Update README.md

README.md

@@ -26,6 +26,7 @@ tags:
 ### Model Overview
 TabPFN-3 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. 
 Inference code can be found at [https://github.com/PriorLabs/tabPFN](https://github.com/PriorLabs/tabPFN).
+More details can be found in the [Model Report](https://storage.googleapis.com/prior-labs-tabpfn-public/reports/TabPFN_3_model_report.pdf).
 
 ### Getting started
 First, install the inference package:
@@ -70,7 +71,7 @@ TabPFN-3 is trained purely on synthetic tabular tasks.
 
 ### Performance Benchmarks
 Evaluated on public benchmarks such as TabArena and TALENT, the model yields SOTA results.
-On proprietary benchmark collections, it yields SOTA results for <100k data points and for >100k data points and <200 features.
+On proprietary benchmark collections, it yields SOTA results for datasets with <100k rows and datasets with 100k-1M rows and <200 features.
 
 
 ### Ethical Considerations