Convert dataset to Parquet (#4)

- Convert dataset to Parquet (8e4f9bdcb7ba90a088b245cdc990832acb36fcd3)
- Delete loading script (e8237fbaae95a1676de515a2932f10b66cb36784)

README.md

@@ -21,6 +21,7 @@ task_ids:
 paperswithcode_id: ronec
 pretty_name: RONEC
 dataset_info:
+  config_name: ronec
   features:
   - name: id
     dtype: int32
@@ -65,19 +66,28 @@ dataset_info:
           '28': I-ORDINAL
           '29': B-FACILITY
           '30': I-FACILITY
-  config_name: ronec
   splits:
   - name: train
-    num_bytes: 8701577
+    num_bytes: 8701559
     num_examples: 9000
   - name: validation
-    num_bytes: 1266490
+    num_bytes: 1266470
     num_examples: 1330
   - name: test
-    num_bytes: 1902224
+    num_bytes: 1902205
     num_examples: 2000
-  download_size: 14675943
-  dataset_size: 11870291
+  download_size: 2935846
+  dataset_size: 11870234
+configs:
+- config_name: ronec
+  data_files:
+  - split: train
+    path: ronec/train-*
+  - split: validation
+    path: ronec/validation-*
+  - split: test
+    path: ronec/test-*
+  default: true
 ---
 
 # Dataset Card for RONEC

ronec.py

@@ -1,161 +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.
-
-import json
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@article{dumitrescu2019introducing,
-  title={Introducing RONEC--the Romanian Named Entity Corpus},
-  author={Dumitrescu, Stefan Daniel and Avram, Andrei-Marius},
-  journal={arXiv preprint arXiv:1909.01247},
-  year={2019}
-}
-"""
-
-# You can copy an official description
-_DESCRIPTION = """\
-RONEC - the Romanian Named Entity Corpus, at version 2.0, holds 12330 sentences with over 0.5M tokens, annotated with 15 classes, to a total of 80.283 distinctly annotated entities. It is used for named entity recognition and represents the largest Romanian NER corpus to date.
-"""
-
-_HOMEPAGE = "https://github.com/dumitrescustefan/ronec"
-
-_LICENSE = "MIT License"
-
-# The HuggingFace dataset library don't host the datasets but only point to the original files
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URL = "https://raw.githubusercontent.com/dumitrescustefan/ronec/master/data/"
-_TRAINING_FILE = "train.json"
-_DEV_FILE = "valid.json"
-_TEST_FILE = "test.json"
-
-
-class RONECConfig(datasets.BuilderConfig):
-    """BuilderConfig for RONEC dataset"""
-
-    def __init__(self, **kwargs):
-        super(RONECConfig, self).__init__(**kwargs)
-
-
-class RONEC(datasets.GeneratorBasedBuilder):
-    """RONEC dataset"""
-
-    VERSION = datasets.Version("2.0.0")
-    BUILDER_CONFIGS = [
-        RONECConfig(name="ronec", version=VERSION, description="RONEC dataset"),
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "id": datasets.Value("int32"),
-                "tokens": datasets.Sequence(datasets.Value("string")),
-                "ner_ids": datasets.Sequence(datasets.Value("int32")),
-                "space_after": datasets.Sequence(datasets.Value("bool")),
-                "ner_tags": datasets.Sequence(
-                    datasets.features.ClassLabel(
-                        names=[
-                            "O",
-                            "B-PERSON",
-                            "I-PERSON",
-                            "B-ORG",
-                            "I-ORG",
-                            "B-GPE",
-                            "I-GPE",
-                            "B-LOC",
-                            "I-LOC",
-                            "B-NAT_REL_POL",
-                            "I-NAT_REL_POL",
-                            "B-EVENT",
-                            "I-EVENT",
-                            "B-LANGUAGE",
-                            "I-LANGUAGE",
-                            "B-WORK_OF_ART",
-                            "I-WORK_OF_ART",
-                            "B-DATETIME",
-                            "I-DATETIME",
-                            "B-PERIOD",
-                            "I-PERIOD",
-                            "B-MONEY",
-                            "I-MONEY",
-                            "B-QUANTITY",
-                            "I-QUANTITY",
-                            "B-NUMERIC",
-                            "I-NUMERIC",
-                            "B-ORDINAL",
-                            "I-ORDINAL",
-                            "B-FACILITY",
-                            "I-FACILITY",
-                        ]
-                    )
-                ),
-            }
-        )
-
-        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=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-
-        urls_to_download = {"train": _URL + _TRAINING_FILE, "dev": _URL + _DEV_FILE, "test": _URL + _TEST_FILE}
-
-        downloaded_files = dl_manager.download(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": downloaded_files["train"]},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": downloaded_files["dev"]},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": downloaded_files["test"]},
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-
-        logger.info("⏳ Generating examples from = %s", filepath)
-        with open(filepath, "r", encoding="utf-8") as f:
-            data = json.load(f)
-            for instance in data:
-                yield instance["id"], instance

ronec/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+size 473051

ronec/train-00000-of-00001.parquet

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+oid sha256:ab7c6430dd3432da23b40fdbc421d2847c4e9d4e6111fa0d3ed6c2a085c3764e
+size 2140208

ronec/validation-00000-of-00001.parquet

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+oid sha256:76bcf692b9396a48d57153a594bafbdb7cea4756680926d9a37cbd680777a0d9
+size 322587