Efficient Few-Shot Learning Without Prompts
Paper
•
2209.11055
•
Published
•
4
SetFit with klue/roberta-base
This is a SetFit model that can be used for Text Classification. This SetFit model uses klue/roberta-base 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:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: klue/roberta-base
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 21 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
| Label | Examples |
|---|---|
| 15.0 |
|
| 5.0 |
|
| 7.0 |
|
| 10.0 |
|
| 3.0 |
|
| 0.0 |
|
| 16.0 |
|
| 4.0 |
|
| 20.0 |
|
| 11.0 |
|
| 17.0 |
|
| 18.0 |
|
| 2.0 |
|
| 19.0 |
|
| 14.0 |
|
| 12.0 |
|
| 13.0 |
|
| 6.0 |
|
| 9.0 |
|
| 1.0 |
|
| 8.0 |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.9808 |
Uses
Direct Use for Inference
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_top_fd5")
# Run inference
preds = model("쌀땅콩엿 40g 30개입 땅콩 엿 (#M)식품>과자/베이커리>엿 T200 > Naverstore > 식품 > 과자/떡/베이커리 > 사탕/껌/엿 > 엿")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 11 | 22.5533 | 62 |
| Label | Training Sample Count |
|---|---|
| 0.0 | 50 |
| 1.0 | 50 |
| 2.0 | 50 |
| 3.0 | 50 |
| 4.0 | 50 |
| 5.0 | 50 |
| 6.0 | 50 |
| 7.0 | 50 |
| 8.0 | 50 |
| 9.0 | 50 |
| 10.0 | 50 |
| 11.0 | 50 |
| 12.0 | 50 |
| 13.0 | 50 |
| 14.0 | 50 |
| 15.0 | 50 |
| 16.0 | 50 |
| 17.0 | 50 |
| 18.0 | 50 |
| 19.0 | 50 |
| 20.0 | 50 |
Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (20, 20)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 30
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0020 | 1 | 0.5412 | - |
| 0.1014 | 50 | 0.4524 | - |
| 0.2028 | 100 | 0.3473 | - |
| 0.3043 | 150 | 0.24 | - |
| 0.4057 | 200 | 0.137 | - |
| 0.5071 | 250 | 0.0886 | - |
| 0.6085 | 300 | 0.0508 | - |
| 0.7099 | 350 | 0.0331 | - |
| 0.8114 | 400 | 0.0217 | - |
| 0.9128 | 450 | 0.0161 | - |
| 1.0142 | 500 | 0.013 | - |
| 1.1156 | 550 | 0.01 | - |
| 1.2170 | 600 | 0.01 | - |
| 1.3185 | 650 | 0.0058 | - |
| 1.4199 | 700 | 0.0032 | - |
| 1.5213 | 750 | 0.002 | - |
| 1.6227 | 800 | 0.0016 | - |
| 1.7241 | 850 | 0.0022 | - |
| 1.8256 | 900 | 0.0021 | - |
| 1.9270 | 950 | 0.0007 | - |
| 2.0284 | 1000 | 0.0005 | - |
| 2.1298 | 1050 | 0.0005 | - |
| 2.2312 | 1100 | 0.0002 | - |
| 2.3327 | 1150 | 0.0002 | - |
| 2.4341 | 1200 | 0.0002 | - |
| 2.5355 | 1250 | 0.0002 | - |
| 2.6369 | 1300 | 0.0002 | - |
| 2.7383 | 1350 | 0.0001 | - |
| 2.8398 | 1400 | 0.0005 | - |
| 2.9412 | 1450 | 0.0004 | - |
| 3.0426 | 1500 | 0.0002 | - |
| 3.1440 | 1550 | 0.0002 | - |
| 3.2454 | 1600 | 0.0001 | - |
