Update README.md

README.md

@@ -22,13 +22,15 @@ On the other hand, there is a significant lack of research focused on extracting
 
 LayoutLM [1] is an excellent solution for the problems because, at its core, it is a regular BERT-alike model, but it is uniquely capable of embedding positional information about the text alongside the text itself.
 
-We have fine-tuned the model on the DocVQA [2] dataset, far surpassing the current SOTA (all-mpnet-base-v2) [3]:
-
-| Model | HR@3 | HR@5 | HR@10 |
-|-------|------|------|-------|
-| all-mpnet-base-v2 (Baseline) | 0.2505 | 0.2941 | 0.3624 |
-| LayoutLM (Our Model) | 0.3159 | 0.3909 | 0.5019 |
-| Relative Improvement | +26.1% | +32.9% | +38.5% |
+We have fine-tuned the model on the DocVQA [2] dataset, showing the potential improvement upon the current SOTA [4]:
+
+| Model                           | HR@3           | HR@5           | HR@10          |
+|---------------------------------|----------------|----------------|----------------|
+| all-mpnet-base-v2               | 0.2500         | 0.2900         | 0.3600         |
+| gte-base-en-v1.5                | 0.3454         | 0.3899         | 0.4554         |
+| snowflake-arctic-embed-m-v1.5   | **0.3548**     | 0.4042         | 0.4573         |
+| LayoutLM-Byne (our model)       | 0.3491         | **0.4269**     | **0.5436**     |
+| Improvement over best competitor| -1.61%         | +5.62%         | +18.87%        |
 
 ### Usage
 Please refer to the Colab workbook or the blog post to learn more!