Create job_titles.py

job_titles.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import os
+import shutil
+import uuid
+from functools import partial
+from pathlib import Path
+from typing import Dict, Iterable
+
+import datasets
+from datasets import DatasetDict, DownloadManager, load_dataset
+import pandas as pd
+
+
+VERSION = datasets.Version("0.0.1")
+
+AVAILABLE_DATASETS = {
+    "job_titles": "https://github.com/jneidel/job-titles"
+}
+
+class JobTitlesDataset(datasets.GeneratorBasedBuilder):
+    """JobTitlesDataset dataset."""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=data_name, version=VERSION, description=f"{data_name} dataset"
+        )
+        for data_name in AVAILABLE_DATASETS
+    ]
+
+    @staticmethod
+    def load(data_name_config: str = "job_titles") -> DatasetDict:
+        ds = load_dataset(__file__, data_name_config)
+        return ds
+
+    def _info(self) -> datasets.DatasetInfo:
+        return datasets.DatasetInfo(
+            description="",
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "name": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/jneidel/job-titles",
+            citation="",
+        )
+
+    def _split_generators(
+        self, dl_manager: DownloadManager
+    ) -> Iterable[datasets.SplitGenerator]:
+        git_repo = AVAILABLE_DATASETS[self.config.name]
+        current_dir = Path(__file__).resolve().parent
+        temp_dir = current_dir / uuid.uuid4().hex
+        temp_dir.mkdir(exist_ok=True)
+        os.system(f"cd {temp_dir} && git clone {git_repo}")
+        shutil.copy(temp_dir / "job-titles" / "job-titles.txt", current_dir)
+        shutil.rmtree(f"{temp_dir}")
+        filepath = str(current_dir / "job-titles.txt")
+
+        # There is no predefined train/val/test split for this dataset.
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": filepath
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: str) -> Iterable[Dict]:
+        with open(filepath, "r") as f_in:
+            for idx, line in enumerate(f_in):
+                line = line.strip()
+                yield idx, {
+                    "id": idx,
+                    "name": line
+                }