Update parquet files

SemEval2020Task9CodeSwitch.py

@@ -1,137 +0,0 @@
-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,
-            }

Spanglish/sem_eval2020_task9_code_switch-test.parquet

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Spanglish/sem_eval2020_task9_code_switch-train.parquet

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