Update NER_Gujarati_data.py

NER_Gujarati_data.py

@@ -1,32 +1,57 @@
-import json
-import os
+# coding=utf-8
+# Copyright 2020 HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
+# Lint as: python3
+"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""
+
+import os
+import json
 import datasets
 
+
+
+
 _CITATION = """\
 """
 
 _DESCRIPTION = """\
 """
 
-_URL = "https://huggingface.co/datasets/Red-8/NER_Gujarati_data/blob/main/datas.zip"
+_URL = "https://huggingface.co/datasets/Red-8/NER_Gujarati_data/resolve/main/data/datas.zip"
+_TRAINING_FILE = "train_data.json"
+_DEV_FILE = "val_data.json"
+_TEST_FILE = "test_data.json"
+
 
 class RedConfig(datasets.BuilderConfig):
-    """BuilderConfig for Red dataset"""
+    """BuilderConfig for Red"""
 
     def __init__(self, **kwargs):
-        """BuilderConfig Red datset.
+        """BuilderConfig forRed.
+
         Args:
           **kwargs: keyword arguments forwarded to super.
         """
         super(RedConfig, self).__init__(**kwargs)
 
+
 class Red(datasets.GeneratorBasedBuilder):
+    """Red dataset."""
 
     BUILDER_CONFIGS = [
-        RedConfig(
-            name="NER_Gujarati_data", version=datasets.Version("1.0.0")
-        ),
+        RedConfig(name="NER_Gujarati_data", version=datasets.Version("1.0.0"), description="Red dataset"),
     ]
 
     def _info(self):
@@ -60,28 +85,17 @@ class Red(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-
-        data_dir = dl_manager.download_and_extract(_URL)
+        downloaded_file = dl_manager.download_and_extract(_URL)
+        data_files = {
+            "train": os.path.join(downloaded_file, _TRAINING_FILE),
+            "dev": os.path.join(downloaded_file, _DEV_FILE),
+            "test": os.path.join(downloaded_file, _TEST_FILE),
+        }
 
         return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir,"train_data.json"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir,"test_data.json"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir,"val_data.json"),
-                },
-            ),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
         ]
 
     def _generate_examples(self, filepath):