Instructions to use JohanHeinsen/Labour_advertisement_identifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JohanHeinsen/Labour_advertisement_identifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JohanHeinsen/Labour_advertisement_identifier") 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/Labour_advertisement_identifier with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("JohanHeinsen/Labour_advertisement_identifier") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
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This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification.
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Metrics: {'accuracy': 0.9966740576496674, 'f1': 0.9387755102040817}
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This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. It is trained to identify ads for labour (either workers searching for employment or employers seeking workers) in early modern Danish newspapers.
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Metrics: {'accuracy': 0.9966740576496674, 'f1': 0.9387755102040817}
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