transformers pytorch tf safetensors bert text-classification da arxiv:1910.09700 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
language:
- da
license: apache-2.0
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>