Update app.py

app.py

@@ -38,34 +38,11 @@ def predict(text):
 markdown_text = '''
 <br>
 <br>
-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).
+This space provides a gradio demo of a [pretrained model](https://huggingface.co/ltg/ssa-perin) (with an easy-to-run wrapper) for structured sentiment analysis (SSA) of Norwegian text, trained on the [NoReC_fine](https://github.com/ltgoslo/norec_fine) dataset. It implements a method described in the paper [Direct parsing to sentiment graphs](https://aclanthology.org/2022.acl-short.51/) by Samuel et al. 2022.
 
-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:
+The model will attempt to identify the following components for a given sentence it deems to be sentiment-bearing: _source expressions_ (the opinion holder), _target expressions_ (what the opinion is directed towards), _polar expressions_ (the part of the text indicating that an opinion is expressed), and finally the _polarity_ (positive or negative).
 
-| Unlabeled sentiment tuple F1 | Target F1  | Relative polarity precision |
-|:----------------------------:|:----------:|:---------------------------:|
-|     0.434                    |  0.541      |        0.926                |
-
-
-In "Word Substitution with Masked Language Models as Data Augmentation for Sentiment Analysis", we analyzed data augmentation strategies for improving performance of the model. Using masked-language modeling (MLM), we augmented the sentences with MLM-substituted words inside, outside, or inside+outside the actual sentiment tuples. The results below show that augmentation may be improve the model performance. This space, however, runs the original model trained without augmentation.
-
-|                | Augmentation rate | Unlabeled sentiment tuple F1 | Target F1 | Relative polarity precision |
-|----------------|-------------------|------------------------------|-----------|-----------------------------|
-| Baseline       | 0%               | 43.39                        | 54.13     | 92.59                       |
-| Outside        | 59%              | **45.08**                    | 56.18     | 92.95                       |
-| Inside         | 9%               | 43.38                        | 55.62     | 92.49                       |
-| Inside+Outside | 27%              | 44.12                        | **56.44** | **93.19**               |
-
-
-
-The model can be easily used for predicting sentiment tuples as follows: 
+See the code below for an example of how you can use the model yourself for predicting such sentiment tuples (along with character offsets in the text):
 
 ```python
 >>> import model_wrapper
@@ -78,9 +55,9 @@ The model can be easily used for predicting sentiment tuples as follows:
     'Polar_expression': [['liker'], ['3:8']],
     'Polarity': 'Positive'}]}]
 ```
-'''
-
 
+To download the model and find more in-depth documentation, please see (https://huggingface.co/ltg/ssa-perin)[https://huggingface.co/ltg/ssa-perin]
+'''
 
 with gr.Blocks() as demo:
     with gr.Row() as row: