---
base_model: klue/roberta-base
library_name: setfit
metrics:
- metric
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: ๐ฏ๊ตญ์ฐ์ ์กฐ์ฒญ๐ฏ ์์ ์ค๋๋ค ์ ๋ฌผ์ธํธ ๋ต๋กํ 18P 9์ 3์ผ ์ถ๊ณ (4์ผ~5์ผ ๋์ฐฉ์์ )_๊ฒฌ๊ณผ์คํ์
(์ค๋ฆฌ์ง๋8p+์คํ์
10p)_๊ฐ์ฌ์
๋ง์์ ์ ํฉ๋๋ค ์ํ๋ฃป(st.fruit)
- text: ํฌ์นด๋ฆฌ์ค์จํธ 245ml 1๊ฐ ์คํจํธ_๋ฝ๋ก๋ก(๋ฐํฌ๋ง) 235ML X 24๋ณ ์ฃผ์ํ์ฌ ์ก๋ฏผ
- text: ํฌ์นด๋ฆฌ์ค์จํธ 245ml 1๊ฐ ๋ฏธ๋์บ_ํฐ์คํผ ์ค์ํธ ์๋ฉ๋ฆฌ์นด๋
ธ 200ml 30๊ฐ ๋์์์ฌ
- text: ์์ดํฌ๋ฒ ์ด๋น ๋ค์ด์ดํธ ๋จ๋ฐฑ์ง ์์ดํฌ ๋ง์๋ ์์ฌ๋์ฉ ์๋จ ์์ ๋ธ๊ธฐ๋ง 750g 3+ํด๋ฉ๋ฐ์ค ๊ตฌ์ฑ_์ด์ฝ+์ค์์ฝ+๋ง์๋ฉ๋ก์ด์ฝ_ํด๋ฉ๋ฐ์ค+ํํฌ๋ณดํ
1๊ฐ+ํ์ดํธ๋ณดํ 1๊ฐ ์ฃผ์ํ์ฌ ์ฌ๋ก์ฐ๋ก์ผ
- text: ํฌ๋ด ์ด์๊ณ ์ํฐ์ง XV ์ค๋ฉ๊ฐ3 ๋ฏธ๋ ์นด์ ๋กํ
ํฌ๋ด ์ด์๊ณ ์๋ฌผ์ฑ ์ํฐ์ง ์ค๋ฉ๊ฐ3โณ (์ฃผ)ํฌํฐ๋ธ๋ดํธ๋ฆฌ์
inference: true
model-index:
- name: SetFit with klue/roberta-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: metric
value: 0.9180474602529828
name: Metric
---
# SetFit with klue/roberta-base
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. 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](https://huggingface.co/klue/roberta-base)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 22 classes
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 9.0 |
- '๋๋ณด์ํ ๊น๋ง๋ 4kg ์๊ณ ๋ ค'
- '[์ฐ๋คํด๋ฐ์ฐฌ]์ฝฉ์๋ฐ 200g ์ฃผ์ํ์ฌ ์ฐ๋คํด'
- '์๊ฒ ๋ฌด์นจ 4kg ๋์ฉ๋ ์
์์ฉ ์๋น ๋ฐ์ฐฌ ๋ฐฉ๊ฒ ์กฐ๋ฆผ (์ ) ํ๋๋ง์ฌ๋์ํ'
|
| 0.0 | - '๋๋ ๋ฐ ์ฟ๊ธฐ๋ฆ๊ฐ๋ฃจ 1kg (๋ณตํฉ) ์ฃผ์ํ์ฌ ์ผ๋ถ'
- 'ํ๋ฆฌ๋ฏธ์ ์๋ชฌ๋๊ฐ๋ฃจ 1kg 95% ์๋ชฌ๋๋ถ๋ง ์๋ชฌ๋ํ์ฐ๋ ํ๋ฆฌ๋ฏธ์ ์๋ชฌ๋๋ถ๋ง(95%) 1kg ๋นํ๋ฏผํ๋ฌ์ค'
- '์ค๋๊ธฐ ํ๊น๊ฐ๋ฃจ 2kg ๋ฆฌ์ผ์ ํต์ปดํผ๋'
|
| 2.0 | - '์๋ก์ด์ฝ์ด๋นํ ์ด์์ผ ์ผ๋ณธ๊ณผ์ ์ฟ ํฌ๋ค์ค ํ์์ฐ์ธ 9๊ฐ์
๋ํค๋ณด๋ฆฌ์ ์ฐ์ธ 12๋งค์
INCAPE CO.,LTD'
- '์์ผ๋ฆฌํจ ์ค๋ฆฌ์ง๋ ๋ฆฌํ 115gx3๋ด ์ธ 5์ข
02.์์ผ๋ฆฌํจ ์ํ ๋ฆฌํ 102g_01.์์ผ๋ฆฌํจ ์ค๋ฆฌ์ง๋ ๋ฆฌํ 115g_06.์กธ์๋ฒ์ฉ๊ป ํกํก!87g (์ฃผ)ํธ๋์กฐ์ด'
- '70g ์ถ์ต์ ๋๋์ค 1๊ฐ ์์ค์ ์ด(SJ)์กฐ์์ผํ'
|
| 12.0 | - '๋ฐ์ ์์คํธ๋ผ๋ฒ์ง ์ฌ๋ฆฌ๋ธ์ค์ผ 2L ์ 3์์ ๋ฐฐ์ก๊ด๋ จ ๊ฐ์ธ์ ๋ณด ์ด์ฉ์ ๋ํด ๋์ํจ ๋ฒ๋๋ฒ์ฆ'
- 'CJ์ ์ผ์ ๋น ๋ฐฑ์ค ์ฝฉ๊ธฐ๋ฆ 1.5L ์ฌ์กฐ ํดํ ์์ฉ์ 1.8L ์ผ์์ ํต'
- 'CJ [๋ง๋ฅ]๋ฐฑ์ค ๊ฑด๊ฐ์ ์๊ฐํ ์๋ฆฌ์ 900ml ๊ฐ์์์ฌ๋ฃ ์๋ง ๋ง์ง ๋ฏฟ๊ณ ๋จน๋ ์ฐ๋ฆฌ์ง ๊ฑด๊ฐํ ์์ฌ๋ฃ ๋ด๋ ์คํ ์ด'
|
| 6.0 | - 'ํ๊ตญ์ ํ ํ๋ฒ๋ผ์ดํ ํ๋ฒํฐ ํ๋ฒ๋ฒ ๋ฒ๋ฆฌ์ง ๋ง๊ตฌ ์์ด'
- '์คํค๋๋ฉ ๊ฐ๋ฒผ์์ง๋ ์์์ค ๋ค์ด์ดํธ 14ํฌ cissus ๋ณด์กฐ์ ์จ์์ค 15ํฌ ์ฌ๋ฐ๋ฅธ์์ ์ฃผ์ํ์ฌ'
- 'ํ๋กฌ๋ฐ์ด์ค ์ํ๋ฆฌ์นด๋ง๊ณ ์ํฐ๋ฏน์ค ๋ ๋ชฌ๋ง 2์ฃผ 14ํฌx1๋ฐ์ค ์ํ๋ฆฌ์นด ๋ง๊ณ ์ํฐ๋ฏน์ค ๋ ๋ชฌ 2์ฃผ ์ฃผ์ํ์ฌ ํ๋กฌ๋ฐ์ด์ค'
