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Datasets:
tner
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fin

Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
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1K - 10K
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tokens
sequence
tags
sequence
[ "(", "l", ")", "\"", "Tranche", "A", "Shares", "\"", "has", "the", "meaning", "as", "defined", "in", "the", "Subscription", "Agreement", "." ]
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[ "(", "m", ")", "\"", "Tranche", "B", "Cash", "Payment", "\"", "has", "the", "meaning", "as", "defined", "in", "the", "Subscription", "Agreement", "." ]
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[ "(", "n", ")", "\"", "USD", "\"", "means", "US", "dollars", ",", "the", "lawful", "currency", "of", "the", "United", "State", "of", "America", "." ]
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[ "1", ".", "2", "Other", "Terms", "Other", "terms", "may", "be", "defined", "elsewhere", "in", "the", "text", "of", "this", "Agreement", "and", ",", "unless", "otherwise", "indicated", ",", "shall", "have", "such", "meaning", "indicated", "throughout", "this", "Agreement", "." ]
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[ "1", ".", "3", "Headings", "Headings", "(", "including", "those", "in", "brackets", "at", "the", "beginning", "of", "paragraphs", ")", "are", "for", "convenience", "only", "and", "do", "not", "affect", "the", "interpretation", "of", "this", "Agreement", "." ]
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[ "Article", "2", "Loan" ]
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[ "2", ".", "1", "Loan", "Subject", "to", "Article", "3", "of", "this", "Agreement", ",", "the", "Company", "hereby", "undertakes", "to", "grant", "to", "the", "Borrower", ",", "and", "the", "Borrower", "agrees", "to", "borrow", "from", "the", "Company", ",", "a", "loan", "in", "an", "aggregate", "amount", "of", "no", "more", "than", "the", "USD", "equivalent", "to", "RMB", "20", ",", "000", ",", "000", "(", "the", "\"", "Loan", "\"),", "in", "which", "the", "USD", "equivalent", "to", "RMB", "14", ",", "613", ",", "000", "shall", "be", "granted", "to", "Airland", "and", "the", "USD", "equivalent", "to", "RMB", "5", ",", "387", ",", "000", "shall", "be", "granted", "to", "Bizexpress", "." ]
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[ "2", ".", "2", "Grant", "of", "the", "Loan" ]
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[ "(", "a", ")", "The", "Borrower", "shall", "inform", "the", "Company", "in", "writing", "of", "account", "information", "on", "the", "date", "preceding", "the", "Funding", "Date", "." ]
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[ "The", "Company", "shall", "remit", "the", "Loan", "to", "the", "Account", "if", "it", "receives", "the", "written", "account", "information", "." ]
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[ "(", "b", ")", "On", "the", "Funding", "Date", ",", "the", "Borrower", "shall", "issue", "a", "receipt", "to", "the", "Company", "evidencing", "that", "it", "has", "fully", "received", "the", "Loan", "." ]
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[ "(", "c", ")", "The", "Loan", "shall", "be", "granted", "to", "the", "Borrower", "in", "USD", "." ]
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[ "The", "exchange", "rate", "between", "USD", "and", "RMB", "shall", "be", "the", "exchange", "rate", "published", "by", "the", "People", "'", "s", "Bank", "of", "China", "on", "the", "date", "preceding", "the", "Funding", "Date", "." ]
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[ "2", ".", "3", "Loan", "Interest", "The", "interest", "of", "the", "Loan", "(", "the", "\"", "Interest", "\")", "shall", "accrue", "on", "the", "outstanding", "balance", "of", "the", "Loan", "commencing", "from", "the", "Funding", "Date", "at", "an", "annual", "rate", "of", "5", "%", "calculated", "on", "a", "basis", "of", "a", "year", "of", "three", "hundred", "sixty", "five", "(", "365", ")", "days", "." ]
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[ "(", "a", ")", "First", "Repayment" ]
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Dataset Card for "tner/fin"

Dataset Summary

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.

  • Entity Types: ORG, LOC, PER, MISC

Dataset Structure

Data Instances

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", ";"]
}

Label ID

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
}

Data Splits

name train validation test
fin 1014 303 150

Citation Information 

@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",
}
Downloads last month
162
Repository:
T-NER
Paper:
aclanthology.org
Dataset:
FIN
Domain:
Financial News
Number of Entity:
4
Size of downloaded dataset files:
652 kB
Size of the auto-converted Parquet files:
134 kB
Number of rows:
1,473

Models trained or fine-tuned on tner/fin