Updated model card

README.md

@@ -27,18 +27,36 @@ model-index:
             value: 93.19%
 ---
 
+# Model Card for SSA-PERIN for Norwegian
 
 
+## 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.
+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.
+
+
+### Model Description
+
+- **Developed by:** The [SANT](https://www.mn.uio.no/ifi/english/research/projects/sant/) project (Sentiment Analysis for Norwegian Text) at [the Language Technology Group](https://www.mn.uio.no/ifi/english/research/groups/ltg/) (LTG) at the University of Oslo.
+- **Funded by:** [SANT](https://www.mn.uio.no/ifi/english/research/projects/sant/) is funded by the Research Council of Norway
+- **Language(s):** Norwegian (Bokmål/Nynorsk)
+- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+### 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
+- **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. 
 
-To see a demo of how it works, you can try the model in a [Hugging Face Space](https://huggingface.co/spaces/ltg/ssa-perin).
 
-## Example usage
+## How to Get Started with the Model
 
-The model attempts to identify the following components for a given sentence: source expressions (the opinion holder), target expressions (what the opinion is directed towards), polar expressions (the part of the text indicating that an opinion expressed), and finally the polarity (positive or negative). For more information about the definition of these concepts, please the paper [A Fine-grained Sentiment Dataset for Norwegian](https://aclanthology.org/2020.lrec-1.618/) by Øvrelid et al. 2020. For each identified expression, the character offsets in the text are also provided.
 
-Here is an eaxmple showing to use  the model for predicting such sentiment tuples: 
+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). For more information about how these categories are defined in the training data, please the paper [A Fine-grained Sentiment Dataset for Norwegian](https://aclanthology.org/2020.lrec-1.618/) by Øvrelid et al. 2020. For each identified expression, the character offsets in the text are also provided.
+
+Here is an example showing how to use the model for predicting such sentiment tuples: 
 
 ```python
 >>> import model_wrapper
@@ -52,26 +70,35 @@ Here is an eaxmple showing to use  the model for predicting such sentiment tuple
     'Polarity': 'Positive'}]}]
 ```
 
+## Training Details
+
+### Training Data
+
+The model is trained on NoReC_fine, a dataset for fine-grained sentiment analysis in Norwegian, based on a subset of documents from the Norwegian Review Corpus (NoReC) which constists of professionally authored reviews from multiple news-sources and across a wide variety of domains, including literature, games, music, products, movies and more.
 
-## Details about the model configuration 
+- **Paper:** [A Fine-grained Sentiment Dataset for Norwegian](https://aclanthology.org/2020.lrec-1.618/) by L. Øvrelid, P. Mæhlum, J. Barnes, and E Velldal, in the Proceedings of the 12th Language Resources and Evaluation Conference, Marseille, 2020
+- **Repository:** [https://github.com/ltgoslo/norec_fine](https://github.com/ltgoslo/norec_fine) 
 
-The method proposed by Samuel et al. 2022 suggests three different ways to encode the sentiment graph: "node-centric", "labeled-edge", and "opinion-tuple".
-The model released here 
-- uses "labeled-edge" graph encoding, 
-- does not use character-level embedding,
+
+### Model Configuration and Training Hyperparameters
+
+The method proposed by Samuel et al. (2022) suggests three different ways to encode sentiment graphs: "node-centric", "labeled-edge", and "opinion-tuple".
+The model released here uses the following configuration: 
+- "labeled-edge" graph encoding, 
+- no character-level embeddings,
 - all other hyperparameters are set to [default values](https://github.com/jerbarnes/direct_parsing_to_sent_graph/blob/main/perin/config/edge_norec.yaml),
-- is trained on top of underlying masked language model [NorBERT 2](https://huggingface.co/ltg/norbert2).
+- trained on top of underlying masked language model [NorBERT 2](https://huggingface.co/ltg/norbert2).
 
-It achieves the following results on the held-out test set of NoReC_fine:
+## Evaluation
 
-| Unlabeled sentiment tuple F1 | Target F1  | Relative polarity precision |
-|:----------------------------:|:----------:|:---------------------------:|
-|     0.434                    |  0.541      |        0.926                |
+The model achieves the following results on the held-out test set of NoReC_fine (see the paper for description the metrics):
 
-The scripts used for training can be found on the [github](https://github.com/jerbarnes/direct_parsing_to_sent_graph) repository accompanying the paper by Samuel et al., 2022.
+- Unlabeled sentiment tuple F1: 0.434
+- Target F1: 0.541 
+- Relative polarity precision: 0.926
 
 
-## Quote us
+## Citation
 
 If you use this model in your academic work, please quote the following paper:
 ```bibtex
@@ -85,3 +112,6 @@ If you use this model in your academic work, please quote the following paper:
       address = "Dublin, Ireland"
 }
 ```
+
+## Model Card Authors
+Erik Velldal and Larisa Kolesnichenko