Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Russian
Size:
10K<n<100K
License:
"""Collection3: Russian dataset for named entity recognition""" | |
import os | |
import datasets | |
logger = datasets.logging.get_logger(__name__) | |
_HOMEPAGE = "http://labinform.ru/pub/named_entities/index.htm" | |
_CITATION = """\ | |
@inproceedings{mozharova-loukachevitch-2016-two-stage-russian-ner, | |
author={Mozharova, Valerie and Loukachevitch, Natalia}, | |
booktitle={2016 International FRUCT Conference on Intelligence, Social Media and Web (ISMW FRUCT)}, | |
title={Two-stage approach in Russian named entity recognition}, | |
year={2016}, | |
pages={1-6}, | |
doi={10.1109/FRUCT.2016.7584769}} | |
""" | |
_DESCRIPTION = """\ | |
Collection3 is a Russian dataset for named entity recognition annotated with LOC (location), PER (person), and ORG (organization) tags. | |
Dataset is based on collection Persons-1000 originally containing 1000 news documents labeled only with names of persons. | |
Additional labels were added by Valerie Mozharova and Natalia Loukachevitch. | |
Conversion to the IOB2 format and splitting into train, validation and test sets was done by DeepPavlov team. | |
For more details see https://ieeexplore.ieee.org/document/7584769 and http://labinform.ru/pub/named_entities/index.htm | |
""" | |
_URLS = { | |
"train": os.path.join("data", "train.txt.gz"), | |
"dev": os.path.join("data", "valid.txt.gz"), | |
"test": os.path.join("data", "test.txt.gz"), | |
} | |
class Collection3(datasets.GeneratorBasedBuilder): | |
"""Collection3 dataset.""" | |
VERSION = datasets.Version("1.0.0") | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"id": datasets.Value("string"), | |
"tokens": datasets.Sequence(datasets.Value("string")), | |
"ner_tags": datasets.Sequence( | |
datasets.features.ClassLabel( | |
names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC",] | |
) | |
), | |
} | |
), | |
homepage=_HOMEPAGE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
data_files = dl_manager.download_and_extract(_URLS) | |
return [ | |
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}), | |
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}), | |
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}), | |
] | |
def _generate_examples(self, filepath): | |
logger.info("⏳ Generating examples from = %s", filepath) | |
with open(filepath, encoding="utf-8") as f: | |
guid = 0 | |
tokens = [] | |
ner_tags = [] | |
for line in f: | |
if line.startswith("<DOCSTART>") or line == "" or line == "\n": | |
if tokens: | |
yield guid, { | |
"id": str(guid), | |
"tokens": tokens, | |
"ner_tags": ner_tags, | |
} | |
guid += 1 | |
tokens = [] | |
ner_tags = [] | |
else: | |
splits = line.split("\t") | |
tokens.append(splits[0]) | |
ner_tags.append(splits[1].rstrip()) | |
# last example | |
if tokens: | |
yield guid, { | |
"id": str(guid), | |
"tokens": tokens, | |
"ner_tags": ner_tags, | |
} | |