Instructions to use JohanHeinsen/Runaway_advertisement_identifier_V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JohanHeinsen/Runaway_advertisement_identifier_V1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JohanHeinsen/Runaway_advertisement_identifier_V1") 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 JohanHeinsen/Runaway_advertisement_identifier_V1 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("JohanHeinsen/Runaway_advertisement_identifier_V1") - Notebooks
- Google Colab
- Kaggle
Runaway_advertisement_identifier
This is a SetFit model that can be used for text classification. The model is designed to identify runaway advertisements from early modern Danish newspapers. It was created from a sample of 4000 texts, of which half where runaway advertisements. It was created by Johan Heinsen and Sofus Landor Dam.
Base model: JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
Metrics
Accuracy: 0.99333 F1: 0.99304
Get started like this:
from setfit import SetFitModel
model = SetFitModel.from_pretrained("JohanHeinsen/Runaway_advertisement_identifier_V1")
preds = model(["Min tjenstepige løb væk fra mig i nat", "Soldaten Jonas er forsvundet fra mit hus. Enhver bedes paagribe ham, om muligt. Han bærer en sort frakke.", "Jeg savner min hund."])
label_map = {0: "nej", 1: "ja"}
predicted_labels = [label_map[int(preds[0])], label_map[int(preds[1])]]
predicted_labels
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Model tree for JohanHeinsen/Runaway_advertisement_identifier_V1
Base model
CALDISS-AAU/DA-BERT_Old_News_V1