Create SemEval2020Task9CodeSwitch.py

SemEval2020Task9CodeSwitch.py

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+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{tjong-kim-sang-2002-introduction,
+    title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition",
+    author = "Tjong Kim Sang, Erik F.",
+    booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)",
+    year = "2002",
+    url = "https://www.aclweb.org/anthology/W02-2024",
+}
+"""
+
+_DESCRIPTION = """\
+Named entities are phrases that contain the names of persons, organizations, locations, times and quantities.
+Example:
+[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
+The shared task of CoNLL-2002 concerns language-independent named entity recognition.
+We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups.
+The participants of the shared task will be offered training and test data for at least two languages.
+They will use the data for developing a named-entity recognition system that includes a machine learning component.
+Information sources other than the training data may be used in this shared task.
+We are especially interested in methods that can use additional unannotated data for improving their performance (for example co-training).
+The train/validation/test sets are available in Spanish and Dutch.
+For more details see https://www.clips.uantwerpen.be/semeval2016/ner/ and https://www.aclweb.org/anthology/W02-2024/
+"""
+
+_URL = "https://raw.githubusercontent.com/YaxinCui/Semeval_2020_task9_data/main/Spanglish/"
+
+TRAINING_FILE_Dict = {
+    'Spanglish': "Spanglish_train.conll",
+
+}
+
+TEST_FILE_Dict = {
+    'Spanglish': "Spanglish_dev.conll",
+}
+
+class Semeval2016Config(datasets.BuilderConfig):
+    """BuilderConfig for Semeval2016"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig forSemeval2016.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(Semeval2016Config, self).__init__(**kwargs)
+
+
+class Semeval2016(datasets.GeneratorBasedBuilder):
+    """Semeval2016 dataset."""
+
+    BUILDER_CONFIGS = [
+        Semeval2016Config(name="Spanglish", version=datasets.Version("1.0.0"), description="Semeval2016 Spanish dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "meta": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    #                     "langs": datasets.Sequence(datasets.features.ClassLabel(names=["lang1","lang2","ambiguous","other","ne","unk","mixed","fw","8","9","10","11",]                         )                     ), 
+                    "label": datasets.features.ClassLabel(
+                            names=[
+                                "positive",
+                                "neutral",
+                                "negative",
+                            ]
+                        ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        
+        if self.config.name=="Spanglish":
+            urls_to_download = {
+                "train": f"{_URL}{TRAINING_FILE_Dict[self.config.name]}",
+                "test": f"{_URL}{TEST_FILE_Dict[self.config.name]}",
+            }
+        
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        prev_pos = '$$$'
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            meta = None
+            tokens = []
+            langs = []
+            label = None
+            for line in f:
+                if len(tokens) and (line == "" or line == "\n"):
+                    yield guid, {
+                        "id": str(guid),
+                        "meta": str(meta),
+                        "tokens": tokens,
+                        "label": label,
+                    }
+                    guid += 1
+                    tokens = []
+                    langs = []
+                    labels = []
+                else:
+                    line = line.strip()
+                    # semeval2016 tokens are space separated
+                    splits = [s.rstrip() for s in line.split("	")]
+                    if len(tokens)==0 and line.startswith("meta	"):
+                        meta = splits[1]
+                        label = splits[2]
+                    else:
+                        tokens.append(splits[0])
+                        langs.append(splits[1])
+            # last example
+            
+            yield guid, {
+                "id": str(guid),
+                "meta": str(meta),
+                "tokens": tokens,
+                "label": label,
+            }