Convert dataset to Parquet

README.md

@@ -258,6 +258,7 @@ language_bcp47:
 tags:
 - language-identification
 dataset_info:
+  config_name: WiLI-2018 dataset
   features:
   - name: sentence
     dtype: string
@@ -500,16 +501,23 @@ dataset_info:
           '232': tuk
           '233': kan
           '234': ltg
-  config_name: WiLI-2018 dataset
   splits:
   - name: train
-    num_bytes: 65408201
+    num_bytes: 65408153
     num_examples: 117500
   - name: test
-    num_bytes: 66491260
+    num_bytes: 66491212
     num_examples: 117500
-  download_size: 130516351
-  dataset_size: 131899461
+  download_size: 91718265
+  dataset_size: 131899365
+configs:
+- config_name: WiLI-2018 dataset
+  data_files:
+  - split: train
+    path: WiLI-2018 dataset/train-*
+  - split: test
+    path: WiLI-2018 dataset/test-*
+  default: true
 ---
 
 # Dataset Card for wili_2018

WiLI-2018 dataset/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a1b582dbc8fc71d6baabc9574835d4a5d925b21f9ab2fcea49c7c4e86acc0df
+size 46000315

WiLI-2018 dataset/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:816e63cff8d7d3da5d9a2aaab68527f9d28e9efd22dab45ca0a9b9517c52ecea
+size 45717950

