Update model card

README.md

@@ -11,7 +11,7 @@ model-index:
   - name: SSA-Perin
     results:
       - task:
-          type: structured sentiment analysis
+          type: token-classification
         dataset:
           name: NoReC
           type: NoReC
@@ -29,8 +29,19 @@ model-index:
 
 
 
-This repository contains a pretrained model (and an easy-to-run wrapper for it) for structured sentiment analysis in Norwegian language, pre-trained on the [NoReC dataset](https://huggingface.co/datasets/norec).
-This is an implementation of the 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 repository contains a pretrained model (and an easy-to-run wrapper for it) for structured sentiment analysis in Norwegian language, pre-trained on the [NoReC_fine dataset](https://github.com/ltgoslo/norec_fine).
+This is an implementation of the method described in
+```bibtex
+@misc{samuel2022direct,
+      title={Direct parsing to sentiment graphs},
+      author={David Samuel and Jeremy Barnes and Robin Kurtz and Stephan Oepen and Lilja Øvrelid and Erik Velldal},
+      year={2022},
+      eprint={2203.13209},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+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 model is also available in the form of a [HF space](https://huggingface.co/spaces/ltg/ssa-perin).
 
 
@@ -40,24 +51,13 @@ 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:
+, and it achieves the following results on the held-out set of the dataset:
 
 | 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: 
 
 ```python