| language: | |
| - da | |
| tags: | |
| - bert | |
| - pytorch | |
| - subjectivity | |
| - objectivity | |
| license: CC-BY_4.0 | |
| datasets: | |
| - Twitter Sentiment | |
| - Europarl Sentiment | |
| widget: | |
| - text: "Jeg tror alligvel, det bliver godt" | |
| metrics: | |
| - f1 | |
| # Danish BERT Tone for the detection of subjectivity/objectivity | |
| The BERT Tone model detects whether a text (in Danish) is subjective or objective. | |
| The model is based on the finetuning of the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO. | |
| See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-tone) for more details. | |
| Here is how to use the model: | |
| ```python | |
| from transformers import BertTokenizer, BertForSequenceClassification | |
| model = BertForSequenceClassification.from_pretrained("DaNLP/da-bert-tone-subjective-objective") | |
| tokenizer = BertTokenizer.from_pretrained("DaNLP/da-bert-tone-subjective-objective") | |
| ``` | |
| ## Training data | |
| The data used for training come from the [Twitter Sentiment](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#twitsent) and [EuroParl sentiment 2](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#europarl-sentiment2) datasets. | |