# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("alexandrainst/da-emotion-classification-base")
model = AutoModelForSequenceClassification.from_pretrained("alexandrainst/da-emotion-classification-base")Quick Links
Danish BERT for emotion classification
The BERT Emotion model classifies a Danish text in one of the following class:
- Glæde/Sindsro
- Tillid/Accept
- Forventning/Interrese
- Overasket/Målløs
- Vrede/Irritation
- Foragt/Modvilje
- Sorg/trist
- Frygt/Bekymret
It is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data.
This model should be used after detecting whether the text contains emotion or not, using the binary BERT Emotion model.
See the DaNLP documentation for more details.
Here is how to use the model:
from transformers import BertTokenizer, BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained("alexandrainst/da-emotion-classification-base")
tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-emotion-classification-base")
Training data
The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alexandrainst/da-emotion-classification-base")