readme: 
  ---
  language:
  - da
  license: cc-by-sa-4.0
  widget:
  - text: Det er super godt
  ---
  
  # 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:** 
    - [GitHub Repo](https://github.com/certainlyio/nordic_bert) 
    -  [Associated Documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-tone)
   
   
  # Uses
   
  ## 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)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
   
   
  ## Recommendations
   
   
  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](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.
   
  ## Training Procedure
   
  ### Preprocessing
   
  It has been finetuned on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO. 
   
  ### Speeds, Sizes, Times
  More information needed.
   
  # Evaluation
   
   
  ## Testing Data, Factors & Metrics
   
  ### Testing Data
   
  More information needed.
   
  ### Factors
   
   
   
  ### Metrics
   
  F1
   
  ## Results 
   
  More information needed.
   
  # Model Examination
   
  More information needed.
   
  # Environmental Impact
   
   
  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
   
  - **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.
   
  ### Hardware
   
  More information needed.
   
  ### Software
   
  More information needed.
   
  # Citation
   
  **BibTeX:**
   
  More information needed.
   
  **APA:**
   
  More information needed.
   
  # 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.
  <details>
  <summary> Click to expand </summary>
  
  ```python
  from transformers import BertTokenizer, BertForSequenceClassification
   
  model = BertForSequenceClassification.from_pretrained("alexandrainst/da-sentiment-base")
  tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-sentiment-base")
  ```
  </details>
   
  
  