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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- feature-extraction |
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- sentence-similarity |
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- setfit |
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- e5 |
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license: mit |
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datasets: |
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- KnutJaegersberg/wikipedia_categories |
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- KnutJaegersberg/wikipedia_categories_labels |
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--- |
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This English model (e5-large as basis) predicts wikipedia categories (roundabout 37 labels). It is trained on the concatenation of the headlines of the lower level categories articles in few shot setting (i.e. 8 subcategories with their headline concatenations per level 2 category). |
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Accuracy on test data split is 85 %. |
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Note that these numbers are just an indicator that training worked, it will differ in production settings, which is why this classifier is meant for corpus exploration. |
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Use the wikipedia_categories_labels dataset as key. |
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from setfit import SetFitModel |
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Download from Hub and run inference |
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model = SetFitModel.from_pretrained("KnutJaegersberg/wikipedia_categories_setfit") |
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Run inference |
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preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) |
