Create dataset_loader.py

dataset_loader.py

@@ -0,0 +1,81 @@
+import json
+import os
+import datasets
+
+# Metadata and descriptions for the dataset
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+    title = {Test Repo Dataset},
+    author={huggingface, Inc.},
+    year={2020}
+}
+"""
+
+_DESCRIPTION = """\
+The Test Repo dataset includes multiple choice questions tailored for NLP research and testing.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/test_repo"
+
+_LICENSE = "Apache License 2.0"
+
+# Define URLs for different parts of the dataset if applicable
+_URLS = {
+    "mcq_domain": "https://huggingface.co/datasets/anand-s/test_repo/train_mcq/",
+}
+
+class TestRepo(datasets.GeneratorBasedBuilder):
+    """Dataset for multiple choice questions from Test Repo."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="mcq_domain", version=VERSION, description="This configuration covers multiple choice questions."),
+    ]
+
+    DEFAULT_CONFIG_NAME = "mcq_domain"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "prompt": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "options": datasets.Sequence(datasets.Value("string")),
+                "answer": datasets.Value("string"),
+                "context": datasets.Value("string"),  # Assuming all data includes context
+                "num_options": datasets.Value("string"),
+                "question_type": datasets.Value("string"),
+            }),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # Download and extract all the files in the directory
+        data_dir = dl_manager.download_and_extract(_URLS[self.config.name])
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"directory": data_dir, "split": "train"},
+            ),
+        ]
+
+    def _generate_examples(self, directory, split):
+        # Iterate over each file in the directory
+        for filename in os.listdir(directory):
+            filepath = os.path.join(directory, filename)
+            if filepath.endswith(".jsonl"):
+                with open(filepath, encoding="utf-8") as f:
+                    for key, row in enumerate(f):
+                        data = json.loads(row)
+                        yield key, {
+                            "prompt": data.get("prompt", ""),
+                            "question": data["question"],
+                            "options": data["options"],
+                            "answer": data.get("answer", ""),
+                            "context": data.get("context", ""),
+                            "num_options": data.get("num_options", ""),
+                            "question_type": data.get("question_type", ""),
+                        }