Add loading script

bertic_data.py

@@ -0,0 +1,123 @@
+import datasets
+import gzip
+import json
+_URL = "http://nl.ijs.si/nikola/dedup_hbs/"
+_URLS = [
+    # "macocu.hbs.translit.dedup.lines.gz",
+    # "hr_news.translit.dedup.lines.gz",
+    # "srwac.translit.dedup.lines.gz",
+    "riznica.translit.dedup.lines.gz",
+    # "mC4.sr.translit.dedup.lines.gz",
+    # "hrwac.translit.dedup.lines.gz",
+    # "cnrwac.translit.dedup.lines.gz",
+    # "classla-sr.translit.dedup.lines.gz",
+    # "classla-hr.translit.dedup.lines.gz",
+    #  "classla-bs.translit.dedup.lines.gz",
+    #  "cc100-sr.translit.dedup.lines.gz",
+    #  "cc100-hr.translit.dedup.lines.gz",
+    #  "bswac.translit.dedup.lines.gz",
+]
+_URLS = [_URL + i for i in _URLS]
+
+
+_DESCRIPTION = """\
+Data used to train BERTić model and its successors.
+"""
+_CITATION = """
+@inproceedings{ljubesic-lauc-2021-bertic,
+    title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian",
+    author = "Ljube{\v{s}}i{\'c}, Nikola  and
+      Lauc, Davor",
+    editor = "Babych, Bogdan  and
+      Kanishcheva, Olga  and
+      Nakov, Preslav  and
+      Piskorski, Jakub  and
+      Pivovarova, Lidia  and
+      Starko, Vasyl  and
+      Steinberger, Josef  and
+      Yangarber, Roman  and
+      Marci{\'n}czuk, Micha{\l}  and
+      Pollak, Senja  and
+      P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
+      Robnik-{\v{S}}ikonja, Marko",
+    booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
+    month = apr,
+    year = "2021",
+    address = "Kiyv, Ukraine",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.bsnlp-1.5",
+    pages = "37--42",
+    abstract = "In this paper we describe a transformer model pre-trained on 8 billion tokens of crawled text from the Croatian, Bosnian, Serbian and Montenegrin web domains. We evaluate the transformer model on the tasks of part-of-speech tagging, named-entity-recognition, geo-location prediction and commonsense causal reasoning, showing improvements on all tasks over state-of-the-art models. For commonsense reasoning evaluation we introduce COPA-HR - a translation of the Choice of Plausible Alternatives (COPA) dataset into Croatian. The BERTi{\'c} model is made available for free usage and further task-specific fine-tuning through HuggingFace.",
+}"""
+
+
+
+class BerticDataConfig(datasets.BuilderConfig):
+    """BuilderConfig for Bertic data sample."""
+
+    def __init__(self, *args, subsets, **kwargs):
+        """BuilderConfig for BerticData.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(BerticDataConfig, self).__init__(**kwargs)
+        self.subsets = subsets
+
+
+class BerticData(datasets.GeneratorBasedBuilder):
+    """Bertic dataset, used for training Bertic model."""
+    VERSION = datasets.Version("1.0.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from BerticDataConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        BerticDataConfig(
+            name='default',
+            subsets=['arxiv', 'open-web-math', 'algebraic-stack'],
+            version=VERSION,
+            description="All subsets"
+        )]
+
+    self._URLS = _URLS
+    def _info(self):
+        features = datasets.Features(
+            {
+                "text": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+    
+        urls_to_download = self._URLS
+        urls_to_download = {i, url for i, url in enumerate(self._URLS)}
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, 
+                gen_kwargs={"filepath": downloaded_files[i]}
+                ) for i in urls_to_download.keys()
+        ]
+
+    def _generate_examples(self, data_files):
+        key = 0 
+        for name in data_files: 
+            with gzip.open(name, "rb") as f: 
+                for line in f.readlines():
+                    yield key, {"text": line.decode("uft-8").strip()}
+                    key += 1