Da Sentiment Base

🚀 Danish BERT Model Card

Danish BERT Tone for sentiment polarity detection

🚀 Quick Start

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")

✨ Features

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.

📚 Documentation

Model Details

Uses

Direct Use

This model can be used for text classification.

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.

Evaluation

Testing Data, Factors & Metrics

Metrics

F1

Environmental Impact

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

Model Card Authors

DaNLP in collaboration with Ezi Ozoani and the Hugging Face team