Instructions to use fhamborg/newsframes-aff with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use fhamborg/newsframes-aff with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("fhamborg/newsframes-aff") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - setfit
How to use fhamborg/newsframes-aff with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("fhamborg/newsframes-aff") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - setfit | |
| - sentence-transformers | |
| - text-classification | |
| pipeline_tag: text-classification | |
| # NewsFrames classifier | |
| This is one of a series of classifiers devised for automatically identifying universal framing dimensions. A paper on the underlying training dataset and the framing dimensions in particular is currently being written. This page will be updated once the paper is finished. | |
| ## Acknowledgements | |
| This work would not have been possible without the contributions by [Tilman Hornung](t1h0), Kim Heinser, and our team of student research assistants. | |