Convert dataset to Parquet

README.md

@@ -20,7 +20,7 @@ task_ids: []
 paperswithcode_id: umc005-english-urdu
 pretty_name: UMC005 English-Urdu
 dataset_info:
-- config_name: bible
+- config_name: all
   features:
   - name: id
     dtype: string
@@ -32,17 +32,17 @@ dataset_info:
         - en
   splits:
   - name: train
-    num_bytes: 2350730
-    num_examples: 7400
+    num_bytes: 5360817
+    num_examples: 13400
   - name: validation
-    num_bytes: 113476
-    num_examples: 300
+    num_bytes: 160035
+    num_examples: 514
   - name: test
-    num_bytes: 104678
-    num_examples: 257
-  download_size: 3683565
-  dataset_size: 2568884
-- config_name: quran
+    num_bytes: 151809
+    num_examples: 457
+  download_size: 2805950
+  dataset_size: 5672661
+- config_name: bible
   features:
   - name: id
     dtype: string
@@ -54,17 +54,17 @@ dataset_info:
         - en
   splits:
   - name: train
-    num_bytes: 2929711
-    num_examples: 6000
+    num_bytes: 2395118
+    num_examples: 7400
   - name: validation
-    num_bytes: 43499
-    num_examples: 214
+    num_bytes: 115264
+    num_examples: 300
   - name: test
-    num_bytes: 44413
-    num_examples: 200
-  download_size: 3683565
-  dataset_size: 3017623
-- config_name: all
+    num_bytes: 106208
+    num_examples: 257
+  download_size: 1279964
+  dataset_size: 2616590
+- config_name: quran
   features:
   - name: id
     dtype: string
@@ -76,16 +76,42 @@ dataset_info:
         - en
   splits:
   - name: train
-    num_bytes: 5280441
-    num_examples: 13400
+    num_bytes: 2965699
+    num_examples: 6000
   - name: validation
-    num_bytes: 156963
-    num_examples: 514
+    num_bytes: 44771
+    num_examples: 214
   - name: test
-    num_bytes: 149079
-    num_examples: 457
-  download_size: 3683565
-  dataset_size: 5586483
+    num_bytes: 45601
+    num_examples: 200
+  download_size: 1530371
+  dataset_size: 3056071
+configs:
+- config_name: all
+  data_files:
+  - split: train
+    path: all/train-*
+  - split: validation
+    path: all/validation-*
+  - split: test
+    path: all/test-*
+  default: true
+- config_name: bible
+  data_files:
+  - split: train
+    path: bible/train-*
+  - split: validation
+    path: bible/validation-*
+  - split: test
+    path: bible/test-*
+- config_name: quran
+  data_files:
+  - split: train
+    path: quran/train-*
+  - split: validation
+    path: quran/validation-*
+  - split: test
+    path: quran/test-*
 ---
 
 # Dataset Card for UMC005 English-Urdu

all/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+oid sha256:aed70abd0ae4f4454cf749ad861a1d729e0d0700375fe625eedc2deccf26f04a
+size 75680

all/train-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f758104aac7324fb7692915e3f03042f264c53966a41e634d501ef4e63e1d284
+size 2651244

all/validation-00000-of-00001.parquet

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+oid sha256:d579e449bf487db726e329a1514f476899368ac0a2a7c39d7c37501d42b7ef6d
+size 79026

bible/test-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bab87966e46e38c934f89f57a9016f390d18c1f9f3210080123a2efe97747eae
+size 51924

bible/train-00000-of-00001.parquet

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+oid sha256:17398c01e8f828b3a1909255fb626cc18bfc88f25acf4fcdc07ef96cd33f6d44
+size 1171873

bible/validation-00000-of-00001.parquet

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+oid sha256:f8f44672ec813774bae5590867744767213504037effe3e9cf418f40f924b8e4
+size 56167

quran/test-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a54ca18643500a63e2544395d7502ecf702ba33b929be7a85392eb64d6906300
+size 27563

quran/train-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6b910a4e9f81b06c7504329baeea951189a820240e1582d66db4ed9455e1f1e7
+size 1475594

quran/validation-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7c835ac28a98af62c07d4bc197e392755bc0cd732f10d36614d39410f8419f45
+size 27214

