Update README.md

README.md

@@ -6,7 +6,7 @@ This code and approach was written and tested on a Hungarian media sentiment cor
 Instead of fine-tuning a BERT model, we extract contextual embeddings from the hidden layers and use those as classical inputs for ML approaches.
 
 ## Results
-The code was benchmarked against a fine-tuned XLM-Roberta on the same corpus, and reached the following topline results (Roberta result in brackets): 8-way sentiment classification weighted F1: 0.65 [0.73], with a range of category-level F1s of 0.35-0.72 [0.51-0.79]; 3-way classification weighted F1: 0.77 [0.82], 0.58-0.82 [0.51-0.87]. The code was run in a Google Colab GPU-supported free notebook.
+The approach was benchmarked against embeddings from a non fine-tuned XLM-Roberta, Hilbert, and fine-tuned XLM-Roberta on the same corpus, and reached the following topline results (Roberta result in brackets): 8-way sentiment classification weighted F1: 0.65 [0.73], with a range of category-level F1s of 0.35-0.72 [0.51-0.79]; 3-way classification weighted F1: 0.77 [0.82], 0.58-0.82 [0.51-0.87]. The code was run in a Google Colab GPU-supported free notebook.
 
 ![image](https://user-images.githubusercontent.com/23291101/140734165-1ef1e008-b3f9-4b6d-ba19-0454ecf8d510.png)