DDSC/europarl
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How to use alexandrainst/da-subjectivivity-classification-base with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="alexandrainst/da-subjectivivity-classification-base") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("alexandrainst/da-subjectivivity-classification-base")
model = AutoModelForSequenceClassification.from_pretrained("alexandrainst/da-subjectivivity-classification-base")The BERT Tone model detects whether a text (in Danish) is subjective or objective. The model is based on the finetuning of the pretrained Danish BERT model by BotXO.
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-subjectivivity-classification-base")
tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-subjectivivity-classification-base")
The data used for training come from the Twitter Sentiment and EuroParl sentiment 2 datasets.