Updated model card

README.md

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 ## Model Details
 
-We here release a pretrained model (and an easy-to-run wrapper) for structured sentiment analysis of Norwegian text, pre-trained on the [NoReC_fine dataset](https://github.com/ltgoslo/norec_fine).
-This is an implementation of the method described in the paper [Direct parsing to sentiment graphs](https://aclanthology.org/2022.acl-short.51/) by Samuel et al. 2022 which demonstrated how a graph-based semantic parser can be applied to the task of structured sentiment analysis, directly predicting sentiment graphs from text.
+We here release a pretrained model (and an easy-to-run wrapper) for structured sentiment analysis 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 which demonstrated how a graph-based semantic parser can be applied to the task of structured sentiment analysis, directly predicting sentiment graphs from text.
 
 
 ### Model Description
@@ -45,7 +44,7 @@ This is an implementation of the method described in the paper [Direct parsing t
 
 ### Model Sources
 
-- **Paper:** [Direct parsing to sentiment graphs](https://aclanthology.org/2022.acl-short.51/) by D. Samuel, J. Barnes, R. Kurtz, S. Oepen, L. Øvrelid, and E. Velldal, in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, 2022
+- **Paper:** [Direct parsing to sentiment graphs](https://aclanthology.org/2022.acl-short.51/) by Samuel et al. published at ACL 2022
 - **Repository:** The scripts used for training can be found on the [github](https://github.com/jerbarnes/direct_parsing_to_sent_graph) repository accompanying the paper of Samuel et al. (2022) above.
 - **Demo:** To see a demo of how it works, you can try the model in our [Hugging Face Space](https://huggingface.co/spaces/ltg/ssa-perin).
 - **Limitations** The training data is based on professional reviews covering multiple domains, but the model may not necessarily generalize to other text types or domains.