|
| 18.0 | - '์ธ์ฐ์ฃฝ์ผ 9ํ ์์ฃฝ์ผ ๊ณ ์ฒด 60g ๋นํ์ฝ์ด'
- '[์ฒญ์ ์] ์ํ์ถ 50g (๊ฒฝ์ฐ์ ) ์ฃผ์ํ์ฌ ์์ค์์ค์ง๋ท์ปด'
- '๋ธ๋๊ทธ ์ ๊ธฐ๋ ์ด๋ชจ ์ฌ๊ณผ์์ด 946ml ์ฒ์ฐ๋ฐํจ ์์ดํธ๋ฆญ ๋ฐ๋๊ทธ๋ฆฌ์ค ์ ํ์ฌ์ด๋ค๋น๋๊ฑฐ ๋๋๊ทธ๋ฆฌ์ค ์ฌ๊ณผ์์ด 1000ml ์ธ์์ด๋ค'
|
| 4.0 | - '๋์ ์๋ฐ ์์๋ญ์ฃฝ 285g ํ์ฑ์ ํต'
- 'ํธ๋๋ฒ๋ ์ซ์์นํจ๋๊ฒ500g ๋ฏธ๋๊น์ค 2์ข
480g 1+1 ๋ฏธ๋๋๊น์ค 1๊ฐ+๋ฏธ๋์นํจ๊น์ค 1๊ฐ ๋ค์ํ๋์'
- '[์ผ์ง์ด๋ฌต] 100์ฌ๊ฐ 1๋ด 320g (10๊ฐ์
) (์ฃผ)๋๋ฐฉ์ ๋'
|
| 21.0 | - '[๋ฆฌ์ค์ฐํ ] ํ ๋ผํ๋จ ์ฌ๋ผ์ด์ค 540g (๊ฒฝ์ฐ์ ) ์ฃผ์ํ์ฌ ์์ค์์ค์ง๋ท์ปด'
- '์ฌ์กฐ ์์ฌ ๋ญ๊ฐ์ด์ด 90g ์ฃผ์ํ์ฌ ํด๋ฒ๋ฆฌ๋ง์คํ'
- '์ํ ๊ฝ์น 400g ์ง์์ฐ์
'
|
| 17.0 | - '๊ณฐํ ํต๋ฐ ํธ๋ก๋ฏน์ค 450g x 4 ์ฝ์คํธ์ปท๋ชฐ'
- 'CJ ํซ์ผ์ต๋ฏน์ค 1kg ํํํฐ ์๋ง์๋ง ์์ผํํฐ ํ๋ฉ์ด๋ ๋ฐ์ญํ ๋ฐํคํธ ๋ง์ด์ปดํผ๋'
- '๊ณฐํ ํต๋ฐ ํธ๋ก๋ฏน์ค 450g x 4๊ฐ ์ฝ์คํธ์ฝ ํ๋ฒ ์ดํน ํธ๋ก ์ฌ๋ฃ ์ด์ง์ฝ์ค'
|
| 20.0 | - '๋ผ์ง ๋จธ๋ฆฟ๊ณ ๊ธฐ ์ฌ๋ผ์ด์ค ์
์์ฉ ๋จธ๋ฆฌ๊ณ ๊ธฐ 1kg ๋ผ์ง๊ตญ๋ฐฅ์ฉ ์ ๋จ ์๋๊ตญ ์ฐฝ์
์ฌ๋ฃ -์ ์ง์๋ ๋งค์ฝค ๋ฐฑํ ์ข
์๋ 500g X 2ํฉ ํธ๋์จ'
- 'ํ์ผ์ ์บ ํ๊ณ ๊ธฐ ๊ตฌ์ด 300g ๊ตญ๋ด์ฐ ์์ปท์ ๋ฌธ ๋ณด์์ ์ผ์๊ณ ๊ธฐ ์์ก ์ ๊ณจ ํ ์๋ฆฌ ์์ โ์์ก์ฉ ๋ท๋ค๋ฆฌ์ด 500g (๊ป๋ฐ๊ธฐ+๊ณ ๊ธฐ) ํ์ผ์์ฐ๊ตฌ์'
- 'ํํฐํ ๊ท์กฑ ํต๋ผ์ง๋ฐ๋ฒ ํ (5-10์ธ๋ถ) ๋งํ๊ณ ๊ธฐ ์บ ํ์์ ์ง๋ค์ด ์ถ์ฅ ๊ฒฝ์ฃผ์์ธ๋ฒ์คํฐ๋ฏธ๋_1/6ํ์ฒด ์ฃผ์ํ์ฌ ํํฐํ'
|
| 15.0 | - '์นด์นด์ค๋์ค 500g ์ค๊ตฌ์ค๊ตฌ(5959)'
- '์ ์ฃผ์ฝฉ์๋ซ๋ ํนํ๊ธฐ์ ๋ก ๋ง๋ ๋์์๊ณ ๋ง์๋ ๋ซ๋ 53 g x 7 ๊ฐ ์ ์ฃผ์ฝฉ ์๋ซ๋ 7 ๊ฐ (์ฃผ)์ผ๋ธ์์์์ค'
- '[2+2] ํด์ฐฌ๋ค ์ฌ๊ณ์ ์์ฅ 500G ๋ฉ๊ฐ๊ธ๋ก๋ฒ001'
|
| 14.0 | - '๋ชฌ์คํฐ ์๋์ง ์ธํธ๋ผ ์ํธ๋ผ 355ml 1๊ฐ ์ฌ๋ฆผ์บ_๊ฒํ ๋ ์ด ๋ ๋ชฌ 240ml 30๊ฐ ์ฃผ์ํ์ฌ ์ก๋ฏผ'
- '๋ฏธ๋ผ ํซ์ด์ฝ ์ค๋ฆฌ์ง๋ ๋ฏธ๋์คํฑ 40T+๋ณผํ ๋ฏธ๋ผ ๋ง์ผ๋10T x2๊ฐ+๋ณผํ์ฆ์ ์ ๋์ฝ๋งํธ'
- '์ผํ ์ด์ ํ์ฐ์ 1.5L 6๊ฐ ์คํจํธ_์๋
์์ํฐ ์ค๋ ์ง์๋ชฝ๋ธ๋ํฐ 500ml 12๊ฐ ์ฃผ์ํ์ฌ ์ก๋ฏผ'
|
| 10.0 | - 'ํ์ธ์ฆ ๋
ธ์๊ฐ ์ผ์ฐน (ํ์ธ์ฆ ๋ฆฌ๋์ค๋์๊ฐ ์ผ์ฐน) 369g (์ฃผ)์์ด๋ฏธ์ํ์์ค'
- '๋์ ์ฒญ์ ์ ์ฐ๋ฆฌ์์ด ์ผ์ฐน 620g / 806kcal ์๋ฆฌZIP'
- '์คํ
์ดํฌ์์ค ACE 260g ์คํ
์ดํฌ์์ค ์์์ฌ ๋งํธ ๋ค๋ผ์กฐ๋ช
'
|
| 16.0 | - '๋กํค๋ฆฟ์ง ๋ฉ์ดํ์๋ฝ 340g ์ธ์ปจ๋ ๋ฒ ์ด์ค'
- '๋ณต์์๋ฆฌ ๋ธ๊ธฐ์ผ 500g 3๊ฐ ๋ธ๊ธฐ์ผ(3๊ฐ) ๋ธ๊ธฐ์ผ+์ ๋ฌผ์ฉ ์ข
์ด๊ฐ๋ฐฉ ๋์จ๋๋์ด'
- 'Chocolate Hazelnut Spread ์ ํ์์ '
|
| 1.0 | - '[๊ณต์] ๋ฅํฐ๊ฒ์ ํค์ฆ์ด๋ฎจ 20g x 14ํฌ (4+4ํํ) 8๋ฐ์ค [47%ํ ์ธ+๋ฌด๋ฃ๋ฐฐ์ก] ์ฃผ์ํ์ฌ ๊ทธ๋ฆฟ์ธํผ์ค'
- '๋ฌ์์ ์ฐจ๊ฐ๋ฒ์ฏ ์ฐจ ์๋ฌผ 300g ์ฐจ๊ฐ๋ฒ์ฏ 300g ๋์
ํ์ฌ๋ฒ์ธ ์ฃผ์ํ์ฌ ๋์์ ์ฝ์ด'
- '์ปคํด๋๋์๊ทธ๋์ถฐ ๋ง๋์นด ํ๋ 2.27kg ์์ผ๋ํ๋ผ์ ์ฐ๋์ค ํ๋๋'
|
| 13.0 | - '์์ธ์ฐ์ ๋๋ฌผ์ฑ ์ํฌ๋ฆผ 500ml ์ปคํผ์ ์ ๋นต ์ํฌ๋ฆผ 1๊ฐ[ํฌ์ฅ ๋ฏธ์ ํ์ ๋ฐฐ์ก์ง์ฐ] ๋๋๋ฆผ'
