Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
Polish
Size:
10K - 100K
License:
Commit
•
3318f30
1
Parent(s):
ffbcf8b
Convert dataset to Parquet (#10)
Browse files- Convert dataset to Parquet (22765328d0eeb93f6d49b560b38b24e1e030fdda)
- Delete loading script (efacde1b81bc3b87a0d8943b0b19ee32b43616be)
- README.md +14 -5
- data/test-00000-of-00001.parquet +3 -0
- data/train-00000-of-00001.parquet +3 -0
- data/validation-00000-of-00001.parquet +3 -0
- nkjp-ner.py +0 -107
README.md
CHANGED
@@ -34,16 +34,25 @@ dataset_info:
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'5': time
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splits:
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- name: train
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num_bytes:
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num_examples: 15794
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- name: test
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num_bytes:
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num_examples: 2058
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- name: validation
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num_bytes:
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num_examples: 1941
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download_size:
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dataset_size:
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---
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# Dataset Card for NJKP NER
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'5': time
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splits:
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- name: train
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num_bytes: 1612117
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num_examples: 15794
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- name: test
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num_bytes: 221088
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num_examples: 2058
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- name: validation
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num_bytes: 196648
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num_examples: 1941
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download_size: 1447759
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dataset_size: 2029853
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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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- split: test
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path: data/test-*
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- split: validation
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path: data/validation-*
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---
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# Dataset Card for NJKP NER
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data/test-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:9a18ea62a046e214c3280d3051fa775525ea47b4978240aecf43370b13a8e116
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size 157416
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data/train-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:44e6efd683033697df43b4295e1610e892c80a6141ed0e57cac41514dfb273d0
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size 1150625
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data/validation-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:6d3626a7be86969974c3991dc349a5f5534b93171ec03f4c6a7d12e1c12f1a45
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+
size 139718
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nkjp-ner.py
DELETED
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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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"""NKJP-NER"""
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import csv
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import os
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import datasets
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from datasets.tasks import TextClassification
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_CITATION = """\
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@book{przepiorkowski2012narodowy,
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title={Narodowy korpus jezyka polskiego},
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author={Przepi{\'o}rkowski, Adam},
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year={2012},
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publisher={Naukowe PWN}
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}
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"""
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_DESCRIPTION = """\
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The NKJP-NER is based on a human-annotated part of National Corpus of Polish (NKJP). We extracted sentences with named entities of exactly one type. The task is to predict the type of the named entity.
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"""
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_HOMEPAGE = "https://klejbenchmark.com/tasks/"
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_LICENSE = "GNU GPL v.3"
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_URLs = "https://klejbenchmark.com/static/data/klej_nkjp-ner.zip"
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class NkjpNer(datasets.GeneratorBasedBuilder):
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"""NKJP-NER"""
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VERSION = datasets.Version("1.1.0")
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"sentence": datasets.Value("string"),
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"target": datasets.ClassLabel(
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names=[
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"geogName",
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"noEntity",
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"orgName",
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"persName",
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"placeName",
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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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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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task_templates=[TextClassification(text_column="sentence", label_column="target")],
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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data_dir = dl_manager.download_and_extract(_URLs)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"filepath": os.path.join(data_dir, "train.tsv"),
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"split": "train",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={"filepath": os.path.join(data_dir, "test_features.tsv"), "split": "test"},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"filepath": os.path.join(data_dir, "dev.tsv"),
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"split": "dev",
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},
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),
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]
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def _generate_examples(self, filepath, split):
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"""Yields examples."""
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with open(filepath, encoding="utf-8") as f:
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reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
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for id_, row in enumerate(reader):
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yield id_, {
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"sentence": row["sentence"],
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"target": -1 if split == "test" else row["target"],
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}
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