Upload Walmart-Amazon-SM.py

Walmart-Amazon-SM.py

@@ -0,0 +1,101 @@
+import datasets
+import pandas as pd
+
+class WalmartAmazonSMConfig(datasets.BuilderConfig):
+
+    def __init__(self, features, data_url, **kwargs):
+        super(WalmartAmazonSMConfig, self).__init__(**kwargs)
+        self.features = features
+        self.data_url = data_url
+
+class WalmartAmazonSM(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        WalmartAmazonSMConfig(
+            name="pairs",
+            features={
+                "column1": datasets.Value("string"),
+                "column2": datasets.Value("string"),
+                "label"  : datasets.Value("bool") 
+            },
+            data_url="https://huggingface.co/datasets/matchbench/Walmart-Amazon-SM/resolve/main/matches.txt"
+        ),
+        WalmartAmazonSMConfig(
+            name="source", 
+            features={
+                "json": datasets.Value("string")
+            }, 
+            data_url="https://huggingface.co/datasets/matchbench/Walmart-Amazon-SM/resolve/main/walmart_amazon-sm-1.csv"
+        ),
+        WalmartAmazonSMConfig(
+            name="target", 
+            features={
+                "json": datasets.Value("string")
+            }, 
+            data_url="https://huggingface.co/datasets/matchbench/Walmart-Amazon-SM/resolve/main/walmart_amazon-sm-2.csv"
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(self.config.features)
+        )
+
+    def _split_generators(self, dl_manager):
+        path = dl_manager.download(self.config.data_url)
+        if self.config.name == "pairs":
+            return [
+                datasets.SplitGenerator(
+                    name=split,
+                    gen_kwargs={
+                        "file_path": path
+                    }
+                )
+                for split in [
+                    datasets.Split.TEST
+                ]
+            ]
+        elif self.config.name == "source":
+            return [
+                datasets.SplitGenerator(
+                    name="source",
+                    gen_kwargs={
+                        "file_path": path
+                    }
+                )
+            ]
+        elif self.config.name == "target":
+            return [
+                datasets.SplitGenerator(
+                    name="target",
+                    gen_kwargs={
+                        "file_path": path
+                    }
+                )
+            ]
+
+    def _generate_examples(self, file_path):
+        if self.config.name == "pairs":
+            with open(file_path, "r") as f:
+                md = {}
+                for _, line in enumerate(f):
+                    t = line.strip().split(',')
+                    if t[0] not in md:
+                        md[t[0]] = {t[1]}
+                    else:
+                        md[t[0]].add(t[1])
+            id = -1
+            for k, v in md.items():
+                for i in v:
+                    id = id + 1
+                    yield id, {"column1": k, "column2": i, "label": True}
+        elif self.config.name == "source":
+            with open(file_path, "r") as f:
+                source = pd.read_csv(file_path)
+                source = source.to_json()
+            yield 0, {"json": source}
+        elif self.config.name == "target":
+            with open(file_path, "r") as f:
+                target = pd.read_csv(file_path)
+                target = target.to_json()
+            yield 0, {"json": target}