Matej Klemen
commited on
Commit
·
aab5879
1
Parent(s):
fc2cf60
Modify script to enable loading all languages supported by Parlamint3
Browse files- parlaMintSI.py → ParlaMint3.py +47 -33
parlaMintSI.py → ParlaMint3.py
RENAMED
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@@ -14,7 +14,6 @@
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# limitations under the License.
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import csv
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import json
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import os
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import datasets
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@@ -41,8 +40,7 @@ The corpora are also marked to the subcorpus they belong to ("reference", until
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The corpora are encoded according to the Parla-CLARIN TEI recommendation (https://clarin-eric.github.io/parla-clarin/), but have been encoded against the compatible,
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but much stricter ParlaMint encoding guidelines (https://clarin-eric.github.io/ParlaMint/) and schemas (included in this distribution).
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This entry contains the ParlaMint TEI-encoded corpora with the derived plain text versions of the corpora along with TSV metadata of the speeches.
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Also included is the 3.0 release of the data and scripts available at the GitHub repository of the ParlaMint project.
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This dataset contains only Slovenian parliamentary debates.
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"""
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@@ -50,15 +48,22 @@ _HOMEPAGE = "http://hdl.handle.net/11356/1486"
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_LICENSE = "Creative Commons - Attribution 4.0 International (CC BY 4.0)"
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_URLS = {
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}
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class
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"""This dataset contains transcriptions of Slovenian parliamentary debates and relevant metadata."""
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def _info(self):
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features = datasets.Features(
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@@ -69,7 +74,7 @@ class ParlaMintSI(datasets.GeneratorBasedBuilder):
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"Body": datasets.Value("string"),
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"Term": datasets.Value("string"),
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"Session": datasets.Value("string"),
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"Meeting": datasets.Value("
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"Sitting": datasets.Value("string"),
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"Agenda": datasets.Value("string"),
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"Subcorpus": datasets.Value("string"),
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@@ -95,35 +100,44 @@ class ParlaMintSI(datasets.GeneratorBasedBuilder):
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)
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def _split_generators(self, dl_manager):
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urls = _URLS[
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download_path = dl_manager.download_and_extract(urls)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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},
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),
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]
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def _generate_examples(self,
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# limitations under the License.
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import csv
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import os
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import datasets
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The corpora are encoded according to the Parla-CLARIN TEI recommendation (https://clarin-eric.github.io/parla-clarin/), but have been encoded against the compatible,
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but much stricter ParlaMint encoding guidelines (https://clarin-eric.github.io/ParlaMint/) and schemas (included in this distribution).
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This entry contains the ParlaMint TEI-encoded corpora with the derived plain text versions of the corpora along with TSV metadata of the speeches.
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Also included is the 3.0 release of the data and scripts available at the GitHub repository of the ParlaMint project.
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"""
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_LICENSE = "Creative Commons - Attribution 4.0 International (CC BY 4.0)"
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SUPPORTED_LANGS = ["at", "ba", "be", "bg", "cz", "dk", "ee", "es-ct", "es-ga", "fr", "gb", "gr", "hr", "hu", "is",
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"it", "lv", "nl", "no", "pl", "pt", "rs", "se", "si", "tr", "ua"]
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_URLS = {
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lang: f"https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1486/ParlaMint-{lang.upper()}.tgz"
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for lang in SUPPORTED_LANGS
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}
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class ParlaMint3(datasets.GeneratorBasedBuilder):
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"""This dataset contains transcriptions of Slovenian parliamentary debates and relevant metadata."""
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name=lang, version=datasets.Version("1.2.0"), description=f"{lang} parliamentary corpus")
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for lang in SUPPORTED_LANGS
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]
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def _info(self):
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features = datasets.Features(
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"Body": datasets.Value("string"),
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"Term": datasets.Value("string"),
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"Session": datasets.Value("string"),
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"Meeting": datasets.Value("string"),
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"Sitting": datasets.Value("string"),
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"Agenda": datasets.Value("string"),
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"Subcorpus": datasets.Value("string"),
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)
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def _split_generators(self, dl_manager):
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urls = _URLS[self.config.name]
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download_path = dl_manager.download_and_extract(urls)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={"data_dir": download_path}
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)
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]
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def _generate_examples(self, data_dir):
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data_dir = os.path.join(data_dir, f"ParlaMint-{self.config.name.upper()}.txt")
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years = [curr_dir for curr_dir in os.listdir(data_dir) if os.path.isdir(os.path.join(data_dir, curr_dir))]
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years = sorted(years, key=lambda _yr: int(_yr))
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for year_dir in years:
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# Metadata inside tab-separated files
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tsv_files = sorted([f for f in os.listdir(os.path.join(data_dir, year_dir)) if f.endswith(".tsv")])
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for fname in tsv_files:
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tsv_path = os.path.join(data_dir, year_dir, fname)
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# Text data inside txt files
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txt_path = os.path.join(data_dir, year_dir, fname.replace("-meta.tsv", ".txt"))
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with open(tsv_path, "r", encoding="utf-8") as f_tsv, \
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open(txt_path, "r", encoding="utf-8") as f_txt:
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tsv_reader = csv.DictReader(f_tsv, delimiter="\t")
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txt_content = {} # ID of utterance -> text of utterance
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for _line in f_txt:
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_parts = _line.strip().split("\t")
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txt_content[_parts[0]] = _parts[1]
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for row in tsv_reader:
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_id = row["ID"]
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text = txt_content[_id]
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example = {key: row.get(key, "") for key in row}
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example["text"] = text
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yield _id, example
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