Upload caner_replicate.py

caner_replicate.py

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+# -*- coding: utf-8 -*-
+"""caner_replicate.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1QgToSf5_B5l43oRCKwI2vGEB0YcRUtHe
+"""
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+@article{article,
+author = {Salah, Ramzi and Zakaria, Lailatul},
+year = {2018},
+month = {12},
+pages = {},
+title = {BUILDING THE CLASSICAL ARABIC NAMED ENTITY RECOGNITION CORPUS (CANERCORPUS)},
+volume = {96},
+journal = {Journal of Theoretical and Applied Information Technology}
+}
+"""
+
+_DESCRIPTION = """\
+Classical Arabic Named Entity Recognition corpus as a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities.
+"""
+
+_HOMEPAGE = "https://github.com/RamziSalah/Classical-Arabic-Named-Entity-Recognition-Corpus"
+_HOMEPAGE = "https://github.com/omarmohamed2011/caner_data_copy"
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+#_URL = "https://github.com/RamziSalah/Classical-Arabic-Named-Entity-Recognition-Corpus/archive/master.zip"
+_URL  = 'https://github.com/omarmohamed2011/caner_data_copy/blob/main/df_caner.zip'
+
+class Caner(datasets.GeneratorBasedBuilder):
+    """Classical Arabic Named Entity Recognition corpus as a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+
+        features = datasets.Features(
+            {
+                "token": datasets.Value("string"),
+                "ner_tag": datasets.ClassLabel(
+                    names=[
+                        "Allah",
+                        "Book",
+                        "Clan",
+                        "Crime",
+                        "Date",
+                        "Day",
+                        "Hell",
+                        "Loc",
+                        "Meas",
+                        "Mon",
+                        "Month",
+                        "NatOb",
+                        "Number",
+                        #"O",
+                        "Org",
+                        "Para",
+                        "Pers",
+                        "Prophet",
+                        "Rlig",
+                        "Sect",
+                        "Time",
+                    ]
+                ),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        my_urls = _URL
+        data_dir = dl_manager.download_and_extract(my_urls)
+        
+        data_dir = '/content/df_caner.csv'
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+               
+                gen_kwargs={
+                    "filepath": data_dir,
+                    #os.path.join(   data_dir, "/df_caner.csv"),
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+
+        with open(filepath, encoding="utf-8") as csv_file:
+            reader = csv.reader(csv_file, delimiter=",")
+            next(reader, None)
+
+            for id_, row in enumerate(reader):
+
+                yield id_, {
+                    "token": row[0],
+                    "ner_tag": row[1],
+                }