- '์์ธ์ฐ์ ๋ฐ๋ฆฌ์คํ์ฆ ํํํฌ๋ฆผ 500g ์คํ๋ ์ดํ ๋ฐ๋ฆฌ์คํ ํํํฌ๋ฆผ 500g_์์ด์ค๋ฐ์ค ์ฌ๊ตฟ์ ํต'
- '[์์ด์ค๋ฐ์ค๋ฌด๋ฃ] ์ ์ธ DB ํํํฌ๋ฆผ 1L ๋ฌด๊ฐ๋น ํผํฉ ์ํฌ๋ฆผ ์ฌ์ดF&B'
|
| 3.0 | - 'ํ์ฐฝ์ ๊ฐ์๋ ํ์ฐฝ ์ ์๋ฐฐ์ถ 20kg ๊ณ ๋ญ์ง ํต๋ฐฐ์ถ ์ ์๋ฐฐ์ถ10kg_12-22๊ธ์์ผ ๋ฐฐ์ก์ถ๋ฐ์ผ ์ฃผ์ํ์ฌ ์ฌ๋ง๋ฃจ(Allmaru)'
- '๊น๊ถํ ์ ๋ผ๋ ํฌ๊ธฐ ๋ฐฐ์ถ ๊น์น ๊น์ฅ๊น์น 2kg ์ฅ๊ณผ ๋ง์๋ [2-3] ์ ์จ์์ฑ ๋ฌต์์ง 5kg ์ฃผ์ํ์ฌ ์ฐ์ํ๋ผ'
- '๋ณด๋ฆฌ๊น์น 3kg 5kg ์ ์ฃผ์ฐ๋ณด๋ฆฌ [100%๊ตญ๋ด์ฐ] ํ๋ฐฑ๊น์น ์ก๋์ '
|
| 8.0 | - '[์ ์ธ๊ณ ๊ท๊ฒฉ](์ ์ธ๊ณ ๋ณธ์ )๋ ๊ตด๋ฆฌ์ค์ฌ๋ฆฌ๋ธ์ค์ผ์ดํผ์์ค ์ฃผ์ํ์ฌ ์์ค์์ค์ง๋ท์ปด'
- 'ํ๋ ์๋ฐ ๋ฒ ์ด์ปจํฌ๋ฆผํ์คํ 630g 1ํฉ 630g ร 2ํฉ ์ ํฌ์ปค๋จธ์ค'
- '[์ฟ ์บฃ][์ฟ ์บฃ๋ฉ์ด๋] ๋ ์ง์ฟ ์บฃ ๋ง๋ผ๋ก์ ์ฐ๋ญ 230g X 3ํฉ ๋ํด๋'
|
| 11.0 | - '์ ๋ฏธ์ ์ญ๊พธ๋ฏธ ๋ณถ์ 450g 2์ธ ์ง๋ค์ด ์์ ์บ ํ ์๋ฆฌ ์ฃผ์ํ์ฌ ๋ฏน์ค์ค๋งฅ์ค(MIXNMAX CO.,LTD.)'
- '์๋ฒฝ์ฅ์ด ๊ตญ์ฐ ์ํฌ๋์นด ํ์ฒ ๋ฏผ๋ฌผ์ฅ์ด 1kg ์์ง ํ 750g์ด๋ด ์ด๋ฒ์ฅ์ด 1kg(์ํฌ๋์นด ์์ด 500g๋ด์ธ) ์๋ฒฝ์ปดํผ๋ ์ฃผ์ํ์ฌ'
- '์๋ง์ ๋ฐ๋ค ์์ฐ์์ธ ์ง๋ฆฌ๋ฉธ์น 1.5kg ํ์ด์ผ์ผ์ผ์ค'
|
| 7.0 | - '์ผ์ ํฐ์ปต๊น๋ฅด๋ณด๋ถ๋ญ๋ณถ์๋ฉด 105g x 4๊ฐ ๊น๋ฅด๋ณด๋ถ๋ญ๋ณถ์๋ฉด 130g x 4 ํ๋ํธ๋ํจ์ฒ'
- '[1+1 ๋๋ฉด ๊ณจ๋ผ๋ด๊ธฐ] CJ ๋์น๋ฏธ ๋ฌผ๋๋ฉด ๋น๋น๋๋ฉด ์ธ 20์ข
[5+5]ํจํฅ๋น๋น๋๋ฉด์์ค85g ์จ์ ์ด์ ์ผ์ ๋น (์ฃผ)'
- '์ฒญ์ ํฌ์ฅ๊ตญ์ 3.75KG / 37์ธ๋ถ ์์น ์๋ ๋ฉธ์น ๋น๋น๊ตญ์ ์๋ฉด (์ฃผ)์ ์ด๋น์์ค'
|
| 5.0 | - '์ฒญ๋ ์์ด์คํ์ ํํผ 3kg 40๊ณผ๋ด์ธ 05_๋๋ด ํํผ 3kg 15-20๊ณผ ๊ฐ๋ฏธ์ธ์๋์กฐํฉ๋ฒ์ธ'
- '๊ตญ์ฐ ์ฅ๋์ด์ฝฉ ์ฝฉ๋๋ฌผ์ฝฉ 1kg ์ฝ์ฝฉ ์๋ชฉํ ๊ฒ์์ฝฉ 8. ์ช์๋ณถ์ ์๋ฆฌํ๊ฐ๋ฃจ 500g ์ฃผ์ํ์ฌ ํ๊ทน์ธ ๋์
ํ์ฌ๋ฒ์ธ'
- '๋ฒ ํธ๋จ์ ๋ฌผ ๋ฐ๊ฑด์กฐ ๋ง๊ณ ๋ฒ ํธ๋จ๊ฑด๋ง๊ณ ๋ง๋ญ์ด 100G X 10๊ฐ์
์ ์์๋ฐ๋ค'
|
| 19.0 | - '๋ฐ์ฌ์๋ช
์ธ ์๋์์ฃผ ์๋ฐํ 800ml ๋ช
์ธ์๋์์ฃผ'
- 'ํด์ฐฝ์ฃผ์กฐ ํด์ฐฝ๋ง๊ฑธ๋ฆฌ 9๋ ํ๋ฆฌ๋ฏธ์ ๋ง๊ฑธ๋ฆฌ ์บ ํ ์์คํค ์ฐจ๋ฐ ์์ธ ํ์ํ
๊ธ๋จํ ๊ฐ์ฑ์ฌ์ง ๋์
ํ์ฌ๋ฒ์ธ ์ ๋ดํ ์ฃผ์ํ์ฌ ์ง๋งค์ฅ์ง์ '
- '๋ค๋๋ฐ์ด์ค ๊ฐ๋ฌด์น์์ฃผ 43๋ 375ml ํญ์๋ฆฌ์์ฑ ๋์
๋ฒ์ธ ์ฐ๋ฆฌ๋๊ฐ (์ฃผ) ์์ธ์ง์ '
|
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.9180 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the ๐ค Hub
model = SetFitModel.from_pretrained("mini1013/master_item_fd")
# Run inference
preds = model("ํฌ์นด๋ฆฌ์ค์จํธ 245ml 1๊ฐ ์คํจํธ_๋ฝ๋ก๋ก(๋ฐํฌ๋ง) 235ML X 24๋ณ ์ฃผ์ํ์ฌ ์ก๋ฏผ")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 9.1979 | 30 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 448 |
| 1.0 | 579 |
| 2.0 | 800 |
| 3.0 | 552 |
| 4.0 | 1049 |
| 5.0 | 350 |
| 6.0 | 800 |
| 7.0 | 100 |
| 8.0 | 400 |
| 9.0 | 414 |
| 10.0 | 581 |
| 11.0 | 275 |
| 12.0 | 450 |
| 13.0 | 300 |
