Create rel-text.py

rel-text.py

@@ -0,0 +1,96 @@
+import os
+import datasets
+import pandas as pd
+
+class relTextConfig(datasets.BuilderConfig):
+    def __init__(self, features, data_url, **kwargs):
+        super(relTextConfig, self).__init__(**kwargs)
+        self.features = features
+        self.data_url = data_url
+
+
+class relText(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        relTextConfig(
+            name="pairs",
+            features={
+                "ltable_id": datasets.Value("string"),
+                "rtable_id": datasets.Value("string"),
+                "label": datasets.Value("string"),
+            },
+            data_url="https://huggingface.co/datasets/matchbench/rel-text/resolve/main/",
+        ),
+
+         relTextConfig(
+            name="source",
+            features={
+                "content": datasets.Value("string"),
+            },
+            data_url="https://huggingface.co/datasets/matchbench/rel-text/resolve/main/left.txt",
+        ),
+        
+        relTextConfig(
+            name="target",
+            features={
+                "id": datasets.Value("string"),
+                "title": datasets.Value("string"),
+                "authors": datasets.Value("string"),
+                "venue": datasets.Value("string"),
+                "year": datasets.Value("string"),
+            },
+            data_url="https://huggingface.co/datasets/matchbench/rel-text/resolve/main/right.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):
+        if split=="source":      #read in txt file.
+            with open(path_file, "r") as f:
+                file = f.readlines()
+            for i in range(len(data)):
+                yield i, {
+                    "content": data[i]
+                }
+        else:
+            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"],
+                    }
+                elif split in ['target']:
+                    yield i, {
+                        "id": row["id"],
+                        "title": row["title"],
+                        "authors": row["authors"],
+                        "venue": row["venue"],
+                        "year": row["year"],
+                    }