Hugging Face's logo

Datasets:
AAU-NLP
/
effective-performance-measurement

Tasks:
Text Classification
Token Classification
Languages:
English
Tags:
financial-nlp
earnings-calls
sec-filings
kpi-extraction
large-language-models
License:
Dataset card Data Studio Files
xet
 Community
effective-performance-measurement / README.md
rasmus-aau's picture
rasmus-aau
Create README.md
5c76397 verified
preview code
|
Raw
History Blame Contribute Delete
2.73 kB
metadata 
language:
  - en
license: mit
task_categories:
  - information-extraction
  - text-classification
  - token-classification
tags:
  - financial-nlp
  - earnings-calls
  - sec-filings
  - kpi-extraction
  - large-language-models
pretty_name: Effective Performance Measurement (ECB & SECB)

Effective Performance Measurement: KPI Extraction Datasets

This dataset repository accompanies the ACL 2026 (Industry Track) paper: "Effective Performance Measurement: Challenges and Opportunities in KPI Extraction from Earnings Calls".

It contains three novel benchmarks and a prediction set designed to evaluate the extraction of Key Performance Indicators (KPIs) from unstructured financial texts, specifically comparing highly regulated SEC filings to conversational earnings calls.

🔗 Associated GitHub Repository: AAU-NLP/effective-performance-measurement

📊 Dataset Overview

This repository includes four data files, covering data from 20 S&P 500 companies between 2023 and 2024.

1. SEC Filings Benchmark (SECB.json)

  • Description: Context-rich text chunks extracted from SEC filings (10-K, 10-Q). This dataset serves as a baseline to test models trained on highly structured, templated financial data.
  • Size: 40,661 chunks.
  • Annotations: 77,677 regex-labeled entities.

2. Earnings Call Benchmark (ECB.json)

  • Description: Raw, unannotated conversational text chunks extracted from corporate earnings calls. This represents the challenging, unstructured domain shift.
  • Size: 10,477 chunks.

3. Annotated Earnings Call Benchmark (ECB-A.json)

  • Description: An expert-annotated subset of the ECB dataset used for evaluating Large Language Model (LLM) extraction and in-context learning techniques.
  • Size: 587 chunks.
  • Annotations: 2,460 entities and 934 relational groups.

4. Longitudinal KPI Tracking (gold_standard_traceable.jsonl)

  • Description: A dataset containing post-hoc semantic clusterings of KPIs to track emergent metrics across multiple quarters.
  • Size: 1,323 traced entity/KPI rows.

💻 How to Load the Data

You can easily load this data using the Hugging Face datasets library, or by downloading the JSON files directly.

from datasets import load_dataset

# Load the entire repository
dataset = load_dataset("AAU-NLP/effective-performance-measurement")

# Alternatively, download specific JSON files if you just want one benchmark
# e.g., wget [https://huggingface.co/datasets/AAU-NLP/effective-performance-measurement/resolve/main/ECB-A.json](https://huggingface.co/datasets/AAU-NLP/effective-performance-measurement/resolve/main/ECB-A.json)