| 3.3469 | 1650 | 0.0001 | - |
| 3.4483 | 1700 | 0.0001 | - |
| 3.5497 | 1750 | 0.0001 | - |
| 3.6511 | 1800 | 0.0001 | - |
| 3.7525 | 1850 | 0.0001 | - |
| 3.8540 | 1900 | 0.0001 | - |
| 3.9554 | 1950 | 0.0001 | - |
| 4.0568 | 2000 | 0.0001 | - |
| 4.1582 | 2050 | 0.0001 | - |
| 4.2596 | 2100 | 0.0001 | - |
| 4.3611 | 2150 | 0.0001 | - |
| 4.4625 | 2200 | 0.0001 | - |
| 4.5639 | 2250 | 0.0 | - |
| 4.6653 | 2300 | 0.0 | - |
| 4.7667 | 2350 | 0.0 | - |
| 4.8682 | 2400 | 0.0001 | - |
| 4.9696 | 2450 | 0.0 | - |
| 5.0710 | 2500 | 0.0 | - |
| 5.1724 | 2550 | 0.0 | - |
| 5.2738 | 2600 | 0.0 | - |
| 5.3753 | 2650 | 0.0 | - |
| 5.4767 | 2700 | 0.0 | - |
| 5.5781 | 2750 | 0.0 | - |
| 5.6795 | 2800 | 0.0013 | - |
| 5.7809 | 2850 | 0.0028 | - |
| 5.8824 | 2900 | 0.0009 | - |
| 5.9838 | 2950 | 0.0013 | - |
| 6.0852 | 3000 | 0.0002 | - |
| 6.1866 | 3050 | 0.0001 | - |
| 6.2880 | 3100 | 0.0 | - |
| 6.3895 | 3150 | 0.0 | - |
| 6.4909 | 3200 | 0.0 | - |
| 6.5923 | 3250 | 0.0 | - |
| 6.6937 | 3300 | 0.0 | - |
| 6.7951 | 3350 | 0.0 | - |
| 6.8966 | 3400 | 0.0 | - |
| 6.9980 | 3450 | 0.0 | - |
| 7.0994 | 3500 | 0.0 | - |
| 7.2008 | 3550 | 0.0 | - |
| 7.3022 | 3600 | 0.0 | - |
| 7.4037 | 3650 | 0.0 | - |
| 7.5051 | 3700 | 0.0 | - |
| 7.6065 | 3750 | 0.0 | - |
| 7.7079 | 3800 | 0.0 | - |
| 7.8093 | 3850 | 0.0 | - |
| 7.9108 | 3900 | 0.0 | - |
| 8.0122 | 3950 | 0.0 | - |
| 8.1136 | 4000 | 0.0 | - |
| 8.2150 | 4050 | 0.0 | - |
| 8.3164 | 4100 | 0.0 | - |
| 8.4178 | 4150 | 0.0 | - |
| 8.5193 | 4200 | 0.0 | - |
| 8.6207 | 4250 | 0.0 | - |
| 8.7221 | 4300 | 0.0 | - |
| 8.8235 | 4350 | 0.0 | - |
| 8.9249 | 4400 | 0.0 | - |
| 9.0264 | 4450 | 0.0 | - |
| 9.1278 | 4500 | 0.0 | - |
| 9.2292 | 4550 | 0.0 | - |
| 9.3306 | 4600 | 0.0 | - |
| 9.4320 | 4650 | 0.0 | - |
| 9.5335 | 4700 | 0.0 | - |
| 9.6349 | 4750 | 0.0 | - |
| 9.7363 | 4800 | 0.0 | - |
| 9.8377 | 4850 | 0.0 | - |
| 9.9391 | 4900 | 0.0 | - |
| 10.0406 | 4950 | 0.0 | - |
| 10.1420 | 5000 | 0.0 | - |
| 10.2434 | 5050 | 0.0 | - |
| 10.3448 | 5100 | 0.0 | - |
| 10.4462 | 5150 | 0.0 | - |
| 10.5477 | 5200 | 0.0 | - |
| 10.6491 | 5250 | 0.0 | - |
| 10.7505 | 5300 | 0.0 | - |
| 10.8519 | 5350 | 0.0 | - |
| 10.9533 | 5400 | 0.0 | - |
| 11.0548 | 5450 | 0.0 | - |
| 11.1562 | 5500 | 0.0 | - |
| 11.2576 | 5550 | 0.0 | - |
| 11.3590 | 5600 | 0.0 | - |
| 11.4604 | 5650 | 0.0 | - |
| 11.5619 | 5700 | 0.0 | - |
| 11.6633 | 5750 | 0.0 | - |
| 11.7647 | 5800 | 0.0 | - |
| 11.8661 | 5850 | 0.0 | - |
| 11.9675 | 5900 | 0.0 | - |
| 12.0690 | 5950 | 0.0 | - |
| 12.1704 | 6000 | 0.0 | - |
