tner/deberta-v3-large-fin
Token Classification
•
Updated
•
23
•
2
tokens
sequence | tags
sequence |
---|---|
[
"(",
"l",
")",
"\"",
"Tranche",
"A",
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[
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[
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[
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[
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[
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"the",
"\"",
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[
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"bankruptcy",
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"general",
"applicability",
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"affecting",
"creditors",
"'",
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"generally",
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"remedies",
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[
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[
"3"
] | [
0
] |
FIN NER dataset formatted in a part of TNER project. FIN dataset contains training (FIN5) and test (FIN3) only, so we randomly sample a half size of test instances from the training set to create validation set.
ORG
, LOC
, PER
, MISC
An example of train
looks as follows.
{
"tags": [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"tokens": ["1", ".", "1", ".", "4", "Borrower", "engages", "in", "criminal", "conduct", "or", "is", "involved", "in", "criminal", "activities", ";"]
}
The label2id dictionary can be found at here.
{
"O": 0,
"B-PER": 1,
"B-LOC": 2,
"B-ORG": 3,
"B-MISC": 4,
"I-PER": 5,
"I-LOC": 6,
"I-ORG": 7,
"I-MISC": 8
}
name | train | validation | test |
---|---|---|---|
fin | 1014 | 303 | 150 |
@inproceedings{salinas-alvarado-etal-2015-domain,
title = "Domain Adaption of Named Entity Recognition to Support Credit Risk Assessment",
author = "Salinas Alvarado, Julio Cesar and
Verspoor, Karin and
Baldwin, Timothy",
booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
month = dec,
year = "2015",
address = "Parramatta, Australia",
url = "https://aclanthology.org/U15-1010",
pages = "84--90",
}