first commit

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.txt filter=lfs diff=lfs merge=lfs -text

patent-classification.py

@@ -0,0 +1,109 @@
+import json
+import os
+
+import datasets
+from datasets.tasks import TextClassification
+
+_CITATION = None
+
+
+_DESCRIPTION = """
+ Patent Classification Dataset: a classification of Patents (9 classes).
+ It contains 11 slightly unbalanced classes, 25k Patents and summaries divided into 3 splits: train (25k), val (5k) and test (5k).
+ Copied from "Long Document Classification From Local Word Glimpses via Recurrent Attention Learning" by JUN HE LIQUN WANG LIU LIU, JIAO FENG AND HAO WU
+ See: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8675939
+ See: https://github.com/LiqunW/Long-document-dataset
+"""
+
+_LABELS = [
+    "Human Necessities",
+    "Performing Operations; Transporting",
+    "Chemistry; Metallurgy",
+    "Textiles; Paper",
+    "Fixed Constructions",
+    "Mechanical Engineering; Lightning; Heating; Weapons; Blasting",
+    "Physics",
+    "Electricity",
+    "General tagging of new or cross-sectional technology",
+]
+
+class PatentClassificationConfig(datasets.BuilderConfig):
+    """BuilderConfig for PatentClassification."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for PatentClassification.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(PatentClassificationConfig, self).__init__(**kwargs)
+
+
+class PatentClassificationDataset(datasets.GeneratorBasedBuilder):
+    """PatentClassification Dataset: classification of Patents (9 classes)."""
+    
+    _DOWNLOAD_URL = "https://huggingface.co/datasets/ccdv/patent-classification/resolve/main/"
+    _TRAIN_FILE = "train_data.txt"
+    _VAL_FILE = "val_data.txt"
+    _TEST_FILE = "test_data.txt"
+    _LABELS_DICT = {label: i for i, label in enumerate(_LABELS)}
+
+    BUILDER_CONFIGS = [
+        PatentClassificationConfig(
+            name="patent",
+            version=datasets.Version("1.0.0"),
+            description="Patent Classification Dataset: A classification task of Patents (9 classes)",
+        ),
+
+        PatentClassificationConfig(
+            name="abstract",
+            version=datasets.Version("1.0.0"),
+            description="Patent Classification Dataset: A classification task of Patents with abstracts (9 classes)",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "patent"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=_LABELS),
+                }
+            ),
+            supervised_keys=None,
+            citation=_CITATION,
+            task_templates=[TextClassification(
+                text_column="text", label_column="label")],
+        )
+
+    def _split_generators(self, dl_manager):
+        train_path = dl_manager.download_and_extract(self._DOWNLOAD_URL + self._TRAIN_FILE)
+        val_path = dl_manager.download_and_extract(self._DOWNLOAD_URL + self._VAL_FILE)
+        test_path = dl_manager.download_and_extract(self._DOWNLOAD_URL + self._TEST_FILE)
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}
+            ),
+        ]
+    
+    def _generate_examples(self, filepath):
+        """Generate PatentClassification examples."""
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+                label = self._LABELS_DICT[data["label"]]
+
+                if self.config.name == "abstract":
+                    text = data["abstract"]
+                else:
+                    text = data["description"]
+                yield id_, {"text": text, "label": label}

test_data.txt

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+oid sha256:9d5e0c38b4fba2b354665550b43ee8495bd6a8da6185cd1ef18dbf5b62bfb065
+size 97819427

train_data.txt

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+oid sha256:2ee4526f8f5fe2196df595f3302415df52a594da33f9147c1ea156b92992e13d
+size 486602299

val_data.txt

@@ -0,0 +1,3 @@
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+oid sha256:8c4a3e43c549469ad7191d74a81a1eebe337edb71376265eafe3d0a72c5eff19
+size 99305673