metadata
base_model: mini1013/master_domain
library_name: setfit
metrics:
- metric
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
쿠쿠 CP-PS011T 자동살균직수정수기 (등록설치비면제/3년무상AS/조리수무료/3년정품필터 ) 쿠쿠본사무료설치/색상선택가능
골드(CP-PS011G)_미설치(X) 쿠쿠홈시스공식인증점
- text: LG전자 오브제컬렉션 매직스페이스 냉장고 (S834PB35) (UP) 주식회사 디깅(Digging Inc.)
- text: 리큅 10단 풀스텐 식품건조기 고추건조기 과일건조기 LID-1904S 주식회사 이스트코퍼레이션
- text: '[공인판매점] 키친에이드 아톰 오븐 5KCO211EBM 그릴 베이킹 토스트 (주)디아씨앤씨'
- text: 쿠첸 인버터 복합 레인지 COV-i231KGF 홀리데이마켓
inference: true
model-index:
- name: SetFit with mini1013/master_domain
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: metric
value: 0.8617920942607373
name: Metric
SetFit with mini1013/master_domain
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:
- 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: mini1013/master_domain
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 47 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 |
|---|---|
| 45 |
|
| 0 |
|
| 32 |
|
| 39 |
|
| 3 |
|
| 13 |
|
| 41 |
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| 18 |
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| 42 |
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| 38 |
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| 33 |
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| 37 |
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| 27 |
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| 31 |
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| 43 |
|
| 2 |
|
| 26 |
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| 44 |
|
| 5 |
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| 22 |
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| 30 |
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| 34 |
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| 14 |
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| 4 |
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| 21 |
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| 17 |
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| 36 |
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| 10 |
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| 16 |
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| 20 |
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| 40 |
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| 15 |
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| 23 |
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| 7 |
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| 25 |
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| 46 |
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| 24 |
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| 29 |
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| 12 |
|
| 11 |
|
| 28 |
|
| 1 |
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| 6 |
|
| 9 |
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| 35 |
|
| 8 |
|
| 19 |
|
Evaluation
Metrics
| Label | Metric |
|---|---|
| all | 0.8618 |
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_el17")
# Run inference
preds = model("쿠첸 인버터 복합 레인지 COV-i231KGF 홀리데이마켓")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 3 | 8.4377 | 25 |
| Label | Training Sample Count |
|---|---|
| 0 | 50 |
| 1 | 50 |
| 2 | 50 |
| 3 | 50 |
| 4 | 50 |
| 5 | 50 |
| 6 | 50 |
| 7 | 50 |
| 8 | 4 |
| 9 | 50 |
| 10 | 50 |
| 11 | 50 |
| 12 | 50 |
| 13 | 50 |
| 14 | 50 |
| 15 | 13 |
| 16 | 50 |
| 17 | 50 |
| 18 | 50 |
| 19 | 3 |
| 20 | 50 |
| 21 | 50 |
| 22 | 50 |
| 23 | 50 |
| 24 | 50 |
| 25 | 50 |
| 26 | 50 |
| 27 | 50 |
| 28 | 50 |
| 29 | 50 |
| 30 | 50 |
| 31 | 50 |
| 32 | 50 |
| 33 | 50 |
| 34 | 50 |
| 35 | 2 |
| 36 | 50 |
| 37 | 50 |
| 38 | 50 |
| 39 | 37 |
| 40 | 50 |
| 41 | 50 |
| 42 | 50 |
| 43 | 50 |
| 44 | 50 |
| 45 | 50 |
| 46 | 50 |
Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (20, 20)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0030 | 1 | 0.4981 | - |
| 0.1479 | 50 | 0.4962 | - |
| 0.2959 | 100 | 0.3194 | - |
| 0.4438 | 150 | 0.2125 | - |
| 0.5917 | 200 | 0.1649 | - |
| 0.7396 | 250 | 0.1254 | - |
| 0.8876 | 300 | 0.0936 | - |
| 1.0355 | 350 | 0.0739 | - |
| 1.1834 | 400 | 0.0466 | - |
