Update README.md

README.md

@@ -166,4 +166,82 @@ configs:
   data_files:
   - split: test
     path: radar-tasks/test-*
+license: cc-by-4.0
+task_categories:
+- table-question-answering
+language:
+- en
+pretty_name: RADAR
+size_categories:
+- 1K<n<10K
 ---
+
+# RADAR: Robust And Data Aware Reasoning Benchmark
+
+## Link: [Paper]() | [Code]()
+
+The **Robust And Data Aware Reasoning (RADAR)** benchmark is designed to evaluate the ability of language models to demonstrate **data-awareness**—that is, to recognize, reason over, and appropriately handle complex data artifacts such as: 
+
+- Missing data  
+- Bad values  
+- Outliers  
+- Inconsistent formatting  
+- Inconsistent multi-column logic  
+
+The full dataset includes **53 tasks** grounded in real-world data tables and varies across data artifact types and table dimensions (by token count and number of columns). In total, RADAR provides **2,980 unique query-table task instances**.
+We also include two subsets of the data: (1) **radar-sizes** (RADAR-S) to focus evaluation on table sizes and (2) **radar-tasks** (RADAR-T) to focus evaluation across all tasks.
+
+## 📊 Dataset Statistics
+
+| **Dataset Split** | **Tasks** | **Instances** | **Tokens (K)** | **Cols** |
+|-------------|-----------|---------------|----------------|----------|
+| RADAR     | 53        | 2,980         | [2,4,8,16]     | [5,10,20] |
+| RADAR-T | 53        | 313           | 8              | 10       |
+| RADAR-S | 10        | 720           | [2,4,8,16]     | [5,10,20] |
+
+
+## 🔭 Dataset Structure
+Each task instance comprises of the follwowing data:
+* `task_id`: a unique id for each source table and query
+* `query`: the query to ask over the data table
+* `answer`: ground truth answer to the query
+* `artifact_type`: the artifact type introduced to the data table for this task
+* `artiact_scope`: does reasoning over the data artifacts involve only a single column, naively or independetly over multiple columns, or jointly or connected over multiple columns
+* `query_cols`: the columns invovled in the query
+* `artifact_reasoning_cols`: the columns invovled in reasoning over the artifacts
+* `table`: the data table for this task (a dictionary with keys "headers" and "rows" to represent the table column names and rows)
+* `num_rows`: number of rows in the tbale
+* `num_cols`: number of columns in the table
+* `recovered_tables_transform_spec`: The right answer is caluclated over the recovered data table(s). We convert the data table in `table` to the recovered data table(s) using this specification indicating which rows to drop and which cells to overwrite.
+* `base_data_num_tokens`: The number of tokens in the data table (before introducing any data artifact perturbations). This may be slightly different after introducing perturbations.
+* `base_data_token_bucket`: The token bucket in which this task belongs to (one of 2000, 4000, 8000, and 16000)
+* `perturbation_note`: Any note about the data artifact perturbation that is introduced.
+
+## 💻 Loading the Data
+Using Hugging Face
+```python
+from datasets import load_dataset
+radar_all = load_dataset("kenqgu/radar", "radar")["test"]
+radar_s = load_dataset("kenqgu/radar", "radar-sizes")["test"]
+radar_t = load_dataset("kenqgu/radar", "radar-tasks")["test"]
+```
+
+Using included RADAR code to load into more usable pydantic objects (need to install radar first).
+```python
+from radar.data import load_task_instances_hf
+
+# load the full dataset
+tasks, task_summary_df = load_task_instances_hf(split="full")
+tasks_s, _ = load_task_instances_hf(split="sizes")
+tasks_t, _ = load_task_instances_hf(split="tasks")
+
+# view the table as a pandas dataframe
+tasks[0].table_df.head()
+```
+
+## 📖 Citation
+
+If you use RADAR in your research, please cite our paper:
+
+```bibtex
+```