| 12.2718 | 6050 | 0.0 | - |
| 12.3732 | 6100 | 0.0 | - |
| 12.4746 | 6150 | 0.0 | - |
| 12.5761 | 6200 | 0.0 | - |
| 12.6775 | 6250 | 0.0005 | - |
| 12.7789 | 6300 | 0.0025 | - |
| 12.8803 | 6350 | 0.0023 | - |
| 12.9817 | 6400 | 0.0004 | - |
| 13.0832 | 6450 | 0.0 | - |
| 13.1846 | 6500 | 0.0 | - |
| 13.2860 | 6550 | 0.0 | - |
| 13.3874 | 6600 | 0.0 | - |
| 13.4888 | 6650 | 0.0 | - |
| 13.5903 | 6700 | 0.0 | - |
| 13.6917 | 6750 | 0.0 | - |
| 13.7931 | 6800 | 0.0003 | - |
| 13.8945 | 6850 | 0.0001 | - |
| 13.9959 | 6900 | 0.0 | - |
| 14.0974 | 6950 | 0.0 | - |
| 14.1988 | 7000 | 0.0 | - |
| 14.3002 | 7050 | 0.0 | - |
| 14.4016 | 7100 | 0.0 | - |
| 14.5030 | 7150 | 0.0 | - |
| 14.6045 | 7200 | 0.0 | - |
| 14.7059 | 7250 | 0.0 | - |
| 14.8073 | 7300 | 0.0 | - |
| 14.9087 | 7350 | 0.0 | - |
| 15.0101 | 7400 | 0.0 | - |
| 15.1116 | 7450 | 0.0 | - |
| 15.2130 | 7500 | 0.0 | - |
| 15.3144 | 7550 | 0.0 | - |
| 15.4158 | 7600 | 0.0 | - |
| 15.5172 | 7650 | 0.0 | - |
| 15.6187 | 7700 | 0.0 | - |
| 15.7201 | 7750 | 0.0 | - |
| 15.8215 | 7800 | 0.0 | - |
| 15.9229 | 7850 | 0.0 | - |
| 16.0243 | 7900 | 0.0 | - |
| 16.1258 | 7950 | 0.0 | - |
| 16.2272 | 8000 | 0.0 | - |
| 16.3286 | 8050 | 0.0 | - |
| 16.4300 | 8100 | 0.0 | - |
| 16.5314 | 8150 | 0.0 | - |
| 16.6329 | 8200 | 0.0 | - |
| 16.7343 | 8250 | 0.0 | - |
| 16.8357 | 8300 | 0.0 | - |
| 16.9371 | 8350 | 0.0 | - |
| 17.0385 | 8400 | 0.0 | - |
| 17.1400 | 8450 | 0.0 | - |
| 17.2414 | 8500 | 0.0 | - |
| 17.3428 | 8550 | 0.0 | - |
| 17.4442 | 8600 | 0.0 | - |
| 17.5456 | 8650 | 0.0 | - |
| 17.6471 | 8700 | 0.0 | - |
| 17.7485 | 8750 | 0.0 | - |
| 17.8499 | 8800 | 0.0 | - |
| 17.9513 | 8850 | 0.0 | - |
| 18.0527 | 8900 | 0.0 | - |
| 18.1542 | 8950 | 0.0 | - |
| 18.2556 | 9000 | 0.0 | - |
| 18.3570 | 9050 | 0.0 | - |
| 18.4584 | 9100 | 0.0 | - |
| 18.5598 | 9150 | 0.0 | - |
| 18.6613 | 9200 | 0.0 | - |
| 18.7627 | 9250 | 0.0 | - |
| 18.8641 | 9300 | 0.0 | - |
| 18.9655 | 9350 | 0.0 | - |
| 19.0669 | 9400 | 0.0 | - |
| 19.1684 | 9450 | 0.0 | - |
| 19.2698 | 9500 | 0.0 | - |
| 19.3712 | 9550 | 0.0 | - |
| 19.4726 | 9600 | 0.0 | - |
| 19.5740 | 9650 | 0.0 | - |
| 19.6755 | 9700 | 0.0 | - |
| 19.7769 | 9750 | 0.0 | - |
| 19.8783 | 9800 | 0.0 | - |
| 19.9797 | 9850 | 0.0 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.2.0a0+81ea7a4
- Datasets: 3.2.0
- Tokenizers: 0.19.1
Citation
BibTeX
@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}
}
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Evaluation results
- Accuracy on Unknowntest set self-reported0.981