| 14.0 | 600 |
| 15.0 | 422 |
| 16.0 | 400 |
| 17.0 | 200 |
| 18.0 | 571 |
| 19.0 | 50 |
| 20.0 | 346 |
| 21.0 | 450 |
### 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 |
|:-------:|:-----:|:-------------:|:---------------:|
| 0.0006 | 1 | 0.4086 | - |
| 0.0316 | 50 | 0.3967 | - |
| 0.0631 | 100 | 0.3705 | - |
| 0.0947 | 150 | 0.3541 | - |
| 0.1263 | 200 | 0.2971 | - |
| 0.1578 | 250 | 0.2651 | - |
| 0.1894 | 300 | 0.2404 | - |
| 0.2210 | 350 | 0.1946 | - |
| 0.2525 | 400 | 0.1848 | - |
| 0.2841 | 450 | 0.1706 | - |
| 0.3157 | 500 | 0.1394 | - |
| 0.3472 | 550 | 0.1364 | - |
| 0.3788 | 600 | 0.1178 | - |
| 0.4104 | 650 | 0.0926 | - |
| 0.4419 | 700 | 0.0949 | - |
| 0.4735 | 750 | 0.0732 | - |
| 0.5051 | 800 | 0.0806 | - |
| 0.5366 | 850 | 0.0648 | - |
| 0.5682 | 900 | 0.0707 | - |
| 0.5997 | 950 | 0.0523 | - |
| 0.6313 | 1000 | 0.0529 | - |
| 0.6629 | 1050 | 0.0491 | - |
| 0.6944 | 1100 | 0.0486 | - |
| 0.7260 | 1150 | 0.0369 | - |
| 0.7576 | 1200 | 0.0296 | - |
| 0.7891 | 1250 | 0.0303 | - |
| 0.8207 | 1300 | 0.0232 | - |
| 0.8523 | 1350 | 0.0281 | - |
| 0.8838 | 1400 | 0.0178 | - |
| 0.9154 | 1450 | 0.0346 | - |
| 0.9470 | 1500 | 0.025 | - |
| 0.9785 | 1550 | 0.0218 | - |
| 1.0101 | 1600 | 0.0335 | - |
| 1.0417 | 1650 | 0.0206 | - |
| 1.0732 | 1700 | 0.0168 | - |
| 1.1048 | 1750 | 0.0294 | - |
| 1.1364 | 1800 | 0.0219 | - |
| 1.1679 | 1850 | 0.0176 | - |
| 1.1995 | 1900 | 0.0196 | - |
| 1.2311 | 1950 | 0.0141 | - |
| 1.2626 | 2000 | 0.0031 | - |
| 1.2942 | 2050 | 0.0131 | - |
| 1.3258 | 2100 | 0.0158 | - |
| 1.3573 | 2150 | 0.0121 | - |
| 1.3889 | 2200 | 0.0088 | - |
| 1.4205 | 2250 | 0.0047 | - |
| 1.4520 | 2300 | 0.0138 | - |
| 1.4836 | 2350 | 0.0029 | - |
| 1.5152 | 2400 | 0.0063 | - |
| 1.5467 | 2450 | 0.0042 | - |
| 1.5783 | 2500 | 0.0015 | - |
| 1.6098 | 2550 | 0.0078 | - |
| 1.6414 | 2600 | 0.0014 | - |
| 1.6730 | 2650 | 0.0055 | - |
| 1.7045 | 2700 | 0.0011 | - |
| 1.7361 | 2750 | 0.0052 | - |
| 1.7677 | 2800 | 0.0018 | - |
| 1.7992 | 2850 | 0.003 | - |
| 1.8308 | 2900 | 0.004 | - |
| 1.8624 | 2950 | 0.0006 | - |
| 1.8939 | 3000 | 0.0058 | - |
| 1.9255 | 3050 | 0.0004 | - |
| 1.9571 | 3100 | 0.0028 | - |
| 1.9886 | 3150 | 0.0005 | - |
| 2.0202 | 3200 | 0.0006 | - |
| 2.0518 | 3250 | 0.0016 | - |
| 2.0833 | 3300 | 0.0036 | - |
| 2.1149 | 3350 | 0.0009 | - |
