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mini1013/
master_cate_bt1_test_flat_top_cate

Name: master_cate_bt1_test_flat_top_cate
Availability: InStock

setfit

safetensors

roberta

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

mini1013/master_domain

finetune

model-index

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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: 4 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
3	'네일팁 실크익스텐션 311160L1720771597 티타늄금 물방울 （풀값 ） LotteOn > 뷰티 > 네일케어 > 네일케어도구 > 손톱깎이 LotteOn > 뷰티 > 네일케어 > 네일케어도구 > 손톱깎이' '엔비베베 어린이 화장품 선물세트 어린이 썬쿠션+키즈네일스티커+워시패드 1개 (#M)쿠팡 홈>뷰티>어린이화장품>세트/키트 Coupang > 뷰티 > 어린이화장품 > 세트/키트' '래쉬톡 원터치 인조 속눈썹 섹시 걸 × 3개입 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리 LotteOn > 뷰티 > 뷰티기기/소품 > 아이/브로우소품 > 속눈썹관리'
0	'오피아이 넌아세톤 리무버 빨강 30ml × 5개 (#M)쿠팡 홈>뷰티>네일>일반네일>리무버 Coupang > 뷰티 > 네일 > 일반네일 > 리무버' '[OPI][리무버] 넌아세톤리무버 30ml ssg > 뷰티 > 메이크업 > 네일 ssg > 뷰티 > 메이크업 > 네일' '포먼트 젤네일 O.4 블러쉬 뷰티 × 1개 (#M)쿠팡 홈>뷰티>네일>젤네일>컬러 젤 Coupang > 뷰티 > 네일 > 젤네일 > 컬러 젤'
2	'오피아이 프로스파 오일투고 큐티클 오일2197877 1 7.5ml x 1개2197877 1 (#M)SSG.COM/메이크업/베이스메이크업/컨실러 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 컨실러' '구찌 뷰티 [구찌] 베르니 아 옹글 하이 샤인 네일 라커 712 멜린다 그린 × 선택완료 (#M)쿠팡 홈>뷰티>네일>일반네일>컬러 매니큐어 Coupang > 뷰티 > 네일 > 일반네일 > 컬러 매니큐어' 'OPI ProSpa 각질 제거 큐티클 크림, 27ml SSG.COM/메이크업/베이스메이크업/메이크업베이스;ssg > 뷰티 > 메이크업 > 베이스메이크업 > 메이크업베이스 ssg > 뷰티 > 메이크업 > 베이스메이크업 > 메이크업베이스'
1	'르 베르니 루쥬 느와르 DepartmentLotteOn > 뷰티 > 헤어/바디 > 핸드/풋케어 > 네일케어 DepartmentLotteOn > 뷰티 > 헤어/바디 > 핸드/풋케어 > 네일케어' '베씨 베이스젤 + 탑젤 + 지브라파일 2p 세트 베이스젤, 탑젤, 지브라파일(100/150) × 1세트 LotteOn > 뷰티 > 네일 > 네일아트소품 LotteOn > 뷰티 > 네일 > 네일아트소품' 'OPI OPI Chrome Effects Nail Lacquer Top Coat CPT31 - 0.5 oz 상세내용참조 × 상세내용참조 (#M)쿠팡 홈>뷰티>메이크업>베이스 메이크업>베이스/프라이머 Coupang > 뷰티 > 메이크업 > 베이스 메이크업 > 베이스/프라이머'

Evaluation

Metrics

Label	Accuracy
all	0.5302

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_cate_bt1_test_flat_top_cate")
# Run inference
preds = model("디올 베르니 212 튀튀 LotteOn > 뷰티 > 메이크업 > 메이크업세트 LotteOn > 뷰티 > 메이크업 > 메이크업세트")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	13	22.7236	41

