Instructions to use alexandrainst/da-hatespeech-classification-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alexandrainst/da-hatespeech-classification-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alexandrainst/da-hatespeech-classification-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alexandrainst/da-hatespeech-classification-base") model = AutoModelForSequenceClassification.from_pretrained("alexandrainst/da-hatespeech-classification-base") - Notebooks
- Google Colab
- Kaggle
Danish BERT for hate speech classification
The BERT HateSpeech model classifies offensive Danish text into 4 categories:
Særlig opmærksomhed(special attention, e.g. threat)Personangreb(personal attack)Sprogbrug(offensive language)Spam & indhold(spam) This model is intended to be used after the BERT HateSpeech detection model.
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-hatespeech-classification-base")
tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-hatespeech-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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