| 2.1465 | 3400 | 0.001 | - |
| 2.1780 | 3450 | 0.0007 | - |
| 2.2096 | 3500 | 0.0003 | - |
| 2.2412 | 3550 | 0.0003 | - |
| 2.2727 | 3600 | 0.0004 | - |
| 2.3043 | 3650 | 0.0002 | - |
| 2.3359 | 3700 | 0.0002 | - |
| 2.3674 | 3750 | 0.0002 | - |
| 2.3990 | 3800 | 0.003 | - |
| 2.4306 | 3850 | 0.0004 | - |
| 2.4621 | 3900 | 0.0013 | - |
| 2.4937 | 3950 | 0.0003 | - |
| 2.5253 | 4000 | 0.0002 | - |
| 2.5568 | 4050 | 0.0001 | - |
| 2.5884 | 4100 | 0.0002 | - |
| 2.6199 | 4150 | 0.0001 | - |
| 2.6515 | 4200 | 0.0003 | - |
| 2.6831 | 4250 | 0.0003 | - |
| 2.7146 | 4300 | 0.0002 | - |
| 2.7462 | 4350 | 0.0001 | - |
| 2.7778 | 4400 | 0.0018 | - |
| 2.8093 | 4450 | 0.0005 | - |
| 2.8409 | 4500 | 0.0001 | - |
| 2.8725 | 4550 | 0.0003 | - |
| 2.9040 | 4600 | 0.0001 | - |
| 2.9356 | 4650 | 0.0002 | - |
| 2.9672 | 4700 | 0.0002 | - |
| 2.9987 | 4750 | 0.0002 | - |
| 3.0303 | 4800 | 0.0018 | - |
| 3.0619 | 4850 | 0.0001 | - |
| 3.0934 | 4900 | 0.0002 | - |
| 3.125 | 4950 | 0.0001 | - |
| 3.1566 | 5000 | 0.0002 | - |
| 3.1881 | 5050 | 0.0004 | - |
| 3.2197 | 5100 | 0.0001 | - |
| 3.2513 | 5150 | 0.0001 | - |
| 3.2828 | 5200 | 0.0002 | - |
| 3.3144 | 5250 | 0.0003 | - |
| 3.3460 | 5300 | 0.0001 | - |
| 3.3775 | 5350 | 0.0003 | - |
| 3.4091 | 5400 | 0.0001 | - |
| 3.4407 | 5450 | 0.0001 | - |
| 3.4722 | 5500 | 0.0001 | - |
| 3.5038 | 5550 | 0.0003 | - |
| 3.5354 | 5600 | 0.0002 | - |
| 3.5669 | 5650 | 0.0001 | - |
| 3.5985 | 5700 | 0.0005 | - |
| 3.6301 | 5750 | 0.0003 | - |
| 3.6616 | 5800 | 0.0001 | - |
| 3.6932 | 5850 | 0.0003 | - |
| 3.7247 | 5900 | 0.0001 | - |
| 3.7563 | 5950 | 0.0001 | - |
| 3.7879 | 6000 | 0.0001 | - |
| 3.8194 | 6050 | 0.0006 | - |
| 3.8510 | 6100 | 0.0002 | - |
| 3.8826 | 6150 | 0.0004 | - |
| 3.9141 | 6200 | 0.0001 | - |
| 3.9457 | 6250 | 0.0001 | - |
| 3.9773 | 6300 | 0.0001 | - |
| 4.0088 | 6350 | 0.0002 | - |
| 4.0404 | 6400 | 0.0001 | - |
| 4.0720 | 6450 | 0.0 | - |
| 4.1035 | 6500 | 0.0001 | - |
| 4.1351 | 6550 | 0.0001 | - |
| 4.1667 | 6600 | 0.0 | - |
| 4.1982 | 6650 | 0.0 | - |
| 4.2298 | 6700 | 0.0 | - |
| 4.2614 | 6750 | 0.0 | - |
| 4.2929 | 6800 | 0.0001 | - |
| 4.3245 | 6850 | 0.0 | - |
| 4.3561 | 6900 | 0.0 | - |
| 4.3876 | 6950 | 0.0002 | - |
| 4.4192 | 7000 | 0.0007 | - |
| 4.4508 | 7050 | 0.0018 | - |
| 4.4823 | 7100 | 0.0001 | - |
| 4.5139 | 7150 | 0.0001 | - |
