add loading script

azaheadhealth.py

@@ -0,0 +1,87 @@
+# coding=utf-8
+
+import json
+import os
+
+import datasets
+from datasets.download.download_manager import DownloadManager
+from datasets.tasks import TextClassification
+
+logger = datasets.logging.get_logger(__name__)
+
+_DESCRIPTION = "`azaheadhealth`"
+
+_VARIANTS = {
+    "micro": {
+        "version": "1.0.0",
+        "splits": {
+            "train": "data/micro_train.json",
+            "test": "data/micro_test.json"
+        }
+    },
+    "small": {
+        "version": "1.0.0",
+        "splits": {
+            "train": "data/small_train.json",
+            "test": "data/small_test.json"
+        }
+    },
+}
+
+class AZAheadHealthConfig(datasets.BuilderConfig):
+    """BuildConfig for AZAheadHealth"""
+
+    def __init__(self, **kwargs):
+        super(AZAheadHealthConfig, self).__init__(**kwargs)
+
+class AZAheadHealth(datasets.GeneratorBasedBuilder):
+    """AZAheadHealth: A custom dataset in the health domain for the AZAhead project."""
+
+    use_auth_token = True
+
+    BUILDER_CONFIGS = [
+        AZAheadHealthConfig(name=name, version=config["version"])
+        for name, config in _VARIANTS.items()
+    ]
+    
+    DEFAULT_CONFIG_NAME = "small"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.ClassLabel(num_classes=2, names=["NEGATIVE", "POSITIVE"]),
+                }
+            ),
+            supervised_keys=None,
+            task_templates=[
+                TextClassification(
+                    text_column="text", label_column="label"
+                )
+            ]
+        )
+    
+    def _split_generators(self, dl_manager: DownloadManager):
+
+        downloaded_files = dl_manager.download(_VARIANTS[self.config.name]["splits"])
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]})
+        ]
+    
+    def _generate_examples(self, filepath):
+        logger.info("generating examples from = %s", filepath)
+        
+        with open(filepath) as fin:
+            content = json.load(fin)
+
+        key = 0
+        for sample in content:
+            yield key, {
+                "text": sample["text"],
+                "label": sample["label"]
+            }
+            key+=1