Instructions to use alexandrainst/da-subjectivivity-classification-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- da
license: apache-2.0
datasets:
- DDSC/twitter-sent
- DDSC/europarl
widget:
- text: Jeg tror alligvel, det bliver godt
Danish BERT Tone for the detection of subjectivity/objectivity
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")
Training data
The data used for training come from the Twitter Sentiment and EuroParl sentiment 2 datasets.