Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
10K - 100K
License:
init
Browse files- README.md +107 -0
- dataset/label.json +1 -0
- dataset/test.json +0 -0
- dataset/train00.json +0 -0
- dataset/train01.json +0 -0
- dataset/train02.json +0 -0
- dataset/train03.json +0 -0
- dataset/valid.json +0 -0
- ontonotes5.py +84 -0
README.md
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---
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language:
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- en
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license:
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- other
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multilinguality:
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- monolingual
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size_categories:
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- 10K<n<100K
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task_categories:
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- token-classification
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task_ids:
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- named-entity-recognition
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pretty_name: Ontonotes5
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---
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# Dataset Card for "tner/ontonotes5"
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## Dataset Description
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- **Repository:** [T-NER](https://github.com/asahi417/tner)
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- **Paper:** [https://aclanthology.org/N06-2015/](https://aclanthology.org/N06-2015/)
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### Dataset Summary
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Ontonotes5 NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
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## Dataset Structure
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### Data Instances
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An example of `train` looks as follows.
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```
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{
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'tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0, 0, 0, 11, 12, 12, 12, 12, 0, 0, 7, 0, 0, 0, 0, 0],
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'tokens': ['``', 'It', "'s", 'very', 'costly', 'and', 'time', '-', 'consuming', ',', "''", 'says', 'Phil', 'Rosen', ',', 'a', 'partner', 'in', 'Fleet', '&', 'Leasing', 'Management', 'Inc.', ',', 'a', 'Boston', 'car', '-', 'leasing', 'company', '.']
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}
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```
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### Label ID
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The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/onotonotes5/raw/main/dataset/label.json).
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```python
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{
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"O": 0,
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"B-CARDINAL": 1,
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"B-DATE": 2,
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"I-DATE": 3,
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"B-PERSON": 4,
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"I-PERSON": 5,
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"B-NORP": 6,
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"B-GPE": 7,
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"I-GPE": 8,
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"B-LAW": 9,
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"I-LAW": 10,
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"B-ORG": 11,
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"I-ORG": 12,
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"B-PERCENT": 13,
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"I-PERCENT": 14,
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"B-ORDINAL": 15,
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"B-MONEY": 16,
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"I-MONEY": 17,
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"B-WORK_OF_ART": 18,
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"I-WORK_OF_ART": 19,
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"B-FAC": 20,
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"B-TIME": 21,
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"I-CARDINAL": 22,
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"B-LOC": 23,
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"B-QUANTITY": 24,
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"I-QUANTITY": 25,
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"I-NORP": 26,
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"I-LOC": 27,
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"B-PRODUCT": 28,
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"I-TIME": 29,
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"B-EVENT": 30,
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"I-EVENT": 31,
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"I-FAC": 32,
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"B-LANGUAGE": 33,
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"I-PRODUCT": 34,
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"I-ORDINAL": 35,
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"I-LANGUAGE": 36
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}
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```
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### Data Splits
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| name |train|validation|test|
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|---------|----:|---------:|---:|
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|conll2003|14041| 3250|3453|
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### Citation Information
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```
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@inproceedings{hovy-etal-2006-ontonotes,
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title = "{O}nto{N}otes: The 90{\%} Solution",
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author = "Hovy, Eduard and
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Marcus, Mitchell and
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Palmer, Martha and
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Ramshaw, Lance and
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Weischedel, Ralph",
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booktitle = "Proceedings of the Human Language Technology Conference of the {NAACL}, Companion Volume: Short Papers",
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month = jun,
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year = "2006",
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address = "New York City, USA",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/N06-2015",
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pages = "57--60",
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}
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```
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dataset/label.json
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{"O": 0, "B-CARDINAL": 1, "B-DATE": 2, "I-DATE": 3, "B-PERSON": 4, "I-PERSON": 5, "B-NORP": 6, "B-GPE": 7, "I-GPE": 8, "B-LAW": 9, "I-LAW": 10, "B-ORG": 11, "I-ORG": 12, "B-PERCENT": 13, "I-PERCENT": 14, "B-ORDINAL": 15, "B-MONEY": 16, "I-MONEY": 17, "B-WORK_OF_ART": 18, "I-WORK_OF_ART": 19, "B-FAC": 20, "B-TIME": 21, "I-CARDINAL": 22, "B-LOC": 23, "B-QUANTITY": 24, "I-QUANTITY": 25, "I-NORP": 26, "I-LOC": 27, "B-PRODUCT": 28, "I-TIME": 29, "B-EVENT": 30, "I-EVENT": 31, "I-FAC": 32, "B-LANGUAGE": 33, "I-PRODUCT": 34, "I-ORDINAL": 35, "I-LANGUAGE": 36}
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dataset/test.json
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dataset/train00.json
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dataset/train01.json
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dataset/train02.json
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dataset/train03.json
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dataset/valid.json
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ontonotes5.py
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""" NER dataset compiled by T-NER library https://github.com/asahi417/tner/tree/master/tner """
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import json
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from itertools import chain
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import datasets
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logger = datasets.logging.get_logger(__name__)
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_DESCRIPTION = """[ontonotes5 NER dataset](https://aclanthology.org/N06-2015/)"""
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_NAME = "ontonotes5"
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_VERSION = "1.0.0"
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_CITATION = """
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@inproceedings{hovy-etal-2006-ontonotes,
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title = "{O}nto{N}otes: The 90{\%} Solution",
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author = "Hovy, Eduard and
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Marcus, Mitchell and
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Palmer, Martha and
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Ramshaw, Lance and
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Weischedel, Ralph",
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booktitle = "Proceedings of the Human Language Technology Conference of the {NAACL}, Companion Volume: Short Papers",
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month = jun,
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year = "2006",
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address = "New York City, USA",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/N06-2015",
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pages = "57--60",
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}
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"""
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_HOME_PAGE = "https://github.com/asahi417/tner"
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_URL = f'https://huggingface.co/datasets/tner/{_NAME}/raw/main/dataset'
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_URLS = {
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str(datasets.Split.TEST): [f'{_URL}/test.json'],
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str(datasets.Split.TRAIN): [f'{_URL}/train{i:02d}.json' for i in range(4)],
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str(datasets.Split.VALIDATION): [f'{_URL}/valid.json'],
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}
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class Ontonotes5Config(datasets.BuilderConfig):
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"""BuilderConfig"""
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def __init__(self, **kwargs):
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"""BuilderConfig.
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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super(Ontonotes5Config, self).__init__(**kwargs)
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class Ontonotes5(datasets.GeneratorBasedBuilder):
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"""Dataset."""
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BUILDER_CONFIGS = [
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Ontonotes5Config(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
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]
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def _split_generators(self, dl_manager):
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downloaded_file = dl_manager.download_and_extract(_URLS)
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return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
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for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]
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def _generate_examples(self, filepaths):
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_key = 0
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for filepath in filepaths:
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logger.info(f"generating examples from = {filepath}")
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with open(filepath, encoding="utf-8") as f:
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_list = [i for i in f.read().split('\n') if len(i) > 0]
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for i in _list:
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data = json.loads(i)
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yield _key, data
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_key += 1
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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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"tokens": datasets.Sequence(datasets.Value("string")),
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"tags": datasets.Sequence(datasets.Value("int")),
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}
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),
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supervised_keys=None,
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homepage=_HOME_PAGE,
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citation=_CITATION,
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)
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