update readme

README.md

@@ -1,3 +1,81 @@
 ---
 license: mit
+dataset_info:
+  features:
+  - name: instance_id
+    dtype: string
+  - name: doc_type
+    dtype: string
+  - name: source
+    dtype: string
+  - name: url
+    dtype: string
+  - name: edu_pred_input
+    dtype: string
+  - name: ground_truth
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 32221618
+    num_examples: 248
+  download_size: 6102151
+  dataset_size: 32221618
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: test/data.parquet
 ---
+
+# Dataset Card for StructBench
+
+## Dataset Summary
+
+StructBench is a benchmark for evaluating fine-grained document structure analysis. It provides a high-quality test set of 248 documents in diverse formats, including 203 Web pages and 47 PDFs.
+
+To ensure reliable ground truth, all documents were:
+
+- Parsed and sentence-segmented
+
+- Manually annotated by human experts for discourse structure
+
+In addition to the structured annotations, raw Web pages and PDF files are included.
+
+## Dataset Structure
+
+- **test/**: evaluation-only split
+  - **data.parquet**: data samples
+  - **raw_pdf_files/**: original PDF files
+  - **raw_web_htmls/**: original WEB htmls
+
+## Tasks
+
+- Discouse Analysis
+- Document Structure Parsing
+- Document Understanding
+
+## Usage
+
+Clone the dataset:
+
+```bash
+git clone https://huggingface.co/datasets/deeplang-ai/StructBench
+```
+
+Or load with Hugging Face datasets library:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("deeplang-ai/StructBench", split="test")
+```
+
+## Citation
+
+```bibtex
+@misc{deeplangai_structbench2025,
+  title={StructBench},
+  author={DeeplangAI},
+  year={2025},
+  howpublished={\url{https://huggingface.co/datasets/deeplang-ai/StructBench}}
+}
+```