| 4.5455 | 7200 | 0.0001 | - |
| 4.5770 | 7250 | 0.0003 | - |
| 4.6086 | 7300 | 0.0001 | - |
| 4.6402 | 7350 | 0.0008 | - |
| 4.6717 | 7400 | 0.0001 | - |
| 4.7033 | 7450 | 0.0 | - |
| 4.7348 | 7500 | 0.0001 | - |
| 4.7664 | 7550 | 0.0001 | - |
| 4.7980 | 7600 | 0.0 | - |
| 4.8295 | 7650 | 0.0 | - |
| 4.8611 | 7700 | 0.0 | - |
| 4.8927 | 7750 | 0.0019 | - |
| 4.9242 | 7800 | 0.0 | - |
| 4.9558 | 7850 | 0.0 | - |
| 4.9874 | 7900 | 0.001 | - |
| 5.0189 | 7950 | 0.0 | - |
| 5.0505 | 8000 | 0.0011 | - |
| 5.0821 | 8050 | 0.0002 | - |
| 5.1136 | 8100 | 0.0004 | - |
| 5.1452 | 8150 | 0.0 | - |
| 5.1768 | 8200 | 0.0018 | - |
| 5.2083 | 8250 | 0.0001 | - |
| 5.2399 | 8300 | 0.0 | - |
| 5.2715 | 8350 | 0.0018 | - |
| 5.3030 | 8400 | 0.0 | - |
| 5.3346 | 8450 | 0.0005 | - |
| 5.3662 | 8500 | 0.0001 | - |
| 5.3977 | 8550 | 0.0 | - |
| 5.4293 | 8600 | 0.0 | - |
| 5.4609 | 8650 | 0.0001 | - |
| 5.4924 | 8700 | 0.0 | - |
| 5.5240 | 8750 | 0.0001 | - |
| 5.5556 | 8800 | 0.0 | - |
| 5.5871 | 8850 | 0.0001 | - |
| 5.6187 | 8900 | 0.0001 | - |
| 5.6503 | 8950 | 0.0 | - |
| 5.6818 | 9000 | 0.0001 | - |
| 5.7134 | 9050 | 0.0008 | - |
| 5.7449 | 9100 | 0.0001 | - |
| 5.7765 | 9150 | 0.0 | - |
| 5.8081 | 9200 | 0.0008 | - |
| 5.8396 | 9250 | 0.0001 | - |
| 5.8712 | 9300 | 0.0 | - |
| 5.9028 | 9350 | 0.0001 | - |
| 5.9343 | 9400 | 0.0 | - |
| 5.9659 | 9450 | 0.0 | - |
| 5.9975 | 9500 | 0.0001 | - |
| 6.0290 | 9550 | 0.0 | - |
| 6.0606 | 9600 | 0.0 | - |
| 6.0922 | 9650 | 0.0 | - |
| 6.1237 | 9700 | 0.0 | - |
| 6.1553 | 9750 | 0.0 | - |
| 6.1869 | 9800 | 0.0 | - |
| 6.2184 | 9850 | 0.0 | - |
| 6.25 | 9900 | 0.0 | - |
| 6.2816 | 9950 | 0.0 | - |
| 6.3131 | 10000 | 0.0 | - |
| 6.3447 | 10050 | 0.0 | - |
| 6.3763 | 10100 | 0.0 | - |
| 6.4078 | 10150 | 0.0 | - |
| 6.4394 | 10200 | 0.0 | - |
| 6.4710 | 10250 | 0.0 | - |
| 6.5025 | 10300 | 0.0001 | - |
| 6.5341 | 10350 | 0.0 | - |
| 6.5657 | 10400 | 0.0001 | - |
| 6.5972 | 10450 | 0.0 | - |
| 6.6288 | 10500 | 0.0 | - |
| 6.6604 | 10550 | 0.0 | - |
| 6.6919 | 10600 | 0.0 | - |
| 6.7235 | 10650 | 0.0 | - |
| 6.7551 | 10700 | 0.0 | - |
| 6.7866 | 10750 | 0.0 | - |
| 6.8182 | 10800 | 0.0 | - |
| 6.8497 | 10850 | 0.0 | - |
| 6.8813 | 10900 | 0.0002 | - |
| 6.9129 | 10950 | 0.0016 | - |
| 6.9444 | 11000 | 0.0 | - |
| 6.9760 | 11050 | 0.0002 | - |
| 7.0076 | 11100 | 0.0 | - |
