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multilexnorm.py

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+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+
+_CITATION = """\
+@inproceedings{multilexnorm,
+  title= {MultiLexNorm: A Shared Task on Multilingual Lexical Normalization,
+  author = "van der Goot, Rob and Ramponi et al.",
+  booktitle = "Proceedings of the 7th Workshop on Noisy User-generated Text (W-NUT 2021)",
+  year = "2021",
+  publisher = "Association for Computational Linguistics",
+  address = "Punta Cana, Dominican Republic"
+}
+"""
+
+_DATASETNAME = "multilexnorm"
+
+_DESCRIPTION = """\
+MULTILEXNPRM is a new benchmark dataset for multilingual lexical normalization
+including 12 language variants,
+we here specifically work on the Indonisian-english language.
+"""
+
+_HOMEPAGE = "https://bitbucket.org/robvanderg/multilexnorm/src/master/"
+
+_LOCAL = False
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LICENSE = "CC-BY-NC-SA 4.0"
+
+_URLS = {
+    "train": "https://bitbucket.org/robvanderg/multilexnorm/raw/e92e5b8f111fea15c7c88aebd4c058f6a1ca8d74/data/iden/train.norm",
+    "validation": "https://bitbucket.org/robvanderg/multilexnorm/raw/e92e5b8f111fea15c7c88aebd4c058f6a1ca8d74/data/iden/dev.norm",
+    "test": "https://bitbucket.org/robvanderg/multilexnorm/raw/e92e5b8f111fea15c7c88aebd4c058f6a1ca8d74/data/iden/test.norm",
+}
+
+_SUPPORTED_TASKS = [Tasks.MULTILEXNORM]
+
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class MultiLexNorm(datasets.GeneratorBasedBuilder):
+    """MultiLexNorm is a new benchmark dataset for lexical normalization for indonisian English language. which is the translation
+     of social media text to canonical text:
+    new pix      comming tomoroe
+    new pictures coming  tomorrow
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="multilexnorm_source",
+            version=_SOURCE_VERSION,
+            description="multilexnorm source schema",
+            schema="source",
+            subset_id="multilexnorm",
+        ),
+        NusantaraConfig(
+            name="multilexnorm_nusantara_t2t",
+            version=_NUSANTARA_VERSION,
+            description="multilexnorm Nusantara schema",
+            schema="nusantara_t2t",
+            subset_id="multilexnorm",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "multilexnorm_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+
+            features = datasets.Features(
+                {
+                    "src_sent": datasets.Value("string"),
+                    "id": datasets.Value("string"),
+                    "norm_sent": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "nusantara_t2t":
+            features = schemas.text2text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+
+        train_path = Path(dl_manager.download_and_extract(_URLS["train"]))
+        validation_path = Path(dl_manager.download_and_extract(_URLS["validation"]))
+        test_path = Path(dl_manager.download_and_extract(_URLS["test"]))
+        data_files = {
+            "train": train_path,
+            "validation": validation_path,
+            "test": test_path,
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_files["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_files["test"],
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_files["validation"],
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+
+        curSent = []
+        print(filepath)
+        if self.config.schema == "source":
+            i = 0
+            for line in open(filepath):
+                tok = line.strip("\n").split("\t")
+
+                if tok == [""] or tok == []:
+                    ex = {"id": str(i), 
+                          "src_sent": " ".join([x[0] for x in curSent]), 
+                          "norm_sent": " ".join([x[1] for x in curSent])}
+                    yield i, ex
+                    i += 1
+                    curSent = []
+
+                else:
+                    if len(tok) > 2:
+                        print("erroneous input, line:\n" + line + "\n in file " + filepath + " contains more then two elements")
+                    if len(tok) == 1:
+                        tok.append("")
+                    curSent.append(tok)
+
+        elif self.config.schema == "nusantara_t2t":
+            i = 0
+            for line in open(filepath):
+                tok = line.strip("\n").split("\t")
+
+                if tok == [""] or tok == []:
+                    ex = {"id": str(i), 
+                          "text_1": " ".join([x[0] for x in curSent]), 
+                          "text_2": " ".join([x[1] for x in curSent]), 
+                          "text_1_name": "src_sent", 
+                          "text_2_name": "norm_sent"}
+                    yield i, ex
+                    i += 1
+                    curSent = []
+
+                else:
+                    if len(tok) > 2:
+                        print("erroneous input, line:\n" + line + "\n in file " + filepath + " contains more then two elements")
+                    if len(tok) == 1:
+                        tok.append("")
+                    curSent.append(tok)
+