Shorten usage

README.md

@@ -102,40 +102,11 @@ Seven of the eight short documents are single plotter sheets 13 to 17 feet long
 
 ## Usage
 
-Load a subset with `datasets`:
+Download the dataset and run a system with the [ExtractBench evaluation framework](https://github.com/run-llama/ExtractBench#extractbench-pro):
 
-```python
-from datasets import load_dataset
-import json
-
-ds = load_dataset("llamaindex/ExtractBenchPro", split="charts_inside_documents")
-case = ds[0]
-schema = json.loads(case["data_schema"])
-expected = json.loads(case["expected_output"])
-```
-
-The rows use the same format as ExtractBench, so the [evaluation framework](https://github.com/run-llama/ExtractBench) scores them unchanged. To rebuild the `<stem>.pdf` + `<stem>.test.json` layout that the framework reads:
-
-```python
-import json, shutil
-from pathlib import Path
-from huggingface_hub import snapshot_download
-
-root = Path(snapshot_download("llamaindex/ExtractBenchPro", repo_type="dataset"))
-out = Path("data/extract_bench_pro")
-for split in ["document_conventions", "house_vocabulary", "house_counting_rules", "drawn_relations", "charts_inside_documents", "long_chart_sheets"]:
-    for line in (root / f"{split}.jsonl").read_text().splitlines():
-        row = json.loads(line)
-        stem = Path(row["pdf"]).stem
-        (out / split).mkdir(parents=True, exist_ok=True)
-        shutil.copyfile(root / row["pdf"], out / split / f"{stem}.pdf")
-        sidecar = {
-            "tags": row["tags"],
-            "data_schema": json.loads(row["data_schema"]),
-            "expected_output": json.loads(row["expected_output"]),
-            "_field_rules": json.loads(row["field_rules"]),
-        }
-        (out / split / f"{stem}.test.json").write_text(json.dumps(sidecar))
+```bash
+uv run extract-bench download --dataset pro
+uv run extract-bench run llamaextract_agentic --dataset pro
 ```
 
 Scoring follows ExtractBench and is fully deterministic: