Update model info in README

app.py

@@ -41,7 +41,13 @@ markdown_text = '''
 This space provides a gradio demo and an easy-to-run wrapper of the pre-trained model for structured sentiment analysis in Norwegian language, pre-trained on the [NoReC dataset](https://huggingface.co/datasets/norec).
 This space containt an implementation of method described in "Direct parsing to sentiment graphs" (Samuel _et al._, ACL 2022). The main repository that also contains the scripts for training the model, can be found on the project [github](https://github.com/jerbarnes/direct_parsing_to_sent_graph).
 
-The proposed method suggests three different ways to encode the sentiment graph: "node-centric", "labeled-edge", and "opinion-tuple". The current model uses the "labeled-edge" graph encoding, and achieves the following results on the held-out set of the NoReC dataset:
+The sentiment graph model is based on an underlying masked language model – [NorBERT 2](https://huggingface.co/ltg/norbert2).
+The proposed method suggests three different ways to encode the sentiment graph: "node-centric", "labeled-edge", and "opinion-tuple".
+The current model
+- uses "labeled-edge" graph encoding
+- does not use character-level embedding
+- all other hyperparameters are set to [default values](https://github.com/jerbarnes/direct_parsing_to_sent_graph/blob/main/perin/config/edge_norec.yaml)
+, and it achieves the following results on the held-out set of the NoReC dataset:
 
 | Unlabeled sentiment tuple F1 | Target F1  | Relative polarity precision |
 |:----------------------------:|:----------:|:---------------------------:|