Define perfect documents as value F1 = 100%; update website link

README.md

@@ -51,7 +51,7 @@ tags:
 
 # ExtractBench Pro
 
-**Quick links:** [\[🌐 Website\]](https://extractbenchpro.ai) [\[💻 Code\]](https://github.com/run-llama/ExtractBench) [\[📚 ExtractBench\]](https://huggingface.co/datasets/llamaindex/ExtractBench)
+**Quick links:** [\[🌐 Website\]](https://www.extractbench.ai/pro/) [\[💻 Code\]](https://github.com/run-llama/ExtractBench) [\[📚 ExtractBench\]](https://huggingface.co/datasets/llamaindex/ExtractBench)
 
 ExtractBench Pro is the hard companion to [ExtractBench](https://huggingface.co/datasets/llamaindex/ExtractBench). The task is the same: given a full document and a user-written schema, return a schema-valid JSON object with the correct, exhaustive values and evidence for each one. The difference is what makes a value hard. In ExtractBench Pro, many values cannot be copied from the page. They have to be read off a chart or a drawing, counted under the document’s own rules, or decoded with its own vocabulary. The documents are derived from real long-tail documents, and grouped into six challenges.
 
@@ -141,7 +141,7 @@ for split in ["document_conventions", "house_vocabulary", "house_counting_rules"
 Scoring follows ExtractBench and is fully deterministic:
 
 - **Unified value F1:** whether the extracted values match the expected output, under one definition for scalar fields and arrays of records.
-- **Near-perfect value F1:** the share of documents with a unified value F1 of at least 99%.
+- **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.