Efficient Few-Shot Learning Without Prompts
Paper
•
2209.11055
•
Published
•
4
This is a SetFit model that can be used for Text Classification. This SetFit model uses mini1013/master_domain as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
| Label | Examples |
|---|---|
| 1.0 |
|
| 8.0 |
|
| 6.0 |
|
| 7.0 |
|
| 5.0 |
|
| 3.0 |
|
| 4.0 |
|
| 0.0 |
|
| 2.0 |
|
| Label | Accuracy |
|---|---|
| all | 1.0 |
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_cate_bc24")
# Run inference
preds = model("순면 모달 귀여운 어린이집 낮잠이불 풀세트 30.디노디노 낮잠패드_패드단품 출산/육아 > 유아침구 > 낮잠이불")
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 7 | 15.1397 | 27 |
| Label | Training Sample Count |
|---|---|
| 0.0 | 70 |
| 1.0 | 70 |
| 2.0 | 70 |
| 3.0 | 70 |
| 4.0 | 70 |
| 5.0 | 70 |
| 6.0 | 70 |
| 7.0 | 70 |
| 8.0 | 70 |
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0081 | 1 | 0.517 | - |
| 0.4032 | 50 | 0.4962 | - |
| 0.8065 | 100 | 0.4906 | - |
| 1.2097 | 150 | 0.454 | - |
| 1.6129 | 200 | 0.0667 | - |
| 2.0161 | 250 | 0.0003 | - |
| 2.4194 | 300 | 0.0001 | - |
| 2.8226 | 350 | 0.0001 | - |
| 3.2258 | 400 | 0.0 | - |
| 3.6290 | 450 | 0.0 | - |
| 4.0323 | 500 | 0.0 | - |
| 4.4355 | 550 | 0.0 | - |
| 4.8387 | 600 | 0.0 | - |
| 5.2419 | 650 | 0.0 | - |
| 5.6452 | 700 | 0.0 | - |
| 6.0484 | 750 | 0.0 | - |
| 6.4516 | 800 | 0.0 | - |
| 6.8548 | 850 | 0.0 | - |
| 7.2581 | 900 | 0.0 | - |
| 7.6613 | 950 | 0.0 | - |
| 8.0645 | 1000 | 0.0 | - |
| 8.4677 | 1050 | 0.0 | - |
| 8.8710 | 1100 | 0.0 | - |
| 9.2742 | 1150 | 0.0 | - |
| 9.6774 | 1200 | 0.0 | - |
| 10.0806 | 1250 | 0.0 | - |
| 10.4839 | 1300 | 0.0 | - |
| 10.8871 | 1350 | 0.0 | - |
| 11.2903 | 1400 | 0.0 | - |
| 11.6935 | 1450 | 0.0 | - |
| 12.0968 | 1500 | 0.0 | - |
| 12.5 | 1550 | 0.0 | - |
| 12.9032 | 1600 | 0.0 | - |
| 13.3065 | 1650 | 0.0 | - |
| 13.7097 | 1700 | 0.0 | - |
| 14.1129 | 1750 | 0.0 | - |
| 14.5161 | 1800 | 0.0 | - |
| 14.9194 | 1850 | 0.0 | - |
| 15.3226 | 1900 | 0.0 | - |
| 15.7258 | 1950 | 0.0 | - |
| 16.1290 | 2000 | 0.0 | - |
| 16.5323 | 2050 | 0.0 | - |
| 16.9355 | 2100 | 0.0 | - |
| 17.3387 | 2150 | 0.0 | - |
| 17.7419 | 2200 | 0.0 | - |
| 18.1452 | 2250 | 0.0 | - |
| 18.5484 | 2300 | 0.0 | - |
| 18.9516 | 2350 | 0.0 | - |
| 19.3548 | 2400 | 0.0 | - |
| 19.7581 | 2450 | 0.0 | - |
| 20.1613 | 2500 | 0.0 | - |
| 20.5645 | 2550 | 0.0 | - |
| 20.9677 | 2600 | 0.0 | - |
| 21.3710 | 2650 | 0.0 | - |
| 21.7742 | 2700 | 0.0 | - |
| 22.1774 | 2750 | 0.0 | - |
| 22.5806 | 2800 | 0.0 | - |
| 22.9839 | 2850 | 0.0 | - |
| 23.3871 | 2900 | 0.0 | - |
| 23.7903 | 2950 | 0.0 | - |
| 24.1935 | 3000 | 0.0 | - |
| 24.5968 | 3050 | 0.0 | - |
| 25.0 | 3100 | 0.0 | - |
| 25.4032 | 3150 | 0.0 | - |
| 25.8065 | 3200 | 0.0 | - |
| 26.2097 | 3250 | 0.0 | - |
| 26.6129 | 3300 | 0.0 | - |
| 27.0161 | 3350 | 0.0 | - |
| 27.4194 | 3400 | 0.0 | - |
| 27.8226 | 3450 | 0.0 | - |
| 28.2258 | 3500 | 0.0 | - |
| 28.6290 | 3550 | 0.0 | - |
| 29.0323 | 3600 | 0.0 | - |
| 29.4355 | 3650 | 0.0 | - |
| 29.8387 | 3700 | 0.0 | - |
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}