Create README.md

README.md

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+---
+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](https://github.com/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.
+
+```python
+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)
+```