| 7.0391 | 11150 | 0.0006 | - |
| 7.0707 | 11200 | 0.0 | - |
| 7.1023 | 11250 | 0.0 | - |
| 7.1338 | 11300 | 0.0 | - |
| 7.1654 | 11350 | 0.0 | - |
| 7.1970 | 11400 | 0.0 | - |
| 7.2285 | 11450 | 0.0 | - |
| 7.2601 | 11500 | 0.0 | - |
| 7.2917 | 11550 | 0.0 | - |
| 7.3232 | 11600 | 0.0 | - |
| 7.3548 | 11650 | 0.0 | - |
| 7.3864 | 11700 | 0.0 | - |
| 7.4179 | 11750 | 0.0 | - |
| 7.4495 | 11800 | 0.0 | - |
| 7.4811 | 11850 | 0.0 | - |
| 7.5126 | 11900 | 0.0 | - |
| 7.5442 | 11950 | 0.0 | - |
| 7.5758 | 12000 | 0.0 | - |
| 7.6073 | 12050 | 0.0 | - |
| 7.6389 | 12100 | 0.0 | - |
| 7.6705 | 12150 | 0.0 | - |
| 7.7020 | 12200 | 0.0 | - |
| 7.7336 | 12250 | 0.0 | - |
| 7.7652 | 12300 | 0.0 | - |
| 7.7967 | 12350 | 0.0003 | - |
| 7.8283 | 12400 | 0.0001 | - |
| 7.8598 | 12450 | 0.0 | - |
| 7.8914 | 12500 | 0.0 | - |
| 7.9230 | 12550 | 0.0 | - |
| 7.9545 | 12600 | 0.0 | - |
| 7.9861 | 12650 | 0.0 | - |
| 8.0177 | 12700 | 0.0001 | - |
| 8.0492 | 12750 | 0.0 | - |
| 8.0808 | 12800 | 0.0 | - |
| 8.1124 | 12850 | 0.0 | - |
| 8.1439 | 12900 | 0.0 | - |
| 8.1755 | 12950 | 0.0 | - |
| 8.2071 | 13000 | 0.0 | - |
| 8.2386 | 13050 | 0.0 | - |
| 8.2702 | 13100 | 0.0 | - |
| 8.3018 | 13150 | 0.0 | - |
| 8.3333 | 13200 | 0.0 | - |
| 8.3649 | 13250 | 0.0 | - |
| 8.3965 | 13300 | 0.0 | - |
| 8.4280 | 13350 | 0.0 | - |
| 8.4596 | 13400 | 0.0 | - |
| 8.4912 | 13450 | 0.0 | - |
| 8.5227 | 13500 | 0.0 | - |
| 8.5543 | 13550 | 0.0 | - |
| 8.5859 | 13600 | 0.0 | - |
| 8.6174 | 13650 | 0.0 | - |
| 8.6490 | 13700 | 0.0021 | - |
| 8.6806 | 13750 | 0.0006 | - |
| 8.7121 | 13800 | 0.0002 | - |
| 8.7437 | 13850 | 0.0013 | - |
| 8.7753 | 13900 | 0.0 | - |
| 8.8068 | 13950 | 0.0 | - |
| 8.8384 | 14000 | 0.0 | - |
| 8.8699 | 14050 | 0.0 | - |
| 8.9015 | 14100 | 0.0 | - |
| 8.9331 | 14150 | 0.0 | - |
| 8.9646 | 14200 | 0.0 | - |
| 8.9962 | 14250 | 0.0 | - |
| 9.0278 | 14300 | 0.0 | - |
| 9.0593 | 14350 | 0.0 | - |
| 9.0909 | 14400 | 0.0 | - |
| 9.1225 | 14450 | 0.0 | - |
| 9.1540 | 14500 | 0.0 | - |
| 9.1856 | 14550 | 0.0002 | - |
| 9.2172 | 14600 | 0.0 | - |
| 9.2487 | 14650 | 0.0 | - |
| 9.2803 | 14700 | 0.0 | - |
| 9.3119 | 14750 | 0.0 | - |
| 9.3434 | 14800 | 0.0 | - |
| 9.375 | 14850 | 0.0 | - |
| 9.4066 | 14900 | 0.0 | - |
| 9.4381 | 14950 | 0.0 | - |
| 9.4697 | 15000 | 0.0 | - |
| 9.5013 | 15050 | 0.0 | - |
