Efficient Few-Shot Learning Without Prompts
Paper
•
2209.11055
•
Published
•
4
This is a SetFit model trained on the dvilasuero/banking77-topics-setfit dataset that can be used for Text Classification. This SetFit model uses thenlper/gte-large 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 |
|---|---|
| 2 |
|
| 0 |
|
| 5 |
|
| 1 |
|
| 3 |
|
| 6 |
|
| 7 |
|
| 4 |
|
| Label | Accuracy |
|---|---|
| all | 0.9231 |
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("HarshalBhg/gte-large-setfit-train-test2")
# Run inference
preds = model("I have a 1 euro fee on my statement.")
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 4 | 10.5833 | 40 |
| Label | Training Sample Count |
|---|---|
| 0 | 10 |
| 1 | 19 |
| 2 | 28 |
| 3 | 36 |
| 4 | 13 |
| 5 | 14 |
| 6 | 15 |
| 7 | 21 |
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0026 | 1 | 0.3183 | - |
| 0.1282 | 50 | 0.0614 | - |
| 0.2564 | 100 | 0.0044 | - |
| 0.3846 | 150 | 0.001 | - |
| 0.5128 | 200 | 0.0008 | - |
| 0.6410 | 250 | 0.001 | - |
| 0.7692 | 300 | 0.0006 | - |
| 0.8974 | 350 | 0.0012 | - |
@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}
}
Base model
thenlper/gte-large