Convert dataset to Parquet (#4)

- Convert dataset to Parquet (9fa34722105606c1cd6f70916a733de60d0d6a5f)
- Delete loading script (8603fef4d12b1c37703a1149d858ae37d904d103)

README.md

@@ -38,16 +38,25 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 14362998
+    num_bytes: 14362966
     num_examples: 70351
   - name: test
-    num_bytes: 2146857
+    num_bytes: 2146853
     num_examples: 9692
   - name: validation
-    num_bytes: 2407643
+    num_bytes: 2407635
     num_examples: 10329
-  download_size: 18284850
-  dataset_size: 18917498
+  download_size: 8697678
+  dataset_size: 18917454
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+  - split: validation
+    path: data/validation-*
 ---
 
 # Dataset Card for ilist

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d51eb14a4b5fcc20ebbc38655adff97f67c5a0a6fce24ea8a7e3ae1b79dd724e
+size 993642

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:855901391da0e3bd1ef32b747d6c27aa12dd87d65974dd1276a19b9aa0c64e1e
+size 6611426

data/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9687c428d3263f17877380837531242f3219ab95b7ccbf1d44975b731181ed73
+size 1092610

ilist.py

@@ -1,117 +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.
-"""Indo-Aryan Language Identification Shared Task Dataset"""
-
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_CITATION = r"""\
-@inproceedings{zampieri-etal-2018-language,
-    title = "Language Identification and Morphosyntactic Tagging: The Second {V}ar{D}ial Evaluation Campaign",
-    author = {Zampieri, Marcos  and
-      Malmasi, Shervin  and
-      Nakov, Preslav  and
-      Ali, Ahmed  and
-      Shon, Suwon  and
-      Glass, James  and
-      Scherrer, Yves  and
-      Samard{\v{z}}i{\'c}, Tanja  and
-      Ljube{\v{s}}i{\'c}, Nikola  and
-      Tiedemann, J{\"o}rg  and
-      van der Lee, Chris  and
-      Grondelaers, Stefan  and
-      Oostdijk, Nelleke  and
-      Speelman, Dirk  and
-      van den Bosch, Antal  and
-      Kumar, Ritesh  and
-      Lahiri, Bornini  and
-      Jain, Mayank},
-    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
-    month = aug,
-    year = "2018",
-    address = "Santa Fe, New Mexico, USA",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/W18-3901",
-    pages = "1--17",
-}
-"""
-
-_DESCRIPTION = """\
-This dataset is introduced in a task which aimed at identifying 5 closely-related languages of Indo-Aryan language family –
-Hindi (also known as Khari Boli), Braj Bhasha, Awadhi, Bhojpuri, and Magahi.
-"""
-
-_URL = "https://raw.githubusercontent.com/kmi-linguistics/vardial2018/master/dataset/{}.txt"
-
-
-class Ilist(datasets.GeneratorBasedBuilder):
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "language_id": datasets.ClassLabel(names=["AWA", "BRA", "MAG", "BHO", "HIN"]),
-                    "text": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/kmi-linguistics/vardial2018",
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="text", label_column="language_id")],
-        )
-
-    def _split_generators(self, dl_manager):
-        filepaths = dl_manager.download_and_extract(
-            {
-                "train": _URL.format("train"),
-                "test": _URL.format("gold"),
-                "dev": _URL.format("dev"),
-            }
-        )
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": filepaths["train"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": filepaths["test"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": filepaths["dev"],
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        with open(filepath, "r", encoding="utf-8") as file:
-            for idx, row in enumerate(file):
-                row = row.strip("\n").split("\t")
-                if len(row) == 1:
-                    continue
-                yield idx, {"language_id": row[1], "text": row[0]}