Upload CodeSwitching-TE-EN.py

CodeSwitching-TE-EN.py

@@ -1,16 +1,24 @@
-# Loading script for the Telugu-English Codeswitch Transliterate dataset
+# Loading script for the Ancora NER dataset. 
 import datasets
-from conllu import parse_incr
-import conllu
 
 logger = datasets.logging.get_logger(__name__)
 
 _CITATION = """ """
 
-_DESCRIPTION = """Telugu English POS Codeswitch dataset.
+_DESCRIPTION = """AnCora Catalan NER.
+                  This is a dataset for Named Eentity Reacognition (NER) from Ancora corpus adapted for 
+                  Machine Learning and Language Model evaluation purposes.
+                  Since multiwords (including Named Entites) in the original Ancora corpus are aggregated as 
+                  a single lexical item using underscores (e.g. "Ajuntament_de_Barcelona") 
+                  we splitted them to align with word-per-line format, and added conventional Begin-Inside-Outside (IOB)
+                   tags to mark and classify Named Entites. 
+                   We did not filter out the different categories of NEs from Ancora (weak and strong). 
+                   We did 6 minor edits by hand.
+                  AnCora corpus is used under [CC-by] (https://creativecommons.org/licenses/by/4.0/) licence.
+                  This dataset was developed by BSC TeMU as part of the AINA project, and to enrich the Catalan Language Understanding Benchmark (CLUB).
                """
 
-_HOMEPAGE = ""
+_HOMEPAGE = """https://zenodo.org/record/4762031"""
 
 _URL = "https://huggingface.co/datasets/anishka/CodeSwitching-TE-EN/resolve/main/"
 _TRAINING_FILE = "te_mtg-ud-train.conllu"
@@ -18,25 +26,25 @@ _DEV_FILE = "te_mtg-ud-dev.conllu"
 _TEST_FILE = "te_mtg-ud-test.conllu"
 
 
-class TeEnCodeSwitchConfig(datasets.BuilderConfig):
+class AncoraCaNerConfig(datasets.BuilderConfig):
     """ Builder config for the Ancora Ca NER dataset """
 
     def __init__(self, **kwargs):
-        """BuilderConfig for TeEnCodeSwitch.
+        """BuilderConfig for AncoraCaNer.
         Args:
           **kwargs: keyword arguments forwarded to super.
         """
-        super(TeEnCodeSwitchConfig, self).__init__(**kwargs)
+        super(AncoraCaNerConfig, self).__init__(**kwargs)
 
 
-class TeEnCodeSwitch(datasets.GeneratorBasedBuilder):
-    """ Te-En-CodeSwitch dataset."""
+class AncoraCaNer(datasets.GeneratorBasedBuilder):
+    """ AncoraCaNer dataset."""
 
     BUILDER_CONFIGS = [
-        TeEnCodeSwitchConfig(
-            name="Te-En-CodeSwitch",
-            version=datasets.Version("0.0.1"),
-            description="Te-En-CodeSwitch dataset"
+        AncoraCaNerConfig(
+            name="AncoraCaNer",
+            version=datasets.Version("2.0.0"),
+            description="AncoraCaNer dataset"
         ),
     ]
 
@@ -71,12 +79,6 @@ class TeEnCodeSwitch(datasets.GeneratorBasedBuilder):
                             ]
                         )
                     ),
-                    "xpos": datasets.Sequence(datasets.Value("string")),
-                    "feats": datasets.Sequence(datasets.Value("string")),
-                    "head": datasets.Sequence(datasets.Value("string")),
-                    "deprel": datasets.Sequence(datasets.Value("string")),
-                    "deps": datasets.Sequence(datasets.Value("string")),
-                    "misc": datasets.Sequence(datasets.Value("string")),
                 }
             ),
             supervised_keys=None,
@@ -92,8 +94,6 @@ class TeEnCodeSwitch(datasets.GeneratorBasedBuilder):
             "test": f"{_URL}{_TEST_FILE}",
         }
         downloaded_files = dl_manager.download_and_extract(urls_to_download)
-        print ("Downloading files: ")
-        print (urls_to_download)
 
         return [
             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
@@ -102,36 +102,30 @@ class TeEnCodeSwitch(datasets.GeneratorBasedBuilder):
         ]
 
     def _generate_examples(self, filepath):
-
-        sentence = []
-        with open(filepath, 'r') as file:
-            for line in file:
-                line = line.strip()
-                 # Skip comment lines and empty lines
-                if line.startswith('#') or not line:
-                    continue
-                if line == '':
-                    # If an empty line is encountered, yield the current sentence
-                    yield sentence
-                    # Reset the sentence for the next iteration
-                    sentence = []
+        logger.info("⏳ Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            tokens = []
+            ner_tags = []
+            for line in f:
+                if line.startswith("-DOCSTART-") or line == "" or line == "\n" or line.startswith("#"):
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "ner_tags": ner_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        ner_tags = []
                 else:
-                    # Split the line by tabs to get fields
-                    fields = line.split('\t')
-                    # Add the fields to the sentence as a dictionary
-                    sentence.append({
-                        'id': fields[0],
-                        'form': fields[1],
-                        'lemma': fields[2],
-                        'upos': fields[3],
-                        'xpos': fields[4],
-                        'feats': fields[5],
-                        'head': fields[6],
-                        'deprel': fields[7],
-                        'deps': fields[8],
-                        'misc': fields[9]
-                    })
-
-        # Yield the last sentence if there is one
-        if sentence:
-            yield sentence
+                    # AncoraCaNer tokens are space separated
+                    splits = line.split('\t')
+                    tokens.append(splits[9])
+                    ner_tags.append(splits[3].rstrip())
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "ner_tags": ner_tags,
+            }