wili_2018.py

@@ -1,334 +0,0 @@
-# coding=utf-8
-# 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.
-
-"""WiLI-2018, the Wikipedia language identification benchmark dataset"""
-
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_CITATION = """\
-@dataset{thoma_martin_2018_841984,
-  author       = {Thoma, Martin},
-  title        = {{WiLI-2018 - Wikipedia Language Identification database}},
-  month        = jan,
-  year         = 2018,
-  publisher    = {Zenodo},
-  version      = {1.0.0},
-  doi          = {10.5281/zenodo.841984},
-  url          = {https://doi.org/10.5281/zenodo.841984}
-}
-"""
-
-_DESCRIPTION = """\
-It is a benchmark dataset for language identification and contains 235000 paragraphs of 235 languages
-"""
-
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://zenodo.org/record/841984"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = "ODC Open Database License v1.0"
-
-
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_TRAIN_DOWNLOAD_URL = "https://drive.google.com/uc?export=download&id=1ZzlIQvw1KNBG97QQCfdatvVrrbeLaM1u"
-_TEST_DOWNLOAD_URL = "https://drive.google.com/uc?export=download&id=1Xx4kFc1Xdzz8AhDasxZ0cSa-a35EQSDZ"
-
-_CLASSES = [
-    "cdo",
-    "glk",
-    "jam",
-    "lug",
-    "san",
-    "rue",
-    "wol",
-    "new",
-    "mwl",
-    "bre",
-    "ara",
-    "hye",
-    "xmf",
-    "ext",
-    "cor",
-    "yor",
-    "div",
-    "asm",
-    "lat",
-    "cym",
-    "hif",
-    "ace",
-    "kbd",
-    "tgk",
-    "rus",
-    "nso",
-    "mya",
-    "msa",
-    "ava",
-    "cbk",
-    "urd",
-    "deu",
-    "swa",
-    "pus",
-    "bxr",
-    "udm",
-    "csb",
-    "yid",
-    "vro",
-    "por",
-    "pdc",
-    "eng",
-    "tha",
-    "hat",
-    "lmo",
-    "pag",
-    "jav",
-    "chv",
-    "nan",
-    "sco",
-    "kat",
-    "bho",
-    "bos",
-    "kok",
-    "oss",
-    "mri",
-    "fry",
-    "cat",
-    "azb",
-    "kin",
-    "hin",
-    "sna",
-    "dan",
-    "egl",
-    "mkd",
-    "ron",
-    "bul",
-    "hrv",
-    "som",
-    "pam",
-    "nav",
-    "ksh",
-    "nci",
-    "khm",
-    "sgs",
-    "srn",
-    "bar",
-    "cos",
-    "ckb",
-    "pfl",
-    "arz",
-    "roa-tara",
-    "fra",
-    "mai",
-    "zh-yue",
-    "guj",
-    "fin",
-    "kir",
-    "vol",
-    "hau",
-    "afr",
-    "uig",
-    "lao",
-    "swe",
-    "slv",
-    "kor",
-    "szl",
-    "srp",
-    "dty",
-    "nrm",
-    "dsb",
-    "ind",
-    "wln",
-    "pnb",
-    "ukr",
-    "bpy",
-    "vie",
-    "tur",
-    "aym",
-    "lit",
-    "zea",
-    "pol",
-    "est",
-    "scn",
-    "vls",
-    "stq",
-    "gag",
-    "grn",
-    "kaz",
-    "ben",
-    "pcd",
-    "bjn",
-    "krc",
-    "amh",
-    "diq",
-    "ltz",
-    "ita",
-    "kab",
-    "bel",
-    "ang",
-    "mhr",
-    "che",
-    "koi",
-    "glv",
-    "ido",
-    "fao",
-    "bak",
-    "isl",
-    "bcl",
-    "tet",
-    "jpn",
-    "kur",
-    "map-bms",
-    "tyv",
-    "olo",
-    "arg",
-    "ori",
-    "lim",
-    "tel",
-    "lin",
-    "roh",
-    "sqi",
-    "xho",
-    "mlg",
-    "fas",
-    "hbs",
-    "tam",
-    "aze",
-    "lad",
-    "nob",
-    "sin",
-    "gla",
-    "nap",
-    "snd",
-    "ast",
-    "mal",
-    "mdf",
-    "tsn",
-    "nds",
-    "tgl",
-    "nno",
-    "sun",
-    "lzh",
-    "jbo",
-    "crh",
-    "pap",
-    "oci",
-    "hak",
-    "uzb",
-    "zho",
-    "hsb",
-    "sme",
-    "mlt",
-    "vep",
-    "lez",
-    "nld",
-    "nds-nl",
-    "mrj",
-    "spa",
-    "ceb",
-    "ina",
-    "heb",
-    "hun",
-    "que",
-    "kaa",
-    "mar",
-    "vec",
-    "frp",
-    "ell",
-    "sah",
-    "eus",
-    "ces",
-    "slk",
-    "chr",
-    "lij",
-    "nep",
-    "srd",
-    "ilo",
-    "be-tarask",
-    "bod",
-    "orm",
-    "war",
-    "glg",
-    "mon",
-    "gle",
-    "min",
-    "ibo",
-    "ile",
-    "epo",
-    "lav",
-    "lrc",
-    "als",
-    "mzn",
-    "rup",
-    "fur",
-    "tat",
-    "myv",
-    "pan",
-    "ton",
-    "kom",
-    "wuu",
-    "tcy",
-    "tuk",
-    "kan",
-    "ltg",
-]
-
-
-class Wili_2018(datasets.GeneratorBasedBuilder):
-    """WiLI Language Identification Dataset"""
-
-    VERSION = datasets.Version("1.1.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="WiLI-2018 dataset",
-            version=VERSION,
-            description="Plain text of import of WiLI-2018",
-        )
-    ]
-
-    def _info(self):
-
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=datasets.Features(
-                {"sentence": datasets.Value("string"), "label": datasets.features.ClassLabel(names=_CLASSES)}
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="sentence", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
-        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
-        ]
-
-    def _generate_examples(self, filepath):
-
-        with open(filepath, encoding="utf-8") as f:
-            for id_, line in enumerate(f):
-                text, label = line.rsplit(",", 1)
-                text = text.strip('"')
-                label = int(label.strip())
-                yield id_, {"sentence": text, "label": label - 1}