README.md

---
license: apache-2.0
language:
- 'no'
pipeline_tag: token-classification
model-index:
- name: SSA-Perin
  results:
  - task:
      type: structured sentiment analysis
    dataset:
      name: NoReC
      type: NoReC
    metrics:
    - name: Unlabeled sentiment tuple F1
      type: Unlabeled sentiment tuple F1
      value: 44.12%
    - name: Target F1
      type: Target F1
      value: 56.44%
    - name: Relative polarity precision
      type: Relative polarity precision
      value: 93.19%
---



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).


  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 dataset:

| Unlabeled sentiment tuple F1 | Target F1  | Relative polarity precision |
|:----------------------------:|:----------:|:---------------------------:|
|     0.434                    |  0.541      |        0.926                |


The model can be easily used for predicting sentiment tuples as follows: 

```python
>>> import model_wrapper
>>> model = model_wrapper.PredictionModel()
>>> model.predict(['vi liker svart kaffe'])
[{'sent_id': '0',
  'text': 'vi liker svart kaffe',
  'opinions': [{'Source': [['vi'], ['0:2']],
    'Target': [['svart', 'kaffe'], ['9:14', '15:20']],
    'Polar_expression': [['liker'], ['3:8']],
    'Polarity': 'Positive'}]}]
```