Label	Training Sample Count
0	49
1	50
2	50
3	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
0.0032	1	0.4603	-
0.1608	50	0.4502	-
0.3215	100	0.4315	-
0.4823	150	0.3996	-
0.6431	200	0.365	-
0.8039	250	0.2954	-
0.9646	300	0.2647	-
1.1254	350	0.2378	-
1.2862	400	0.2257	-
1.4469	450	0.2165	-
1.6077	500	0.213	-
1.7685	550	0.1999	-
1.9293	600	0.1838	-
2.0900	650	0.1614	-
2.2508	700	0.1164	-
2.4116	750	0.0553	-
2.5723	800	0.0366	-
2.7331	850	0.0279	-
2.8939	900	0.0219	-
3.0547	950	0.0166	-
3.2154	1000	0.0111	-
3.3762	1050	0.0067	-
3.5370	1100	0.0084	-
3.6977	1150	0.0066	-
3.8585	1200	0.0048	-
4.0193	1250	0.0028	-
4.1801	1300	0.0005	-
4.3408	1350	0.0003	-
4.5016	1400	0.0004	-
4.6624	1450	0.0001	-
4.8232	1500	0.0001	-
4.9839	1550	0.0001	-
5.1447	1600	0.0001	-
5.3055	1650	0.0001	-
5.4662	1700	0.0002	-
5.6270	1750	0.0	-
5.7878	1800	0.0	-
5.9486	1850	0.0	-
6.1093	1900	0.0001	-
6.2701	1950	0.0	-
6.4309	2000	0.0	-
6.5916	2050	0.0	-
6.7524	2100	0.0	-
6.9132	2150	0.0002	-
7.0740	2200	0.0002	-
7.2347	2250	0.0	-
7.3955	2300	0.0	-
7.5563	2350	0.0	-
7.7170	2400	0.0	-
7.8778	2450	0.0	-
8.0386	2500	0.0	-
8.1994	2550	0.0	-
8.3601	2600	0.0	-
8.5209	2650	0.0	-
8.6817	2700	0.0	-
8.8424	2750	0.0	-
9.0032	2800	0.0	-
9.1640	2850	0.0	-
9.3248	2900	0.0	-
9.4855	2950	0.0	-
9.6463	3000	0.0	-
9.8071	3050	0.0	-
9.9678	3100	0.0	-
10.1286	3150	0.0	-
10.2894	3200	0.0	-
10.4502	3250	0.0	-
10.6109	3300	0.0	-
10.7717	3350	0.0	-
10.9325	3400	0.0	-
11.0932	3450	0.0	-
11.2540	3500	0.0	-
11.4148	3550	0.0	-
11.5756	3600	0.0	-
11.7363	3650	0.0	-
11.8971	3700	0.0	-
12.0579	3750	0.0004	-
12.2186	3800	0.0	-
12.3794	3850	0.0001	-
12.5402	3900	0.0001	-
12.7010	3950	0.0	-
12.8617	4000	0.0001	-
13.0225	4050	0.0002	-
13.1833	4100	0.0009	-
13.3441	4150	0.0037	-
13.5048	4200	0.0025	-
13.6656	4250	0.0009	-
13.8264	4300	0.0002	-
13.9871	4350	0.0002	-
14.1479	4400	0.0	-
14.3087	4450	0.0002	-
14.4695	4500	0.0001	-
14.6302	4550	0.0004	-
14.7910	4600	0.0008	-
14.9518	4650	0.0	-
15.1125	4700	0.0	-
15.2733	4750	0.0001	-
15.4341	4800	0.0	-
15.5949	4850	0.0	-
15.7556	4900	0.0002	-
15.9164	4950	0.0	-
16.0772	5000	0.0	-
16.2379	5050	0.0001	-
16.3987	5100	0.0	-
16.5595	5150	0.0	-
16.7203	5200	0.0	-
16.8810	5250	0.0	-
17.0418	5300	0.0	-
17.2026	5350	0.0	-
17.3633	5400	0.0	-
17.5241	5450	0.0	-
17.6849	5500	0.0	-
17.8457	5550	0.0	-
18.0064	5600	0.0	-
18.1672	5650	0.0	-
18.3280	5700	0.0	-
18.4887	5750	0.0	-
18.6495	5800	0.0	-
18.8103	5850	0.0	-
18.9711	5900	0.0	-
19.1318	5950	0.0	-
19.2926	6000	0.0	-
19.4534	6050	0.0	-
19.6141	6100	0.0	-
19.7749	6150	0.0	-
19.9357	6200	0.0	-
20.0965	6250	0.0	-
20.2572	6300	0.0	-
20.4180	6350	0.0	-
20.5788	6400	0.0	-
20.7395	6450	0.0	-
20.9003	6500	0.0	-
21.0611	6550	0.0	-
21.2219	6600	0.0	-
21.3826	6650	0.0	-
21.5434	6700	0.0	-
21.7042	6750	0.0	-
21.8650	6800	0.0	-
22.0257	6850	0.0	-
22.1865	6900	0.0	-
22.3473	6950	0.0	-
22.5080	7000	0.0	-
22.6688	7050	0.0	-
22.8296	7100	0.0	-
22.9904	7150	0.0	-
23.1511	7200	0.0	-
23.3119	7250	0.0	-
23.4727	7300	0.0	-
23.6334	7350	0.0	-
23.7942	7400	0.0	-
23.9550	7450	0.0	-
24.1158	7500	0.0	-
24.2765	7550	0.0	-
24.4373	7600	0.0	-
24.5981	7650	0.0	-
24.7588	7700	0.0	-
24.9196	7750	0.0	-
25.0804	7800	0.0	-
25.2412	7850	0.0	-
25.4019	7900	0.0	-
25.5627	7950	0.0	-
25.7235	8000	0.0	-
25.8842	8050	0.0	-
26.0450	8100	0.0	-
26.2058	8150	0.0	-
26.3666	8200	0.0	-
26.5273	8250	0.0	-
26.6881	8300	0.0	-
26.8489	8350	0.0	-
27.0096	8400	0.0	-
27.1704	8450	0.0	-
27.3312	8500	0.0	-
27.4920	8550	0.0	-
27.6527	8600	0.0	-
27.8135	8650	0.0	-
27.9743	8700	0.0	-
28.1350	8750	0.0	-
28.2958	8800	0.0	-
28.4566	8850	0.0	-
28.6174	8900	0.0	-
28.7781	8950	0.0	-
28.9389	9000	0.0	-
29.0997	9050	0.0	-
29.2605	9100	0.0	-
29.4212	9150	0.0	-
29.5820	9200	0.0	-
29.7428	9250	0.0	-
29.9035	9300	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

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}
}