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: '[바다원] 깨끗한 돌김자반볶음 오리지널 40g x 5봉  (주)씨제이이엔엠'
  - text: 쭈꾸미사령부 매운맛 300g 3개 불타는 매운맛 원츄쟈챠
  - text: 냉동 새우 튀김 300g 6미 10미 대용량 업소용 빵가루 왕새우튀김 코코넛쉬림프 360g (30미) 주식회사 더꽃게
  - text: 잇투헤븐 팔당 불 오징어 매운 오징어 볶음 400g 쭈꾸미도사 쭈꾸미볶음 01.팔당불오징어400g 1팩 (주)잇투헤븐
  - text: CJ 명가김 파래김 4g 16입  트릴리어네어스
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: 0.8689361702127659
            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: 6 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
2.0	'훈제연어(통) 약1.1kg 냉동연어 필렛 슬라이스 칠레산 HACCP 국내가공 화이트베어 화이트베어 훈제연어슬라이스 ±1.3kg 주식회사 셀피' '안동간고등어 80g 10팩(5마리) 동의합니다_80g 10팩(5마리) 델리아마켓' '제주 국내산 손질 고등어 2KG 한팩150g이상 11-12팩 3KG(16-19팩) 효명가'
1.0	'동원F&B 양반 김치맛 김부각 50g 1개 동원F&B 양반 김치맛 김부각 50g 1개 다팔아스토어' '오뚜기 옛날 자른미역 50G 대성상사' '환길산업 섬마을 해초샐러드 냉동 해초무침 2kg 제루통상'
0.0	'Fish Tree 국물용멸치 1.3kg 케이원' 'Fish Tree 국물용 볶음용 멸치 1.3kg 1kg 뼈건강 깊은맛 육수 대멸치 좋은식감 국물용 멸치 1.3kg 유라너스' 'Fish Tree 국물용 멸치 1.3kg 이숍'
3.0	'랭킹수산 장어구이 혼합 140gx20팩(데리야끼10매콤10) -인증 제이원무역' '올반 대왕 오징어튀김 400g 나라유통' '바다愛한끼 이원일 연평도 꽃게 해물탕 760g 소스포함 2팩 (주)티알엔'
5.0	'날치알 동림 담홍 레드 800G [800G][동림]날치알(골드)(팩) 주식회사 명품씨푸드' '날치알 동림 담홍 레드 800G [800G][동림]날치알(레드)(팩) 주식회사 명품씨푸드' '날치알 동림 담홍 레드 800G [800gG[코아]날치알[골드] 주식회사 명품씨푸드'
4.0	'명인오가네 연어장 250g 명인오가네몰' '[나브연] 수제 간장 연어장 750g 덜짜게 주희종' '[나브연] 수제 간장 연어장 500g 보통 주희종'

Evaluation

Metrics

Label	Metric
all	0.8689

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_fd11")
# Run inference
preds = model("CJ 명가김 파래김 4g 16입  트릴리어네어스")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	9.1164	23

Label	Training Sample Count
0.0	50
1.0	50
2.0	50
3.0	50
4.0	50
5.0	25

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
0.0233	1	0.4609	-
1.1628	50	0.2116	-
2.3256	100	0.0876	-
3.4884	150	0.0442	-
4.6512	200	0.0254	-
5.8140	250	0.0133	-
6.9767	300	0.0252	-
8.1395	350	0.0176	-
9.3023	400	0.0116	-
10.4651	450	0.004	-
11.6279	500	0.0231	-
12.7907	550	0.0023	-
13.9535	600	0.0017	-
15.1163	650	0.0002	-
16.2791	700	0.0001	-
17.4419	750	0.0001	-
18.6047	800	0.0001	-
19.7674	850	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}
}