Add dataset card

README.md

@@ -1,3 +1,83 @@
 ---
-license: cc-by-4.0
+pretty_name: OpenExempt
+language:
+- en
+license:
+- cc-by-4.0
+task_categories:
+- question-answering
+- text-generation
+tags:
+- legal
+- law
+- bankruptcy
+- reasoning
+source_datasets:
+- original
+multilinguality:
+- monolingual
+dataset_info:
+  splits:
+  - name: test
+    num_examples: 9300
+  - name: validation
+    num_examples: 465
+  features:
+  - name: id
+    dtype: string
+  - name: prompt
+    dtype: string
+  - name: solution
+    dtype: string
+  - name: config
+    dtype: string
+  - name: case
+    dtype: string
 ---
+
+# Dataset Card for OpenExempt
+OpenExempt: A Diagnostic Benchmark for Legal Reasoning and a Framework for Creating Custom Benchmarks on Demand.
+
+- **Paper:**
+- **Repository: https://github.com/servantez/OpenExempt**
+
+- **License: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)**
+
+
+## OpenExempt Overview
+OpenExempt is a framework and benchmark for diagnostic evaluation of legal reasoning capabilities in language models. The [OpenExempt Framework](https://github.com/servantez/OpenExempt) is capable of creating complex legal reasoning tasks on demand, where each task scenario is dynamically shaped by the user through configuration settings. OpenExempt computes gold solutions for each task using expert-crafted symbolic representations of relevant U.S. federal and state statutes. Using this framework, we construct the OpenExempt Benchmark, a diagnostic benchmark with 9,765 samples across nine evaluation suites, designed to carefully probe model capabilities through controlled task variation. 
+
+## Dataset Summary
+The OpenExempt Benchmark provides diagnostic evaluation of legal reasoning in language models.
+
+### Languages
+All OpenExempt tasks are in English.
+
+### Dataset Structure
+OpenExempt is organized into 9 evaluation suites (3 competency suites and 6 diagnostic suites):
+
+**Competency Suites**. These suites evaluate core legal reasoning abilities at increasing levels of difficulty:
+
+- `basic_competency`: 1,050 samples (1,000 test, 50 validation)  
+- `intermediate_competency`: 1,470 samples (1,400 test, 70 validation)  
+- `advanced_competency`: 1,470 samples (1,400 test, 70 validation)  
+
+**Diagnostic Suites**. These suites are designed to probe specific dimensions of reasoning, robustness, and error propagation:
+
+- `temporal_reasoning`: 525 samples (500 test, 25 validation)  
+- `reasoning_decomposition`: 1,470 samples (1,400 test, 70 validation)  
+- `asset_scaling`: 1,680 samples (1,600 test, 80 validation)  
+- `distractor_robustness`: 525 samples (500 test, 25 validation)  
+- `sycophancy_robustness`: 525 samples (500 test, 25 validation)
+- `obfuscation_robustness`: 525 samples (500 test, 25 validation)
+
+The `baseline_robustness` suite contains tasks without obfuscating statements and serves as a direct point of comparison against robustness suites.
+
+### Data Fields
+OpenExempt examples contain the following data fields:
+
+- `id`: A unique identifier for the task instance.  
+- `prompt`: The natural-language task prompt presented to the model, including the factual scenario, instructions, and relevant statutes.  
+- `solution`: The gold solution for the task, expressed as a string (often containing structured content).  
+- `config`: The configuration parameters used to construct the example, expressed as a string.  
+- `case`: The case details for the example, expressed as a string.