um005.py

@@ -1,145 +0,0 @@
-# coding=utf-8
-# Copyright 2020 HuggingFace Datasets Authors.
-#
-# 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.
-
-# Lint as: python3
-import os
-
-import datasets
-
-
-_DESCRIPTION = """\
-UMC005 English-Urdu is a parallel corpus of texts in English and Urdu language with sentence alignments. The corpus can be used for experiments with statistical machine translation.
-
-The texts come from four different sources:
-- Quran
-- Bible
-- Penn Treebank (Wall Street Journal)
-- Emille corpus
-
-The authors provide the religious texts of Quran and Bible for direct download. Because of licensing reasons, Penn and Emille texts cannot be redistributed freely. However, if you already hold a license for the original corpora, we are able to provide scripts that will recreate our data on your disk. Our modifications include but are not limited to the following:
-
-- Correction of Urdu translations and manual sentence alignment of the Emille texts.
-- Manually corrected sentence alignment of the other corpora.
-- Our data split (training-development-test) so that our published experiments can be reproduced.
-- Tokenization (optional, but needed to reproduce our experiments).
-- Normalization (optional) of e.g. European vs. Urdu numerals, European vs. Urdu punctuation, removal of Urdu diacritics.
-"""
-_HOMEPAGE_URL = "https://ufal.mff.cuni.cz/umc/005-en-ur/"
-_URL = "https://ufal.mff.cuni.cz/umc/005-en-ur/download.php?f=umc005-corpus.zip"
-_CITATION = """\
-@unpublished{JaZeWordOrderIssues2011,
-author      = {Bushra Jawaid and Daniel Zeman},
-title       = {Word-Order Issues in {English}-to-{Urdu} Statistical Machine Translation},
-year        = {2011},
-journal     = {The Prague Bulletin of Mathematical Linguistics},
-number      = {95},
-institution = {Univerzita Karlova},
-address     = {Praha, Czechia},
-issn        = {0032-6585},
-}
-"""
-
-_ALL = "all"
-_VERSION = "1.0.0"
-_SOURCES = ["bible", "quran"]
-_SOURCES_FILEPATHS = {
-    s: {
-        "train": {"urdu": "train.ur", "english": "train.en"},
-        "dev": {"urdu": "dev.ur", "english": "dev.en"},
-        "test": {"urdu": "test.ur", "english": "test.en"},
-    }
-    for s in _SOURCES
-}
-
-
-class UM005Config(datasets.BuilderConfig):
-    def __init__(self, *args, sources=None, **kwargs):
-        super().__init__(*args, version=datasets.Version(_VERSION, ""), **kwargs)
-        self.sources = sources
-
-    @property
-    def language_pair(self):
-        return ("ur", "en")
-
-
-class UM005(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        UM005Config(name=source, sources=[source], description=f"Source: {source}.") for source in _SOURCES
-    ] + [
-        UM005Config(
-            name=_ALL,
-            sources=_SOURCES,
-            description="All sources included: bible, quran",
-        )
-    ]
-    BUILDER_CONFIG_CLASS = UM005Config
-    DEFAULT_CONFIG_NAME = _ALL
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "translation": datasets.Translation(languages=self.config.language_pair),
-                },
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE_URL,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        path = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"datapath": path, "datatype": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"datapath": path, "datatype": "dev"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"datapath": path, "datatype": "test"},
-            ),
-        ]
-
-    def _generate_examples(self, datapath, datatype):
-        if datatype == "train":
-            ur_file = "train.ur"
-            en_file = "train.en"
-        elif datatype == "dev":
-            ur_file = "dev.ur"
-            en_file = "dev.en"
-        elif datatype == "test":
-            ur_file = "test.ur"
-            en_file = "test.en"
-        else:
-            raise Exception("Invalid dataype. Try one of: dev, train, test")
-
-        for source in self.config.sources:
-            urdu_path = os.path.join(datapath, source, ur_file)
-            english_path = os.path.join(datapath, source, en_file)
-            with open(urdu_path, encoding="utf-8") as u, open(english_path, encoding="utf-8") as e:
-                for sentence_counter, (x, y) in enumerate(zip(u, e)):
-                    x = x.strip()
-                    y = y.strip()
-                    id_ = f"{source}-{sentence_counter}"
-                    yield id_, {
-                        "id": id_,
-                        "translation": {"ur": x, "en": y},
-                    }