| 9.5328 | 15100 | 0.0 | - |
| 9.5644 | 15150 | 0.0 | - |
| 9.5960 | 15200 | 0.0 | - |
| 9.6275 | 15250 | 0.0 | - |
| 9.6591 | 15300 | 0.0 | - |
| 9.6907 | 15350 | 0.0 | - |
| 9.7222 | 15400 | 0.0002 | - |
| 9.7538 | 15450 | 0.0 | - |
| 9.7854 | 15500 | 0.0 | - |
| 9.8169 | 15550 | 0.0 | - |
| 9.8485 | 15600 | 0.0 | - |
| 9.8801 | 15650 | 0.0 | - |
| 9.9116 | 15700 | 0.0001 | - |
| 9.9432 | 15750 | 0.0 | - |
| 9.9747 | 15800 | 0.0003 | - |
| 10.0063 | 15850 | 0.0 | - |
| 10.0379 | 15900 | 0.0 | - |
| 10.0694 | 15950 | 0.0001 | - |
| 10.1010 | 16000 | 0.0 | - |
| 10.1326 | 16050 | 0.0 | - |
| 10.1641 | 16100 | 0.0 | - |
| 10.1957 | 16150 | 0.0 | - |
| 10.2273 | 16200 | 0.0 | - |
| 10.2588 | 16250 | 0.0 | - |
| 10.2904 | 16300 | 0.0 | - |
| 10.3220 | 16350 | 0.0 | - |
| 10.3535 | 16400 | 0.0008 | - |
| 10.3851 | 16450 | 0.0 | - |
| 10.4167 | 16500 | 0.0 | - |
| 10.4482 | 16550 | 0.0 | - |
| 10.4798 | 16600 | 0.0 | - |
| 10.5114 | 16650 | 0.0 | - |
| 10.5429 | 16700 | 0.0 | - |
| 10.5745 | 16750 | 0.0 | - |
| 10.6061 | 16800 | 0.0 | - |
| 10.6376 | 16850 | 0.0 | - |
| 10.6692 | 16900 | 0.0009 | - |
| 10.7008 | 16950 | 0.0 | - |
| 10.7323 | 17000 | 0.0 | - |
| 10.7639 | 17050 | 0.0 | - |
| 10.7955 | 17100 | 0.0 | - |
| 10.8270 | 17150 | 0.0 | - |
| 10.8586 | 17200 | 0.0 | - |
| 10.8902 | 17250 | 0.0 | - |
| 10.9217 | 17300 | 0.0 | - |
| 10.9533 | 17350 | 0.0 | - |
| 10.9848 | 17400 | 0.0 | - |
| 11.0164 | 17450 | 0.0001 | - |
| 11.0480 | 17500 | 0.0 | - |
| 11.0795 | 17550 | 0.0 | - |
| 11.1111 | 17600 | 0.0 | - |
| 11.1427 | 17650 | 0.0 | - |
| 11.1742 | 17700 | 0.0 | - |
| 11.2058 | 17750 | 0.0 | - |
| 11.2374 | 17800 | 0.0 | - |
| 11.2689 | 17850 | 0.0 | - |
| 11.3005 | 17900 | 0.0 | - |
| 11.3321 | 17950 | 0.0 | - |
| 11.3636 | 18000 | 0.0 | - |
| 11.3952 | 18050 | 0.0 | - |
| 11.4268 | 18100 | 0.0 | - |
| 11.4583 | 18150 | 0.0 | - |
| 11.4899 | 18200 | 0.0003 | - |
| 11.5215 | 18250 | 0.0 | - |
| 11.5530 | 18300 | 0.0005 | - |
| 11.5846 | 18350 | 0.0 | - |
| 11.6162 | 18400 | 0.0 | - |
| 11.6477 | 18450 | 0.0 | - |
| 11.6793 | 18500 | 0.0 | - |
| 11.7109 | 18550 | 0.0 | - |
| 11.7424 | 18600 | 0.0 | - |
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### 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
```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}
}
```