| 1.3314 | 450 | 0.0464 | - |
| 1.4793 | 500 | 0.0444 | - |
| 1.6272 | 550 | 0.0447 | - |
| 1.7751 | 600 | 0.0254 | - |
| 1.9231 | 650 | 0.0264 | - |
| 2.0710 | 700 | 0.0251 | - |
| 2.2189 | 750 | 0.0321 | - |
| 2.3669 | 800 | 0.0237 | - |
| 2.5148 | 850 | 0.0203 | - |
| 2.6627 | 900 | 0.0217 | - |
| 2.8107 | 950 | 0.016 | - |
| 2.9586 | 1000 | 0.014 | - |
| 3.1065 | 1050 | 0.0076 | - |
| 3.2544 | 1100 | 0.0096 | - |
| 3.4024 | 1150 | 0.0118 | - |
| 3.5503 | 1200 | 0.0058 | - |
| 3.6982 | 1250 | 0.0121 | - |
| 3.8462 | 1300 | 0.0126 | - |
| 3.9941 | 1350 | 0.0064 | - |
| 4.1420 | 1400 | 0.0046 | - |
| 4.2899 | 1450 | 0.0061 | - |
| 4.4379 | 1500 | 0.008 | - |
| 4.5858 | 1550 | 0.0018 | - |
| 4.7337 | 1600 | 0.0081 | - |
| 4.8817 | 1650 | 0.0131 | - |
| 5.0296 | 1700 | 0.008 | - |
| 5.1775 | 1750 | 0.0069 | - |
| 5.3254 | 1800 | 0.006 | - |
| 5.4734 | 1850 | 0.0021 | - |
| 5.6213 | 1900 | 0.0039 | - |
| 5.7692 | 1950 | 0.0045 | - |
| 5.9172 | 2000 | 0.0032 | - |
| 6.0651 | 2050 | 0.0016 | - |
| 6.2130 | 2100 | 0.0014 | - |
| 6.3609 | 2150 | 0.0008 | - |
| 6.5089 | 2200 | 0.0012 | - |
| 6.6568 | 2250 | 0.0025 | - |
| 6.8047 | 2300 | 0.0004 | - |
| 6.9527 | 2350 | 0.0025 | - |
| 7.1006 | 2400 | 0.0023 | - |
| 7.2485 | 2450 | 0.0019 | - |
| 7.3964 | 2500 | 0.004 | - |
| 7.5444 | 2550 | 0.0021 | - |
| 7.6923 | 2600 | 0.0019 | - |
| 7.8402 | 2650 | 0.0041 | - |
| 7.9882 | 2700 | 0.0014 | - |
| 8.1361 | 2750 | 0.001 | - |
| 8.2840 | 2800 | 0.0024 | - |
| 8.4320 | 2850 | 0.0044 | - |
| 8.5799 | 2900 | 0.0022 | - |
| 8.7278 | 2950 | 0.0003 | - |
| 8.8757 | 3000 | 0.0021 | - |
| 9.0237 | 3050 | 0.0002 | - |
| 9.1716 | 3100 | 0.0002 | - |
| 9.3195 | 3150 | 0.002 | - |
| 9.4675 | 3200 | 0.0002 | - |
| 9.6154 | 3250 | 0.0002 | - |
| 9.7633 | 3300 | 0.0002 | - |
| 9.9112 | 3350 | 0.0003 | - |
| 10.0592 | 3400 | 0.0002 | - |
| 10.2071 | 3450 | 0.0003 | - |
| 10.3550 | 3500 | 0.0003 | - |
| 10.5030 | 3550 | 0.0002 | - |
| 10.6509 | 3600 | 0.002 | - |
| 10.7988 | 3650 | 0.0002 | - |
| 10.9467 | 3700 | 0.0002 | - |
| 11.0947 | 3750 | 0.0014 | - |
| 11.2426 | 3800 | 0.0003 | - |
| 11.3905 | 3850 | 0.0001 | - |
| 11.5385 | 3900 | 0.0034 | - |
| 11.6864 | 3950 | 0.0017 | - |
| 11.8343 | 4000 | 0.0016 | - |
| 11.9822 | 4050 | 0.0002 | - |
| 12.1302 | 4100 | 0.0002 | - |
| 12.2781 | 4150 | 0.0004 | - |
| 12.4260 | 4200 | 0.0002 | - |
| 12.5740 | 4250 | 0.0002 | - |
| 12.7219 | 4300 | 0.0002 | - |
| 12.8698 | 4350 | 0.0001 | - |
| 13.0178 | 4400 | 0.0003 | - |
| 13.1657 | 4450 | 0.0002 | - |
| 13.3136 | 4500 | 0.0001 | - |
| 13.4615 | 4550 | 0.0019 | - |
| 13.6095 | 4600 | 0.0002 | - |
| 13.7574 | 4650 | 0.0001 | - |
| 13.9053 | 4700 | 0.0001 | - |
| 14.0533 | 4750 | 0.0001 | - |
| 14.2012 | 4800 | 0.0001 | - |
| 14.3491 | 4850 | 0.0001 | - |
| 14.4970 | 4900 | 0.0001 | - |
| 14.6450 | 4950 | 0.0001 | - |
| 14.7929 | 5000 | 0.0001 | - |
| 14.9408 | 5050 | 0.0001 | - |
| 15.0888 | 5100 | 0.0001 | - |
| 15.2367 | 5150 | 0.0001 | - |
| 15.3846 | 5200 | 0.0001 | - |
| 15.5325 | 5250 | 0.0001 | - |
| 15.6805 | 5300 | 0.0001 | - |
| 15.8284 | 5350 | 0.0001 | - |
| 15.9763 | 5400 | 0.0001 | - |
| 16.1243 | 5450 | 0.0019 | - |
| 16.2722 | 5500 | 0.0001 | - |
| 16.4201 | 5550 | 0.0001 | - |
| 16.5680 | 5600 | 0.0002 | - |
| 16.7160 | 5650 | 0.0001 | - |
| 16.8639 | 5700 | 0.0001 | - |
| 17.0118 | 5750 | 0.0001 | - |
| 17.1598 | 5800 | 0.0001 | - |
| 17.3077 | 5850 | 0.0001 | - |
| 17.4556 | 5900 | 0.0001 | - |
| 17.6036 | 5950 | 0.0001 | - |
| 17.7515 | 6000 | 0.0001 | - |
| 17.8994 | 6050 | 0.0017 | - |
| 18.0473 | 6100 | 0.0001 | - |
| 18.1953 | 6150 | 0.0001 | - |
| 18.3432 | 6200 | 0.0001 | - |
| 18.4911 | 6250 | 0.0001 | - |
| 18.6391 | 6300 | 0.0001 | - |
| 18.7870 | 6350 | 0.0001 | - |
| 18.9349 | 6400 | 0.0001 | - |
| 19.0828 | 6450 | 0.0001 | - |
| 19.2308 | 6500 | 0.0001 | - |
| 19.3787 | 6550 | 0.0001 | - |
| 19.5266 | 6600 | 0.0019 | - |
| 19.6746 | 6650 | 0.0001 | - |
| 19.8225 | 6700 | 0.0001 | - |
| 19.9704 | 6750 | 0.0001 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.46.1
- PyTorch: 2.4.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.20.0
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}
}