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: 2 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 |
---|---|
1 |
|
0 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.8787 |
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_bt13_9_test_flat")
# Run inference
preds = model("미쟝센 퍼펙트 세럼 트리트먼트 330ml × 1개 (#M)쿠팡 홈>뷰티>헤어>트리트먼트/팩/앰플>일반 트리트먼트 Coupang > 뷰티 > 헤어 > 트리트먼트/팩/앰플 > 일반 트리트먼트")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 11 | 21.07 | 49 |
Label | Training Sample Count |
---|---|
0 | 50 |
1 | 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 |
---|---|---|---|
0.0064 | 1 | 0.4262 | - |
0.3185 | 50 | 0.4176 | - |
0.6369 | 100 | 0.314 | - |
0.9554 | 150 | 0.0953 | - |
1.2739 | 200 | 0.0302 | - |
1.5924 | 250 | 0.0123 | - |
1.9108 | 300 | 0.0005 | - |
2.2293 | 350 | 0.0002 | - |
2.5478 | 400 | 0.0001 | - |
2.8662 | 450 | 0.0001 | - |
3.1847 | 500 | 0.0001 | - |
3.5032 | 550 | 0.0 | - |
3.8217 | 600 | 0.0001 | - |
4.1401 | 650 | 0.0 | - |
4.4586 | 700 | 0.0 | - |
4.7771 | 750 | 0.0 | - |
5.0955 | 800 | 0.0001 | - |
5.4140 | 850 | 0.0001 | - |
5.7325 | 900 | 0.0 | - |
6.0510 | 950 | 0.0 | - |
6.3694 | 1000 | 0.0 | - |
6.6879 | 1050 | 0.0 | - |
7.0064 | 1100 | 0.0 | - |
7.3248 | 1150 | 0.0 | - |
7.6433 | 1200 | 0.0 | - |
7.9618 | 1250 | 0.0 | - |
8.2803 | 1300 | 0.0 | - |
8.5987 | 1350 | 0.0 | - |
8.9172 | 1400 | 0.0 | - |
9.2357 | 1450 | 0.0 | - |
9.5541 | 1500 | 0.0 | - |
9.8726 | 1550 | 0.0 | - |
10.1911 | 1600 | 0.0 | - |
10.5096 | 1650 | 0.0 | - |
10.8280 | 1700 | 0.0 | - |
11.1465 | 1750 | 0.0 | - |
11.4650 | 1800 | 0.0 | - |
11.7834 | 1850 | 0.0 | - |
12.1019 | 1900 | 0.0 | - |
12.4204 | 1950 | 0.0 | - |
12.7389 | 2000 | 0.0 | - |
13.0573 | 2050 | 0.0 | - |
13.3758 | 2100 | 0.0 | - |
13.6943 | 2150 | 0.0 | - |
14.0127 | 2200 | 0.0 | - |
14.3312 | 2250 | 0.0 | - |
14.6497 | 2300 | 0.0 | - |
14.9682 | 2350 | 0.0 | - |
15.2866 | 2400 | 0.0 | - |
15.6051 | 2450 | 0.0 | - |
15.9236 | 2500 | 0.0 | - |
16.2420 | 2550 | 0.0 | - |
16.5605 | 2600 | 0.0 | - |
16.8790 | 2650 | 0.0 | - |
17.1975 | 2700 | 0.0001 | - |
17.5159 | 2750 | 0.0001 | - |
17.8344 | 2800 | 0.0003 | - |
18.1529 | 2850 | 0.0 | - |
18.4713 | 2900 | 0.0 | - |
18.7898 | 2950 | 0.0 | - |
19.1083 | 3000 | 0.0 | - |
19.4268 | 3050 | 0.0 | - |
19.7452 | 3100 | 0.0001 | - |
20.0637 | 3150 | 0.0002 | - |
20.3822 | 3200 | 0.0 | - |
20.7006 | 3250 | 0.0 | - |
21.0191 | 3300 | 0.0 | - |
21.3376 | 3350 | 0.0 | - |
21.6561 | 3400 | 0.0 | - |
21.9745 | 3450 | 0.0 | - |
22.2930 | 3500 | 0.0 | - |
22.6115 | 3550 | 0.0 | - |
22.9299 | 3600 | 0.0 | - |
23.2484 | 3650 | 0.0 | - |
23.5669 | 3700 | 0.0 | - |
23.8854 | 3750 | 0.0 | - |
24.2038 | 3800 | 0.0 | - |
24.5223 | 3850 | 0.0 | - |
24.8408 | 3900 | 0.0 | - |
25.1592 | 3950 | 0.0 | - |
25.4777 | 4000 | 0.0 | - |
25.7962 | 4050 | 0.0 | - |
26.1146 | 4100 | 0.0 | - |
26.4331 | 4150 | 0.0 | - |
26.7516 | 4200 | 0.0 | - |
27.0701 | 4250 | 0.0 | - |
27.3885 | 4300 | 0.0 | - |
27.7070 | 4350 | 0.0 | - |
28.0255 | 4400 | 0.0 | - |
28.3439 | 4450 | 0.0 | - |
28.6624 | 4500 | 0.0 | - |
28.9809 | 4550 | 0.0 | - |
29.2994 | 4600 | 0.0 | - |
29.6178 | 4650 | 0.0 | - |
29.9363 | 4700 | 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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