File size: 1,016 Bytes
cb36cd8 f65aba2 5da8d2e cb36cd8 f65aba2 3a1c4d1 5da8d2e 3a1c4d1 f65aba2 5da8d2e f65aba2 5da8d2e f65aba2 5da8d2e 3a1c4d1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 |
---
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- sentence-similarity
- setfit
- e5
license: mit
datasets:
- KnutJaegersberg/wikipedia_categories
- KnutJaegersberg/wikipedia_categories_labels
---
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 🤮"]) |