Upload clefip2011.py

clefip2011.py

@@ -0,0 +1,137 @@
+import os
+import pandas as pd
+import datasets
+
+
+class CLEFIP2011Config(datasets.BuilderConfig):
+    """Custom Config for CLEFIP2011"""
+
+    def __init__(self, dataset_type=None, **kwargs):
+        super(CLEFIP2011Config, self).__init__(**kwargs)
+        self.dataset_type = dataset_type
+
+
+class CLEFIP2011(datasets.GeneratorBasedBuilder):
+    """Custom Dataset Loader"""
+
+    BUILDER_CONFIGS = [
+        CLEFIP2011Config(
+            name="bibliographic",
+            version=datasets.Version("1.0.0"),
+            description="CLEF-IP 2011 Bibliographic Data",
+            dataset_type="bibliographic",
+        ),
+    ]
+
+    def _info(self):
+
+        if self.config.dataset_type == "bibliographic":
+            features = datasets.Features(
+                {
+                    "ucid": datasets.Value("string"),
+                    "country": datasets.Value("string"),
+                    "doc_number": datasets.Value("string"),
+                    "kind": datasets.Value("string"),
+                    "lang": datasets.Value("string"),
+                    "corrected_lang": datasets.Value("string"),
+                    "date": datasets.Value("string"),
+                    "family_id": datasets.Value("string"),
+                    "date_produced": datasets.Value("string"),
+                    "status": datasets.Value("string"),
+                    "ecla_list": datasets.Value("string"),
+                    "applicant_name_list": datasets.Value("string"),
+                    "inventor_name_list": datasets.Value("string"),
+                    "title_de_text": datasets.Value("string"),
+                    "title_fr_text": datasets.Value("string"),
+                    "title_en_text": datasets.Value("string"),
+                    "abstract_de_exist": datasets.Value("bool"),
+                    "abstract_fr_exist": datasets.Value("bool"),
+                    "abstract_en_exist": datasets.Value("bool"),
+                    "description_de_exist": datasets.Value("bool"),
+                    "description_fr_exist": datasets.Value("bool"),
+                    "description_en_exist": datasets.Value("bool"),
+                    "claims_de_exist": datasets.Value("bool"),
+                    "claims_fr_exist": datasets.Value("bool"),
+                    "claims_en_exist": datasets.Value("bool"),
+                }
+            )
+        return datasets.DatasetInfo(
+            description="CLEF-IP 2011 Bibliographic dataset.",
+            features=features,
+            supervised_keys=None,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        archive_path = dl_manager.download_and_extract(
+            "https://huggingface.co/datasets/amylonidis/PatClass2011/resolve/main/clefip2011_bibliographic_clean.tar.gz"
+        )
+
+        bibliographic_file = os.path.join(archive_path, "clefip2011_bibliographic.csv")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepaths": [bibliographic_file],
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepaths, split):
+
+        for filepath in filepaths:
+            df = pd.read_csv(filepath, header=None)
+
+            column_names = [
+                "ucid",
+                "country",
+                "doc_number",
+                "kind",
+                "lang",
+                "corrected_lang",
+                "date",
+                "family_id",
+                "date_produced",
+                "status",
+                "ecla_list",
+                "applicant_name_list",
+                "inventor_name_list",
+                "title_de_text",
+                "title_fr_text",
+                "title_en_text",
+                "abstract_de_exist",
+                "abstract_fr_exist",
+                "abstract_en_exist",
+                "description_de_exist",
+                "description_fr_exist",
+                "description_en_exist",
+                "claims_de_exist",
+                "claims_fr_exist",
+                "claims_en_exist",
+            ]
+            df.columns = column_names
+
+            df["date"] = pd.to_datetime(df["date"], format="%Y%m%d").astype(str)
+
+            df["date_produced"] = pd.to_datetime(
+                df["date_produced"], format="%Y%m%d"
+            ).astype(str)
+
+            boolean_columns = [
+                "abstract_de_exist",
+                "abstract_fr_exist",
+                "abstract_en_exist",
+                "description_de_exist",
+                "description_fr_exist",
+                "description_en_exist",
+                "claims_de_exist",
+                "claims_fr_exist",
+                "claims_en_exist",
+            ]
+            for col in boolean_columns:
+                df[col] = df[col].astype(bool)
+
+            for idx, row in df.iterrows():
+                yield idx, row.to_dict()