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: 10 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 |
---|---|
9 |
|
2 |
|
0 |
|
4 |
|
8 |
|
6 |
|
3 |
|
5 |
|
7 |
|
1 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.7773 |
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_item_top_bt13")
# Run inference
preds = model("케라스타즈 엘릭서 얼팀 헤어오일 엠페리얼 티 100ml × 1개 (#M)쿠팡 홈>뷰티>헤어>헤어에센스/오일>헤어오일 Coupang > 뷰티 > 헤어 > 헤어에센스/오일 > 헤어오일")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 13 | 24.996 | 125 |
Label | Training Sample Count |
---|---|
0 | 50 |
1 | 50 |
2 | 50 |
3 | 50 |
4 | 50 |
5 | 50 |
6 | 50 |
7 | 50 |
8 | 50 |
9 | 50 |
Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 100
- 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 |
---|---|---|---|
0.0013 | 1 | 0.4713 | - |
0.0639 | 50 | 0.4253 | - |
0.1279 | 100 | 0.3864 | - |
0.1918 | 150 | 0.358 | - |
0.2558 | 200 | 0.3284 | - |
0.3197 | 250 | 0.3139 | - |
0.3836 | 300 | 0.2877 | - |
0.4476 | 350 | 0.2604 | - |
0.5115 | 400 | 0.2218 | - |
0.5754 | 450 | 0.1841 | - |
0.6394 | 500 | 0.1548 | - |
0.7033 | 550 | 0.1272 | - |
0.7673 | 600 | 0.1068 | - |
0.8312 | 650 | 0.0866 | - |
0.8951 | 700 | 0.0656 | - |
0.9591 | 750 | 0.0477 | - |
1.0230 | 800 | 0.0377 | - |
1.0870 | 850 | 0.0249 | - |
1.1509 | 900 | 0.0144 | - |
1.2148 | 950 | 0.0131 | - |
1.2788 | 1000 | 0.0153 | - |
1.3427 | 1050 | 0.012 | - |
1.4066 | 1100 | 0.0104 | - |
1.4706 | 1150 | 0.0102 | - |
1.5345 | 1200 | 0.0079 | - |
1.5985 | 1250 | 0.0039 | - |
1.6624 | 1300 | 0.0026 | - |
1.7263 | 1350 | 0.0015 | - |
1.7903 | 1400 | 0.001 | - |
1.8542 | 1450 | 0.0013 | - |
1.9182 | 1500 | 0.0013 | - |
1.9821 | 1550 | 0.001 | - |
2.0460 | 1600 | 0.0009 | - |
2.1100 | 1650 | 0.0012 | - |
2.1739 | 1700 | 0.0007 | - |
2.2379 | 1750 | 0.0009 | - |
2.3018 | 1800 | 0.0009 | - |
2.3657 | 1850 | 0.0007 | - |
2.4297 | 1900 | 0.0011 | - |
2.4936 | 1950 | 0.0008 | - |
2.5575 | 2000 | 0.0015 | - |
2.6215 | 2050 | 0.0028 | - |
2.6854 | 2100 | 0.0032 | - |
2.7494 | 2150 | 0.0019 | - |
2.8133 | 2200 | 0.0017 | - |
2.8772 | 2250 | 0.0008 | - |
2.9412 | 2300 | 0.0019 | - |
3.0051 | 2350 | 0.0016 | - |
3.0691 | 2400 | 0.0018 | - |
3.1330 | 2450 | 0.0013 | - |
3.1969 | 2500 | 0.0007 | - |
3.2609 | 2550 | 0.0006 | - |
3.3248 | 2600 | 0.0009 | - |
3.3887 | 2650 | 0.0016 | - |
3.4527 | 2700 | 0.002 | - |
3.5166 | 2750 | 0.0032 | - |
3.5806 | 2800 | 0.0012 | - |
3.6445 | 2850 | 0.0012 | - |
3.7084 | 2900 | 0.0014 | - |
3.7724 | 2950 | 0.0011 | - |
3.8363 | 3000 | 0.0005 | - |
3.9003 | 3050 | 0.0007 | - |
3.9642 | 3100 | 0.0004 | - |
4.0281 | 3150 | 0.0003 | - |
4.0921 | 3200 | 0.0007 | - |
4.1560 | 3250 | 0.0005 | - |
4.2199 | 3300 | 0.0005 | - |
4.2839 | 3350 | 0.0006 | - |
4.3478 | 3400 | 0.0004 | - |
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4.6675 | 3650 | 0.0007 | - |
4.7315 | 3700 | 0.0009 | - |
4.7954 | 3750 | 0.0005 | - |
4.8593 | 3800 | 0.0006 | - |
4.9233 | 3850 | 0.0007 | - |
4.9872 | 3900 | 0.0005 | - |
5.0512 | 3950 | 0.0006 | - |
5.1151 | 4000 | 0.0004 | - |
5.1790 | 4050 | 0.0005 | - |
5.2430 | 4100 | 0.0007 | - |
5.3069 | 4150 | 0.0004 | - |
5.3708 | 4200 | 0.0005 | - |
5.4348 | 4250 | 0.0004 | - |
5.4987 | 4300 | 0.0005 | - |
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7.0332 | 5500 | 0.0007 | - |
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7.1611 | 5600 | 0.0093 | - |
7.2251 | 5650 | 0.0075 | - |
7.2890 | 5700 | 0.0017 | - |
7.3529 | 5750 | 0.0012 | - |
7.4169 | 5800 | 0.001 | - |
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7.6726 | 6000 | 0.0006 | - |
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10.0384 | 7850 | 0.0018 | - |
10.1023 | 7900 | 0.0045 | - |
10.1662 | 7950 | 0.0024 | - |
10.2302 | 8000 | 0.0013 | - |
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29.0281 | 22700 | 0.0007 | - |
29.0921 | 22750 | 0.0005 | - |
29.1560 | 22800 | 0.0004 | - |
29.2199 | 22850 | 0.0005 | - |
29.2839 | 22900 | 0.0007 | - |
29.3478 | 22950 | 0.0005 | - |
29.4118 | 23000 | 0.0004 | - |
29.4757 | 23050 | 0.0006 | - |
29.5396 | 23100 | 0.0004 | - |
29.6036 | 23150 | 0.0006 | - |
29.6675 | 23200 | 0.0005 | - |
29.7315 | 23250 | 0.0005 | - |
29.7954 | 23300 | 0.0007 | - |
29.8593 | 23350 | 0.0006 | - |
29.9233 | 23400 | 0.0006 | - |
29.9872 | 23450 | 0.0006 | - |
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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