Upload iapp_squad.py with huggingface_hub

iapp_squad.py

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+# coding=utf-8
+import json
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_DATASETNAME = "iapp_squad"
+_CITATION = """\
+@dataset
+{
+  kobkrit_viriyayudhakorn_2021_4539916,
+  author       = {Kobkrit Viriyayudhakorn and Charin Polpanumas},
+  title        = {iapp_wiki_qa_squad},
+  month        = feb,
+  year         = 2021,
+  publisher    = {Zenodo},
+  version      = 1,
+  doi          = {10.5281/zenodo.4539916},
+  url          = {https://doi.org/10.5281/zenodo.4539916}
+}
+"""
+
+_DESCRIPTION = """
+`iapp_wiki_qa_squad` is an extractive question answering dataset from Thai Wikipedia articles.
+It is adapted from [the original iapp-wiki-qa-dataset](https://github.com/iapp-technology/iapp-wiki-qa-dataset)
+to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, resulting in
+5761/742/739 questions from 1529/191/192 articles.
+"""
+
+_HOMEPAGE = "https://github.com/iapp-technology/iapp-wiki-qa-dataset"
+_LICENSE = Licenses.MIT.value
+_HF_URL = " https://huggingface.co/datasets/iapp_wiki_qa_squad"
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+_LOCAL = False
+_LANGUAGES = ["tha"]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+_URLS = {
+    "train": "https://raw.githubusercontent.com/iapp-technology/iapp-wiki-qa-dataset/main/squad_format/data/train.jsonl",
+    "validation": "https://raw.githubusercontent.com/iapp-technology/iapp-wiki-qa-dataset/main/squad_format/data/valid.jsonl",
+    "test": "https://raw.githubusercontent.com/iapp-technology/iapp-wiki-qa-dataset/main/squad_format/data/test.jsonl",
+}
+
+
+class IappWikiQASquadDataset(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(name=f"{_DATASETNAME}_source", version=datasets.Version(_SOURCE_VERSION), description=_DESCRIPTION, subset_id=f"{_DATASETNAME}", schema="source"),
+        SEACrowdConfig(name=f"{_DATASETNAME}_seacrowd_qa", version=datasets.Version(_SEACROWD_VERSION), description=_DESCRIPTION, subset_id=f"{_DATASETNAME}", schema="seacrowd_qa"),
+    ]
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "question_id": datasets.Value("string"),
+                    "article_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"),
+                            "answer_end": datasets.Value("int32"),
+                        }
+                    ),
+                }
+            )
+        elif self.config.schema == "seacrowd_qa":
+            features = schemas.qa_features
+            features["meta"] = {
+                "answer_start": datasets.Value("int32"),
+                "answer_end": datasets.Value("int32"),
+            }
+        return datasets.DatasetInfo(description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, citation=_CITATION, license=_LICENSE)
+
+    def _split_generators(self, dl_manager):
+        file_paths = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": file_paths["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": file_paths["validation"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": file_paths["test"]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+                if self.config.schema == "source":
+                    yield id_, {
+                        "question_id": data["question_id"],
+                        "article_id": data["article_id"],
+                        "title": data["title"],
+                        "context": data["context"],
+                        "question": data["question"],
+                        "answers": {
+                            "text": data["answers"]["text"],
+                            "answer_start": data["answers"]["answer_start"],
+                            "answer_end": data["answers"]["answer_end"],
+                        },
+                    }
+                elif self.config.schema == "seacrowd_qa":
+                    yield id_, {
+                        "id": id_,
+                        "question_id": data["question_id"],
+                        "document_id": data["article_id"],
+                        "question": data["question"],
+                        "type": "abstractive",
+                        "choices": [],
+                        "context": data["context"],
+                        "answer": data["answers"]["text"],
+                        "meta": {"answer_start": data["answers"]["answer_start"][0], "answer_end": data["answers"]["answer_end"][0]},
+                    }