# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("alexandrainst/da-binary-emotion-classification-base")
model = AutoModelForSequenceClassification.from_pretrained("alexandrainst/da-binary-emotion-classification-base")Quick Links
Danish BERT for emotion detection
The BERT Emotion model detects whether a Danish text is emotional or not. It is based on the pretrained Danish BERT model by BotXO which has been fine-tuned on social media data.
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-binary-emotion-classification-base")
tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-binary-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-binary-emotion-classification-base")