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Model Card for Danish BERT

Danish BERT Tone for sentiment polarity detection

Model Details

Model Description

The BERT Tone model detects sentiment polarity (positive, neutral or negative) in Danish texts. It has been finetuned on the pretrained Danish BERT model by BotXO.

  • Developed by: DaNLP
  • Shared by [Optional]: Hugging Face
  • Model type: Text Classification
  • Language(s) (NLP): Danish (da)
  • License: cc-by-sa-4.0
  • Related Models: More information needed
    • Parent Model: BERT
  • Resources for more information:


Direct Use

This model can be used for text classification

Downstream Use [Optional]

More information needed.

Out-of-Scope Use

The model should not be used to intentionally create hostile or alienating environments for people.

Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.


Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Training Details

Training Data

The data used for training come from the Twitter Sentiment and EuroParl sentiment 2 datasets.

Training Procedure


It has been finetuned on the pretrained Danish BERT model by BotXO.

Speeds, Sizes, Times

More information needed.


Testing Data, Factors & Metrics

Testing Data

More information needed.





More information needed.

Model Examination

More information needed.

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: More information needed.
  • Hours used: More information needed.
  • Cloud Provider: More information needed.
  • Compute Region: More information needed.
  • Carbon Emitted: More information needed.

Technical Specifications [optional]

Model Architecture and Objective

More information needed.

Compute Infrastructure

More information needed.


More information needed.


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Glossary [optional]

More information needed.

More Information [optional]

More information needed.

Model Card Authors [optional]

DaNLP in collaboration with Ezi Ozoani and the Hugging Face team

Model Card Contact

More information needed.

How to Get Started with the Model

Use the code below to get started with the model.

Click to expand
from transformers import BertTokenizer, BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained("alexandrainst/da-sentiment-base")
tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-sentiment-base")
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