print the loaded configuration

WikiNER.py

@@ -18,20 +18,33 @@ import pandas as pd
 import datasets
 
 
-
-# Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """
+@inproceedings{ghaddar-langlais-2017-winer,
+    title = "{W}i{NER}: A {W}ikipedia Annotated Corpus for Named Entity Recognition",
+    author = "Ghaddar, Abbas  and
+      Langlais, Phillippe",
+    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
+    month = nov,
+    year = "2017",
+    address = "Taipei, Taiwan",
+    publisher = "Asian Federation of Natural Language Processing",
+    url = "https://aclanthology.org/I17-1042",
+    pages = "413--422",
+    abstract = "We revisit the idea of mining Wikipedia in order to generate named-entity annotations. We propose a new methodology that we applied to English Wikipedia to build WiNER, a large, high quality, annotated corpus. We evaluate its usefulness on 6 NER tasks, comparing 4 popular state-of-the art approaches. We show that LSTM-CRF is the approach that benefits the most from our corpus. We report impressive gains with this model when using a small portion of WiNER on top of the CONLL training material. Last, we propose a simple but efficient method for exploiting the full range of WiNER, leading to further improvements.",
+}
 """
 
-# You can copy an official description
-_DESCRIPTION = """\
+_DESCRIPTION = """
+Created by Nothman et al. at 2013, the WikiNER Dataset
+contains 7,200 manually-labelled Wikipedia articles 
+across nine languages: English, German, French, Polish,
+Italian, Spanish,Dutch, Portuguese and Russian., in
+Multi-Lingual language. Containing 7,2 in Text file format.
 """
-_URLS = {
-    "en": "https://huggingface.co/datasets/meczifho/WikiNER/tree/main/data",
-    "fr": "https://huggingface.co/datasets/meczifho/WikiNER/tree/main/data",
-}
 
 _HOMEPAGE = ""
+LANGUAGES = ["en", "fr", "de", "es", "it", "nl", "pt", "pl", "ru"]
+_URLS = {lang: "https://huggingface.co/datasets/mnaguib/WikiNER/tree/main/data" for lang in LANGUAGES}
 
 
 class WikiNER(datasets.GeneratorBasedBuilder):
@@ -39,34 +52,31 @@ class WikiNER(datasets.GeneratorBasedBuilder):
     This is the WikiNER dataset. It is a dataset of sentences from Wikipedia with named entities tagged.
     """
 
-    VERSION = datasets.Version("1.1.0")
+    VERSION = datasets.Version("2.0.0")
 
-    # You will be able to load one or the other configurations in the following list with
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="en", version=VERSION, description=""),
-        datasets.BuilderConfig(name="fr", version=VERSION, description=""), 
+        datasets.BuilderConfig(name="en", version=VERSION, description="English dataset"),
+        datasets.BuilderConfig(name="fr", version=VERSION, description="French dataset"),
+        datasets.BuilderConfig(name="de", version=VERSION, description="German dataset"),
+        datasets.BuilderConfig(name="es", version=VERSION, description="Spanish dataset"),
+        datasets.BuilderConfig(name="it", version=VERSION, description="Italian dataset"),
+        datasets.BuilderConfig(name="nl", version=VERSION, description="Dutch dataset"),
+        datasets.BuilderConfig(name="pt", version=VERSION, description="Portuguese dataset"),
+        datasets.BuilderConfig(name="pl", version=VERSION, description="Polish dataset"),
+        datasets.BuilderConfig(name="ru", version=VERSION, description="Russian dataset"),
     ]
-    
-    DEFAULT_CONFIG_NAME = "en"  
+
+    DEFAULT_CONFIG_NAME = "en"
 
     def _info(self):
-        if self.config.name == "fr":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "words": datasets.Sequence(datasets.Value("string")),
-                    "ner_tags": datasets.Sequence(datasets.Value("int32")),
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        else: 
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "words": datasets.Sequence(datasets.Value("string")),
-                    "ner_tags": datasets.Sequence(datasets.Value("int32")),
-                }
-            )
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "words": datasets.Sequence(datasets.Value("string")),
+                "ner_tags": datasets.Sequence(datasets.Value("int32")),
+            }
+        )
+
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=features,
@@ -76,8 +86,10 @@ class WikiNER(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        train_pq = dl_manager.download_and_extract('https://huggingface.co/datasets/meczifho/WikiNER/resolve/main/data/train.parquet')
-        test_pq = dl_manager.download_and_extract('https://huggingface.co/datasets/meczifho/WikiNER/resolve/main/data/test.parquet')
+        train_pq = dl_manager.download_and_extract(
+            'https://huggingface.co/datasets/mnaguib/WikiNER/resolve/main/data/train.parquet')
+        test_pq = dl_manager.download_and_extract(
+            'https://huggingface.co/datasets/mnaguib/WikiNER/resolve/main/data/test.parquet')
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -101,15 +113,12 @@ class WikiNER(datasets.GeneratorBasedBuilder):
     def _generate_examples(self, filepath, split):
         # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        #read the parquet file
         df = pd.read_parquet(filepath)
-        if self.config.name == "en":
-            df = df[df['id'].str.startswith("en")]
-        else:
-            df = df[df['id'].str.startswith("fr")]
+        print("Loading only {} examples".format(self.config.name))
+        df = df[df['id'].str.startswith(self.config.name)]
         for key, row in df.iterrows():
             yield key, {
                 "id": row["id"],
                 "words": row["words"],
-                "ner_tags": row["tags"],
+                "ner_tags": row["ner_tags"],
             }

data/test.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:fb47bc2c89270bd33ac9353931f0955bf044603513311bfa146b23480a3ecb40
-size 20778038
+oid sha256:6a2730b8676b1157632c8a51037f1b7305f136e93ee19b8a6d259872429e5932
+size 20779194

data/train.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:84d55f1327515b7655c72bd992d560301e50bd69416a4b9e16849748d8e746ec
-size 189882205
+oid sha256:3e91b2ddaff1ee61c53780d3b57e0d305ac9015c5d8ee843087d420a6faa0917
+size 189892537