| language: | |
| - da | |
| tags: | |
| - bert | |
| - pytorch | |
| - emotion | |
| license: CC-BY_4.0 | |
| datasets: | |
| - social media | |
| metrics: | |
| - f1 | |
| widget: | |
| - text: "Jeg ejer en rød bil og det er en god bil." | |
| # Danish BERT for emotion classification | |
| The BERT Emotion model classifies a Danish text in one of the following class: | |
| * Glæde/Sindsro | |
| * Tillid/Accept | |
| * Forventning/Interrese | |
| * Overasket/Målløs | |
| * Vrede/Irritation | |
| * Foragt/Modvilje | |
| * Sorg/trist | |
| * Frygt/Bekymret | |
| It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data. | |
| This model should be used after detecting whether the text contains emotion or not, using the binary [BERT Emotion model](https://huggingface.co/DaNLP/da-bert-emotion-binary). | |
| See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-emotion) for more details. | |
| Here is how to use the model: | |
| ```python | |
| from transformers import BertTokenizer, BertForSequenceClassification | |
| model = BertForSequenceClassification.from_pretrained("DaNLP/da-bert-emotion-classification") | |
| tokenizer = BertTokenizer.from_pretrained("DaNLP/da-bert-emotion-classification") | |
| ``` | |
| ## Training data | |
| The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio. | |