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:
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) 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.
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 and EuroParl sentiment 2 datasets.
Training Procedure
Preprocessing
It has been finetuned on the pretrained Danish 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 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.
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.
from transformers import BertTokenizer, BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained("alexandrainst/da-sentiment-base")
tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-sentiment-base")