Add loading script.

corpus-carolina.py

@@ -0,0 +1,210 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset
+# script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Carolina Corpus"""
+
+from lxml import etree
+import os
+import datasets
+
+
+_HOMEPAGE = "https://sites.usp.br/corpuscarolina/"
+
+
+_DESCRIPTION = """
+Carolina is an Open Corpus for Linguistics and Artificial Intelligence with a
+robust volume of texts of varied typology in contemporary Brazilian Portuguese
+(1970-2021).
+"""
+
+
+_CITATION = r"""
+@misc{corpusCarolinaV1.1,
+    title={
+        Carolina:
+        The Open Corpus for Linguistics and Artificial Intelligence},
+    author={
+        Finger, Marcelo and
+        Paixão de Sousa, Maria Clara and
+        Namiuti, Cristiane and
+        Martins do Monte, Vanessa and
+        Costa, Aline Silva and
+        Serras, Felipe Ribas and
+        Sturzeneker, Mariana Lourenço and
+        Guets, Raquel de Paula and
+        Mesquita, Renata Morais and
+        Mello, Guilherme Lamartine de and
+        Crespo, Maria Clara Ramos Morales and
+        Rocha, Maria Lina de Souza Jeannine and
+        Brasil, Patrícia and
+        Silva, Mariana Marques da and
+        Palma, Mayara Feliciano},
+    howpublished={\url{https://sites.usp.br/corpuscarolina/corpus}},
+    year={2022},
+    note={Version 1.1 (Ada)},
+}
+"""
+
+
+_LICENSE = """
+The Open Corpus for Linguistics and Artificial Intelligence (Carolina) was
+compiled for academic purposes, namely linguistic and computational analysis.
+It is composed of texts assembled in various digital repositories, whose
+licenses are multiple and therefore should be observed when making use of the
+corpus. The Carolina headers are licensed under Creative Commons
+Attribution-NonCommercial-ShareAlike 4.0 International."
+"""
+
+
+def _taxonomies():
+    """Creates a map between taxonomy code and name
+
+    Returns
+    -------
+    dict
+        The dictionary of codes and names.
+    """
+    return dict(
+        dat="datasets and other corpora",
+        jud="judicial branch",
+        leg="legislative branch",
+        pub="public domain works",
+        soc="social media",
+        uni="university_domains",
+        wik="wikis",
+    )
+
+
+_VERSION = "1.1.0"
+_CORPUS_URL = "corpus/{taxonomy}/"
+_CHECKSUM_FNAME = _CORPUS_URL + "checksum.sha256"
+
+
+class CarolinaConfig(datasets.BuilderConfig):
+    """Carolina Configuration."""
+    def __init__(self, taxonomy: str = None, **kwargs):
+        """BuilderConfig for Carolina
+
+        Parameters
+        ----------
+        taxonomy : str
+            The taxonomy code (3 letters). The code defines the taxonomy
+            to download. If `None`, all taxonomies will be downloaded.
+        **kwargs
+            Arguments passed to super.
+        """
+        # validates taxonomy
+        if taxonomy is None:
+            taxonomy = "all"
+        elif taxonomy != "all" and taxonomy not in _taxonomies():
+            raise ValueError(f"Invalid taxonomy: {taxonomy}")
+
+        # custom name and description
+        description = "Carolina corpus."
+        if taxonomy == "all":
+            name = "carolina"
+            description += " Using all taxonomies."
+        else:
+            name = _taxonomies()[taxonomy]
+            description += f" Using taxonomy {taxonomy}"
+
+        super(CarolinaConfig, self).__init__(
+            name=name, description=description, **kwargs)
+
+        # Carolina attributes
+        self.taxonomy = taxonomy
+        self.version = datasets.Version(_VERSION)
+
+
+class Carolina(datasets.GeneratorBasedBuilder):
+    """Carolina Downloader and Builder"""
+
+    BUILDER_CONFIG_CLASS = CarolinaConfig
+
+    def _info(self):
+        features = datasets.Features({
+            "meta": datasets.Value("string"),
+            "text": datasets.Value("string")
+        })
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            features=features,
+            license=_LICENSE
+        )
+
+    def _split_generators(self, dl_manager):
+        # list taxonomies to download
+        if self.config.taxonomy == "all":
+            taxonomies = _taxonomies().values()
+        else:
+            taxonomies = [_taxonomies()[self.config.taxonomy]]
+
+        zip_urls = dict()
+        for taxonomy in taxonomies:
+            # download checksum file
+            checksum_path = _CHECKSUM_FNAME.format(taxonomy=taxonomy)
+            checksum_path = dl_manager.download(checksum_path)
+
+            tax_url = _CORPUS_URL.format(taxonomy=taxonomy)
+
+            # extract and build zip urls
+            with open(checksum_path, encoding="utf-8") as cfile:
+                for line in cfile:
+                    fname = line.split()[1]
+                    if fname.endswith(".xml.zip"):
+                        zip_url = tax_url + fname           # download url
+                        fname = os.path.split(fname)[1]     # removes subdirs
+                        fname = fname[:-4]                  # removes .zip
+                        zip_urls[fname] = zip_url           # xml -> zip url
+
+        # extractions are made in cache folders and
+        # the path returned is the folder path, not the
+        # extracted file (or files). It is necessary to
+        # build the xml file path. It is made using the
+        # zip_urls dict structure.
+        extracted = dl_manager.download_and_extract(zip_urls)
+        xml_files = [os.path.join(v, k) for k, v in extracted.items()]
+        xml_files = sorted(xml_files)
+
+        return [
+            datasets.SplitGenerator(
+                name="corpus",
+                gen_kwargs={"filepaths": xml_files}
+            )
+        ]
+
+    def _generate_examples(self, filepaths):
+        TEI_NS = "{http://www.tei-c.org/ns/1.0}"
+        parser_params = dict(
+            huge_tree=True,
+            encoding="utf-8",
+            tag=f"{TEI_NS}TEI"
+        )
+
+        _key = 0
+        for path in filepaths:
+            # parse xml file
+            for _, tei in etree.iterparse(path, **parser_params):
+                header = tei.find(f"{TEI_NS}teiHeader")
+
+                example = {
+                    "meta": etree.tostring(
+                        header, encoding="utf-8").decode("utf-8"),
+                    "text": tei.find(f".//{TEI_NS}body/{TEI_NS}p").text
+                }
+                yield _key, example
+                _key += 1