingface.co/datasets/khalidalt/tydiqa-goldp).
 This dataset version does the following additional preprocessing to the dataset

tydiqa-goldp-th.py

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+"""
+Modified from https://huggingface.co/datasets/khalidalt/tydiqa-goldp/blob/main/tydiqa-goldp.py
+"""
+
+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 = ["thai"]
+
+_URL = "https://huggingface.co/datasets/chompk/tydiqa-goldp-th/resolve/main/tydiqa.{split}.jsonl"
+_VERSION = datasets.Version("1.1.0", "")
+
+
+class tydiqa_GoldP_th(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=lang,
+            description=f"tydiqa-GoldP language {lang}",
+            version=_VERSION,
+        )
+        for lang in _LANG
+    ]
+
+
+    def _info(self):
+        # TODO(tydiqa): Specifies the datasets.DatasetInfo object
+        
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=dataset.Features(
+                "paragraphs": datasets.features.Sequence({
+                    "context": datasets.Value("string"),
+                    "qas": datasets.features.Sequence({
+                        "answers": datasets.features.Sequence({
+                            "answer_start": datasets.Value("int32"),
+                            "answer_end": datasets.Value("int32"),
+                            "text": datasets.Value("string"),
+                        }),
+                        "question": datasets.Value("string"),
+                        "id": datasets.Value("string"),
+                    })
+                })
+            ),
+            # 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:
+            for _id,row in enumerate(f):
+                data = json.loads(row)
+                
+                yield _id, data