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wikipedia_categories_setfit
KnutJaegersberg/wikipedia_categories_setfit
wikipedia_categories_setfit is a sentence similarity model from KnutJaegersberg. Use it when you need a score for how close two texts are. It is set up for setfit. The card lists the license as mit.
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 subca…
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At a glance
- Task
- Sentence Similarity
- Library
- setfit
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Jul 11, 2023
- Updated
- Jul 11, 2023
- SHA
- 3a1c4d11
Model card
From the Hugging Face model README
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).
Accuracy on test data split is 85 %.
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.
Use the wikipedia_categories_labels dataset as key.
from setfit import SetFitModel
Download from Hub and run inference model = SetFitModel.from_pretrained("KnutJaegersberg/wikipedia_categories_setfit")
Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
- Task
- Sentence Similarity
- Library
- setfit
- Type
- bert
- License
- mit
- Created
- Jul 11, 2023
- Updated
- Jul 11, 2023