Update nerutc.py

nerutc.py

@@ -1,30 +1,30 @@
 import csv
+from ast import literal_eval
 
 import datasets
 
-
 logger = datasets.logging.get_logger(__name__)
 
-_CITATION = """Citation"""
+_CITATION = """"""
 
-_DESCRIPTION = """Description"""
+_DESCRIPTION = """"""
 
 _DOWNLOAD_URLS = {
-    "train": "Splitdataset\nerutc_train.csv",
-    "test": "Splitdataset\nerutc_test.csv",
+    "train": "https://huggingface.co/datasets/hezarai/parstwiner/resolve/main/parstwiner_train.csv",
+    "test": "https://huggingface.co/datasets/hezarai/parstwiner/resolve/main/parstwiner_test.csv",
 }
 
 
-class DatasetNameConfig(datasets.BuilderConfig):
+class ParsTwiNERConfig(datasets.BuilderConfig):
     def __init__(self, **kwargs):
-        super(DatasetNameConfig, self).__init__(**kwargs)
+        super(ParsTwiNERConfig, self).__init__(**kwargs)
 
 
-class DatasetName(datasets.GeneratorBasedBuilder):
+class ParsTwiNER(datasets.GeneratorBasedBuilder):
     BUILDER_CONFIGS = [
-        DatasetNameConfig(
-            name="nerutc",
-            version=datasets.Version("1.1.1"),
+        ParsTwiNERConfig(
+            name="ParsTwiNER",
+            version=datasets.Version("1.0.0"),
             description=_DESCRIPTION,
         ),
     ]
@@ -35,21 +35,28 @@ class DatasetName(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "tokens": datasets.Sequence(datasets.Value("string")),
-                    # TODO YOU SHOULD PUT THE EXTRACTED UNIQUE TAGS IN YOUR DATASET HERE. THIS LIST IS JUST AN EXAMPLE
-                    """
-                    To extract unique tags from a pandas dataframe use this code and paste the output list below.
-
-                    ```python
-                    unique_tags = df["TAGS_COLUMN_NAME"].explode().unique()
-                    print(unique_tags)
-                    ```
-                    """
-                    "ner_tags": datasets.Sequence(  # USE `pos_tags`, `ner_tags`, `chunk_tags`, etc.
-                        datasets.features.ClassLabel(names=['O' 'B-UNI' 'I-UNI'])  # TODO
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-POG",
+                                "I-POG",
+                                "B-PER",
+                                "I-PER",
+                                "B-ORG",
+                                "I-ORG",
+                                "B-NAT",
+                                "I-NAT",
+                                "B-LOC",
+                                "I-LOC",
+                                "B-EVE",
+                                "I-EVE",
+                            ]
+                        )
                     ),
                 }
             ),
-            homepage="PUT PATH TO THE ORIGINAL DATASET HOME PAGE HERE (OPTIONAL BUT RECOMMENDED)",
+            homepage="https://github.com/overfit-ir/parstwiner",
             citation=_CITATION,
         )
 
@@ -62,28 +69,24 @@ class DatasetName(datasets.GeneratorBasedBuilder):
         test_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["test"])
 
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}
+            ),
         ]
 
-    # TODO
     def _generate_examples(self, filepath):
-        """
-        Per each file_path read the csv file and iterate it.
-        For each row yield a tuple of (id, {"tokens": ..., "tags": ..., ...})
-        Each call to this method yields an output like below:
-        ```
-        (124, {"tokens": ["hello", "world"], "pos_tags": ["NOUN", "NOUN"]})
-        ```
-        """
         logger.info("⏳ Generating examples from = %s", filepath)
         with open(filepath, encoding="utf-8") as csv_file:
             csv_reader = csv.reader(csv_file, quotechar='"', skipinitialspace=True)
 
-            # Uncomment below line to skip the first row if your csv file has a header row
-            # next(csv_reader, None)
+            next(csv_reader, None)
 
             for id_, row in enumerate(csv_reader):
                 tokens, ner_tags = row
                 # Optional preprocessing here
+                tokens = literal_eval(tokens)
+                ner_tags = literal_eval(ner_tags)
                 yield id_, {"tokens": tokens, "ner_tags": ner_tags}