Create Walmart-Amazon.py

Walmart-Amazon.py

@@ -0,0 +1,89 @@
+import os
+import datasets
+import pandas as pd
+
+class WalmartAmazonConfig(datasets.BuilderConfig):
+    def __init__(self, features, data_url, **kwargs):
+        super(WalmartAmazonConfig, self).__init__(**kwargs)
+        self.features = features
+        self.data_url = data_url
+
+class WalmartAmazon(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        WalmartAmazonConfig(
+            name="pairs",
+            features={
+                "ltable_id":datasets.Value("string"),
+                "rtable_id":datasets.Value("string"),
+                "label":datasets.Value("string"),
+            },
+            data_url="https://huggingface.co/datasets/matchbench/Walmart-Amazon/resolve/main/",
+        ),
+        WalmartAmazonConfig(
+            name="source",
+            features={
+                "id":datasets.Value("string"),
+                "title":datasets.Value("string"),
+                "category":datasets.Value("string"),
+                "brand":datasets.Value("string"),
+				"modelno":datasets.Value("string"),
+				"price":datasets.Value("string"),
+            },
+            data_url="https://huggingface.co/datasets/matchbench/Walmart-Amazon/resolve/main/tableA.csv",
+        ),
+        WalmartAmazonConfig(
+            name="target",
+            features={
+				"id":datasets.Value("string"),
+                "title":datasets.Value("string"),
+                "category":datasets.Value("string"),
+                "brand":datasets.Value("string"),
+				"modelno":datasets.Value("string"),
+				"price":datasets.Value("string"),
+            },
+            data_url="https://huggingface.co/datasets/matchbench/Walmart-Amazon/resolve/main/tableB.csv",
+        ),
+    ]
+    
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(self.config.features)
+        )
+    
+    def _split_generators(self, dl_manager):
+        if self.config.name == "pairs":
+            return [
+                datasets.SplitGenerator(
+                    name=split,
+                    gen_kwargs={
+                        "path_file": dl_manager.download_and_extract(os.path.join(self.config.data_url, f"{split}.csv")),
+                        "split":split,
+                    }
+                )
+                for split in ["train", "valid", "test"]
+            ]
+        if self.config.name == "source":
+            return [ datasets.SplitGenerator(name="source",gen_kwargs={"path_file":dl_manager.download_and_extract(self.config.data_url), "split":"source",})]
+        if self.config.name == "target":
+            return [ datasets.SplitGenerator(name="target",gen_kwargs={"path_file":dl_manager.download_and_extract(self.config.data_url), "split":"target",})]
+    
+    
+    
+    def _generate_examples(self, path_file, split):
+        file = pd.read_csv(path_file)
+        for i, row in file.iterrows():
+            if split not in ['source', 'target']:
+                yield i, {
+                    "ltable_id": row["ltable_id"],
+                    "rtable_id": row["rtable_id"],
+                    "label": row["label"],
+                }
+            else:
+                yield i, {
+                    "id": row["id"],
+                    "title": row["title"],
+                    "category": row["category"],
+                    "brand": row["brand"],
+					"modelno: row["modelno"],
+					"price": row["price"],
+                }