FinNER

Overview

The FinNER dataset is a corpus of financial agreements from public U.S. Security and Exchange Commission (SEC) filings, annotated with Person, Organization, Location, and Miscellaneous entities to support information extraction for credit risk assessment.

Task Description

• Task Type: Financial Named Entity Recognition (NER)

• Input: Financial agreement text from SEC filings

• Output: Identified entity spans with types

• Domain: Finance, legal documents, credit risk

Key Features

• Financial agreements from SEC filings

• Annotated for credit risk assessment applications

• Standard NER entity types adapted for finance

• Specialized for financial document processing

• Useful for legal and financial AI applications

Evaluation Notes

• Default configuration uses 5-shot evaluation

• Metrics: Precision, Recall, F1-Score, Accuracy

• Entity types: PER, ORG, LOC, MISC

Properties

Property	Value
Benchmark Name	fin_ner
Dataset ID	extraordinarylab/fin-ner
Paper	N/A
Tags	Knowledge, NER
Metrics	precision, recall, f1_score, accuracy
Default Shots	5-shot
Evaluation Split	test
Train Split	train

Data Statistics

Metric	Value
Total Samples	305
Prompt Length (Mean)	2891.13 chars
Prompt Length (Min/Max)	2663 / 6149 chars

Sample Example

Subset: default

{
  "input": [
    {
      "id": "d8a8baf8",
      "content": "Here are some examples of named entity recognition:\n\nInput:\n( l ) \" Tranche A Shares \" has the meaning as defined in the Subscription Agreement .\n\nOutput:\n<response>( l ) \" Tranche A Shares \" has the meaning as defined in the Subscription Agr ... [TRUNCATED] ... osing tag.\n\nText to process:\nSubordinated Loan Agreement - Silicium de Provence SAS and Evergreen Solar Inc . 7 - December 2007 [ HERBERT SMITH LOGO ] ................................ 2007 SILICIUM DE PROVENCE SAS and EVERGREEN SOLAR , INC .\n"
    }
  ],
  "target": "<response>Subordinated Loan Agreement - <organization>Silicium de Provence SAS</organization> and <organization>Evergreen Solar Inc</organization> . 7 - December 2007 [ <person>HERBERT SMITH</person> LOGO ] ................................ 2007 <organization>SILICIUM DE PROVENCE SAS</organization> and <organization>EVERGREEN SOLAR</organization> , INC .</response>",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "tokens": [
      "Subordinated",
      "Loan",
      "Agreement",
      "-",
      "Silicium",
      "de",
      "Provence",
      "SAS",
      "and",
      "Evergreen",
      "Solar",
      "Inc",
      ".",
      "7",
      "-",
      "December",
      "2007",
      "[",
      "HERBERT",
      "SMITH",
      "LOGO",
      "]",
      "................................",
      "2007",
      "SILICIUM",
      "DE",
      "PROVENCE",
      "SAS",
      "and",
      "EVERGREEN",
      "SOLAR",
      ",",
      "INC",
      "."
    ],
    "ner_tags": [
      "O",
      "O",
      "O",
      "O",
      "B-ORG",
      "I-ORG",
      "I-ORG",
      "I-ORG",
      "O",
      "B-ORG",
      "I-ORG",
      "I-ORG",
      "O",
      "O",
      "O",
      "O",
      "O",
      "O",
      "B-PER",
      "I-PER",
      "O",
      "O",
      "O",
      "O",
      "B-ORG",
      "I-ORG",
      "I-ORG",
      "I-ORG",
      "O",
      "B-ORG",
      "I-ORG",
      "O",
      "O",
      "O"
    ]
  }
}

Note: Some content was truncated for display.

Prompt Template

Prompt Template:

You are a named entity recognition system that identifies the following entity types:
{entities}

Process the provided text and mark all named entities with XML-style tags.

For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.

Available entity tags: {entity_list}

INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.

Text to process:
{text}

Few-shot Template

Here are some examples of named entity recognition:

{fewshot}

You are a named entity recognition system that identifies the following entity types:
{entities}

Process the provided text and mark all named entities with XML-style tags.

For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.

Available entity tags: {entity_list}

INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.

Text to process:
{text}

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets fin_ner \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['fin_ner'],
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)