Datasets:
Convert dataset to Parquet
#2
by
albertvillanova
HF staff
- opened
- README.md +9 -5
- opus_memat.py +0 -88
- xh-en/train-00000-of-00001.parquet +3 -0
README.md
CHANGED
@@ -17,9 +17,9 @@ source_datasets:
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task_categories:
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- translation
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task_ids: []
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-
paperswithcode_id: null
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pretty_name: OpusMemat
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dataset_info:
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features:
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- name: translation
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dtype:
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@@ -27,13 +27,17 @@ dataset_info:
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languages:
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- xh
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- en
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config_name: xh-en
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splits:
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- name: train
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num_bytes:
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num_examples: 154764
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download_size:
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dataset_size:
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---
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# Dataset Card for [opus_memat]
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task_categories:
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- translation
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task_ids: []
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pretty_name: OpusMemat
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dataset_info:
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+
config_name: xh-en
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features:
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- name: translation
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dtype:
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languages:
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- xh
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- en
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splits:
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- name: train
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+
num_bytes: 25400442
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num_examples: 154764
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download_size: 14115561
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dataset_size: 25400442
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configs:
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- config_name: xh-en
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data_files:
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- split: train
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path: xh-en/train-*
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---
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# Dataset Card for [opus_memat]
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opus_memat.py
DELETED
@@ -1,88 +0,0 @@
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Xhosa-English parallel corpora, """
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import os
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import datasets
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_CITATION = """\
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J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th\
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International Conference on Language Resources and Evaluation (LREC 2012)
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"""
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_DESCRIPTION = """\
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Xhosa-English parallel corpora, funded by EPSRC, the Medical Machine Translation project worked on machine translation\
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between ixiXhosa and English, with a focus on the medical domain."""
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_URLs = {"train": "https://object.pouta.csc.fi/OPUS-memat/v1/moses/en-xh.txt.zip"}
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class OpusMemat(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="xh-en", version=VERSION, description="Xhosa-English parallel corpora")
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{"translation": datasets.features.Translation(languages=tuple(self.config.name.split("-")))}
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),
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supervised_keys=None,
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homepage="http://opus.nlpl.eu/memat.php",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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data_dir = 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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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"source_file": os.path.join(data_dir["train"], "memat.en-xh.xh"),
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"target_file": os.path.join(data_dir["train"], "memat.en-xh.en"),
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"split": "train",
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},
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),
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]
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def _generate_examples(self, source_file, target_file, split):
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"""This function returns the examples in the raw (text) form."""
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with open(source_file, encoding="utf-8") as f:
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source_sentences = f.read().split("\n")
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with open(target_file, encoding="utf-8") as f:
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target_sentences = f.read().split("\n")
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assert len(target_sentences) == len(source_sentences), "Sizes do not match: %d vs %d for %s vs %s." % (
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len(source_sentences),
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len(target_sentences),
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source_file,
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target_file,
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)
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source, target = tuple(self.config.name.split("-"))
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for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)):
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result = {"translation": {source: l1, target: l2}}
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yield idx, result
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xh-en/train-00000-of-00001.parquet
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:24225fece9f29cc89525c6fdedc9b9f68a6558c710bd8c3ab884078a9ab784d3
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size 14115561
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