Match usage to ExtractBench

README.md

@@ -102,21 +102,35 @@ Seven of the eight short documents are single plotter sheets 13 to 17 feet long
 
 ## Usage
 
-Download the dataset and run a system with the [ExtractBench evaluation framework](https://github.com/run-llama/ExtractBench#extractbench-pro):
+Load a split with `datasets`:
+
+```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"])
+```
+
+To run an extraction system, download the files in the layout expected by the [evaluation framework](https://github.com/run-llama/ExtractBench), then run a pipeline:
 
 ```bash
-uv run extract-bench download --dataset pro
-uv run extract-bench run llamaextract_agentic --dataset pro
+uv run extract-bench download --dataset pro                    # data-pro/<split>/<doc>.pdf + <doc>.test.json
+uv run extract-bench run llamaextract_agentic --dataset pro    # inference -> evaluation -> reports
 ```
 
-Scoring follows ExtractBench and is fully deterministic:
+ExtractBench Pro is scored the same way as ExtractBench. Value accuracy asks whether a system returned the right values, and is scored with unified value F1 on every document. Grounding asks whether the system can show where each value came from, and is scored only on the 81 documents with box ground truth.
 
 - **Unified value F1:** whether the extracted values match the expected output, under one definition for scalar fields and arrays of records.
 - **Perfect documents:** the share of documents with a unified value F1 of 100%.
 - **Word-level grounding F1:** a field counts as grounded only when its value is correct and its predicted box overlaps an accepted box for that field at IoU 0.5.
 - **Page-level grounding F1:** requires the correct source page rather than a box.
 
-The evaluation dataset files are:
+Scoring is fully deterministic: the same predictions and ground truth always produce the same score, with no model in the loop. The [code repository](https://github.com/run-llama/ExtractBench) contains the full metric definitions.
+
+The evaluation dataset files include:
 
 - [document_conventions.jsonl](document_conventions.jsonl): 22 test cases across 875 pages
 - [house_vocabulary.jsonl](house_vocabulary.jsonl): 14 test cases across 606 pages