albertvillanova HF staff commited on
Commit
4749e1d
1 Parent(s): a64a471

Convert dataset to Parquet (#3)

Browse files

- Convert dataset to Parquet (480cf0fa6ee2345e092a0b5e112b4288b1d7bd17)
- Delete loading script (d80ee8d544056c4e52969412df285c2db26a5ef7)
- Delete legacy dataset_infos.json (02b65ac6335eff7d82448471ea7bbac8db4d5aee)

Files changed (4) hide show
  1. README.md +8 -3
  2. caner.py +0 -122
  3. data/train-00000-of-00001.parquet +3 -0
  4. dataset_infos.json +0 -1
README.md CHANGED
@@ -49,10 +49,15 @@ dataset_info:
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  '20': Time
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  splits:
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  - name: train
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- num_bytes: 5095721
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  num_examples: 258240
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- download_size: 17063406
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- dataset_size: 5095721
 
 
 
 
 
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  ---
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  # Dataset Card for CANER
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  '20': Time
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  splits:
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  - name: train
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+ num_bytes: 5095617
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  num_examples: 258240
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+ download_size: 1459014
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+ dataset_size: 5095617
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: data/train-*
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  ---
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  # Dataset Card for CANER
caner.py DELETED
@@ -1,122 +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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- """A new corpus of tagged data that can be useful for handling the issues in recognition of Classical Arabic named entities"""
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-
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-
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- import csv
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- import os
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-
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- import datasets
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-
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-
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- _CITATION = """\
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- @article{article,
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- author = {Salah, Ramzi and Zakaria, Lailatul},
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- year = {2018},
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- month = {12},
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- pages = {},
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- title = {BUILDING THE CLASSICAL ARABIC NAMED ENTITY RECOGNITION CORPUS (CANERCORPUS)},
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- volume = {96},
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- journal = {Journal of Theoretical and Applied Information Technology}
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- }
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- """
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-
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- _DESCRIPTION = """\
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- Classical Arabic Named Entity Recognition corpus as a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities.
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- """
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-
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- _HOMEPAGE = "https://github.com/RamziSalah/Classical-Arabic-Named-Entity-Recognition-Corpus"
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-
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- # TODO: Add the licence for the dataset here if you can find it
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- _LICENSE = ""
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-
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- _URL = "https://raw.githubusercontent.com/RamziSalah/Classical-Arabic-Named-Entity-Recognition-Corpus/master/CANERCorpus.csv"
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-
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-
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- class Caner(datasets.GeneratorBasedBuilder):
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- """Classical Arabic Named Entity Recognition corpus as a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities"""
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-
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- VERSION = datasets.Version("1.1.0")
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-
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- def _info(self):
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-
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- features = datasets.Features(
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- {
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- "token": datasets.Value("string"),
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- "ner_tag": datasets.ClassLabel(
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- names=[
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- "Allah",
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- "Book",
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- "Clan",
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- "Crime",
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- "Date",
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- "Day",
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- "Hell",
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- "Loc",
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- "Meas",
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- "Mon",
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- "Month",
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- "NatOb",
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- "Number",
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- "O",
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- "Org",
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- "Para",
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- "Pers",
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- "Prophet",
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- "Rlig",
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- "Sect",
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- "Time",
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- ]
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- ),
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- }
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- )
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-
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- return datasets.DatasetInfo(
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- description=_DESCRIPTION,
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- features=features,
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- supervised_keys=None,
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- homepage=_HOMEPAGE,
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- license=_LICENSE,
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- citation=_CITATION,
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- )
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-
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- def _split_generators(self, dl_manager):
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- """Returns SplitGenerators."""
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-
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- data_path = dl_manager.download(_URL)
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-
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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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- "filepath": data_path,
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- },
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- )
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- ]
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-
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- def _generate_examples(self, filepath):
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- """Yields examples."""
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-
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- with open(filepath, encoding="utf-8") as csv_file:
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- reader = csv.reader(csv_file, delimiter=",")
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- next(reader, None)
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-
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- for id_, row in enumerate(reader):
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-
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- yield id_, {
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- "token": row[0],
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- "ner_tag": row[1],
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- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
data/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 sha256:68d710a709cefb398a3b44ac3d0629975fed8ba489747448c499c65baffc3de2
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+ size 1459014
dataset_infos.json DELETED
@@ -1 +0,0 @@
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- {"default": {"description": "Classical Arabic Named Entity Recognition corpus as a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities.\n", "citation": "@article{article,\nauthor = {Salah, Ramzi and Zakaria, Lailatul},\nyear = {2018},\nmonth = {12},\npages = {},\ntitle = {BUILDING THE CLASSICAL ARABIC NAMED ENTITY RECOGNITION CORPUS (CANERCORPUS)},\nvolume = {96},\njournal = {Journal of Theoretical and Applied Information Technology}\n}\n", "homepage": "https://github.com/RamziSalah/Classical-Arabic-Named-Entity-Recognition-Corpus", "license": "", "features": {"token": {"dtype": "string", "id": null, "_type": "Value"}, "ner_tag": {"num_classes": 21, "names": ["Allah", "Book", "Clan", "Crime", "Date", "Day", "Hell", "Loc", "Meas", "Mon", "Month", "NatOb", "Number", "O", "Org", "Para", "Pers", "Prophet", "Rlig", "Sect", "Time"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "caner", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5095721, "num_examples": 258240, "dataset_name": "caner"}}, "download_checksums": {"https://github.com/RamziSalah/Classical-Arabic-Named-Entity-Recognition-Corpus/archive/master.zip": {"num_bytes": 17063406, "checksum": "b4f6bbcc1074dfb9a6cf53fbbd5825a8eafbff842cd89ed20ab33f5b3ef6cddb"}}, "download_size": 17063406, "post_processing_size": null, "dataset_size": 5095721, "size_in_bytes": 22159127}}