Fix language and task array

README.md

@@ -1,90 +1,12 @@
 ---
-language: en
-size_categories: 10K<n<100K
-task_categories:
-- text-classification
+languages: en
+task_categories: text-classification
+tags:
+- long context
 task_ids:
 - multi-class-classification
 - topic-classification
-tags:
-- long context
-dataset_info:
-- config_name: abstract
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': Human Necessities
-          '1': Performing Operations; Transporting
-          '2': Chemistry; Metallurgy
-          '3': Textiles; Paper
-          '4': Fixed Constructions
-          '5': Mechanical Engineering; Lightning; Heating; Weapons; Blasting
-          '6': Physics
-          '7': Electricity
-          '8': General tagging of new or cross-sectional technology
-  splits:
-  - name: train
-    num_bytes: 17225101
-    num_examples: 25000
-  - name: validation
-    num_bytes: 3472854
-    num_examples: 5000
-  - name: test
-    num_bytes: 3456733
-    num_examples: 5000
-  download_size: 12067953
-  dataset_size: 24154688
-- config_name: patent
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': Human Necessities
-          '1': Performing Operations; Transporting
-          '2': Chemistry; Metallurgy
-          '3': Textiles; Paper
-          '4': Fixed Constructions
-          '5': Mechanical Engineering; Lightning; Heating; Weapons; Blasting
-          '6': Physics
-          '7': Electricity
-          '8': General tagging of new or cross-sectional technology
-  splits:
-  - name: train
-    num_bytes: 466788625
-    num_examples: 25000
-  - name: validation
-    num_bytes: 95315107
-    num_examples: 5000
-  - name: test
-    num_bytes: 93844869
-    num_examples: 5000
-  download_size: 272966251
-  dataset_size: 655948601
-configs:
-- config_name: abstract
-  data_files:
-  - split: train
-    path: abstract/train-*
-  - split: validation
-    path: abstract/validation-*
-  - split: test
-    path: abstract/test-*
-- config_name: patent
-  data_files:
-  - split: train
-    path: patent/train-*
-  - split: validation
-    path: patent/validation-*
-  - split: test
-    path: patent/test-*
-  default: true
+size_categories: 10K<n<100K
 ---
 
 **Patent Classification: a classification of Patents and abstracts (9 classes).** 

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 9 unbalanced classes, 35k Patents and summaries divided into 3 splits: train (25k), val (5k) and test (5k).
+ Data are sampled from "BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization." by Eva Sharma, Chen Li and Lu Wang 
+ See: https://aclanthology.org/P19-1212.pdf 
+ See: https://evasharma.github.io/bigpatent/
+"""
+
+_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._TRAIN_FILE)
+        val_path = dl_manager.download_and_extract(self._VAL_FILE)
+        test_path = dl_manager.download_and_extract(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}

patent/test-00000-of-00001.parquet

@@ -1,3 +0,0 @@
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-oid sha256:d52a3aa7829e35a1da254c5e849ebf4275602a03aeecb37ea174f6e7f988f4fc
-size 39091297

patent/train-00000-of-00001.parquet

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:104e2188980f4a104ab3920b58da17b3d039cde551489a1e29179fac813e04a9
-size 194413762

patent/validation-00000-of-00001.parquet

@@ -1,3 +0,0 @@
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-oid sha256:71e0515ecbb4e990b86d8c4b16f275c32493f885965dc54c527b9926e15c2922
-size 39461192

abstract/test-00000-of-00001.parquet → test_data.txt

@@ -1,3 +1,3 @@
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-size 1720512
+oid sha256:6624a83224d868e575d8c8c210a89135b00ab3ccb563fb0b5ab5f9268e569ca5
+size 97938986

abstract/validation-00000-of-00001.parquet → train_data.txt

@@ -1,3 +1,3 @@
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-oid sha256:61a4282d20a6623db135489039b09a5fd541d6158d8f2378ac3098c532ae4671
-size 1735855
+oid sha256:32a8220d7b12b1705f44b0004d6a7ba984fc6038b236cfa63f51563c5f57e8b2
+size 487201907

abstract/train-00000-of-00001.parquet → val_data.txt

@@ -1,3 +1,3 @@
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-size 8611586
+oid sha256:687787fffcaece738e823508382617abfb0fbbf82917e593600894917e78d1b2
+size 99425708