Rename fever_dataset_loader.py to dataset.py

dataset.py

@@ -0,0 +1,47 @@
+import gzip
+import pandas as pd
+from datasets import Dataset, DatasetInfo, GeneratorBasedBuilder, SplitGenerator, Split
+
+class FeverDataset(GeneratorBasedBuilder):
+    VERSION = "1.0.0"
+
+    def _info(self):
+        return DatasetInfo(
+            description="FEVER Dataset with span annotations.",
+            features=datasets.Features({
+                "query": datasets.Value("string"),
+                "document": datasets.Value("string"),
+                "spans": datasets.Value("string"),
+            }),
+            supervised_keys=None,
+            homepage="https://huggingface.co/datasets/jinaai/fever-span-annotated",
+            license="CC BY 4.0"
+        )
+
+    def _split_generators(self, dl_manager):
+        # Define dataset splits
+        return [
+            SplitGenerator(
+                name=Split.TEST,
+                gen_kwargs={
+                    "file_paths": [
+                        "data/fever-pairs_shard_000000.tsv.gz",
+                        "data/fever-pairs_shard_000001.tsv.gz",
+                        "data/fever-pairs_shard_000002.tsv.gz",
+                        "data/fever-pairs_shard_000003.tsv.gz",
+                        "data/fever-pairs_shard_000004.tsv.gz"
+                    ]
+                },
+            ),
+        ]
+
+    def _generate_examples(self, file_path):
+        # Open the compressed file
+        with gzip.open(file_path, 'rt') as f:
+            df = pd.read_csv(f, delimiter='\t')
+            for idx, row in df.iterrows():
+                yield idx, {
+                    "query": row["col1"],
+                    "document": row["col2"],
+                    "spans": row["col3"],
+                }

fever_dataset_loader.py

@@ -1,12 +0,0 @@
-import gzip
-import pandas as pd
-from datasets import Dataset
-
-def load_my_dataset(file_path):
-    # Open and read the compressed .tsv.gz file
-    with gzip.open(file_path, 'rt') as f:
-        # Load it into a pandas DataFrame
-        df = pd.read_csv(f, delimiter='\t')  # Adjust delimiter if needed
-
-    # Convert the DataFrame to a Hugging Face dataset
-    return Dataset.from_pandas(df)