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---
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: 올비고 천연 저자극 어성초 때비누 목욕 샤워 비누 어성초때비누 1개 (주) 솔루미랩
- text: 폴미첼 XTG 왁스 100ml 엑스티지 11203582 옵션없음 그리드
- text: 아요델 콜라겐 리프팅 아이크림 20ml 6개 옵션없음 건강드림
- text: 존슨즈 콘스타치 파우더 피부 분칠 아기엉덩이 아기 옵션없음 에이치제이컴퍼니
- text: '[NEW] 3CE 드롭 글로우 젤 3.8g (+글로시파우치) (도착보장) MILDER (주)난다'
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: 0.9036363636363637
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:** 13 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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 |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 12 | <ul><li>'엘립스 헤어에센스 비타민 오일 바이탈리티 위드 진생 허니 오렌지 자 50ml 1022179 옵션없음 가이던스'</li><li>'아모스 녹차실감 지성샴푸 500g 컬링2x에센스150g+컬링2x에센스38g 아모스 전문샵'</li><li>'헤드앤숄더 쿨 멘솔 컨디셔닝 린스 850ml x 1개 옵션없음 지니인터네셔널 주식회사'</li></ul> |
| 1 | <ul><li>'[위글위글] 네일 발톱깎이 손톱깎이 세트 - Smile We Love Pink Smile We Love Pink 주식회사 아트쉐어'</li><li>'3종 손톱깎이세트 택1 4W51DC511E C. 구름 케이스 화이트몰'</li><li>'요고마요 YOGO 요고 망고비트 젤오프 비트 망고비트 젤오프_네일 비트홀더 케이스 증정 아이비티(IBT)'</li></ul> |
| 0 | <ul><li>'사임당 크린싱젤 120ml X 2개 (클린징 세안제) 옵션없음 바른스토어'</li><li>'페리페라 스피디 브로우 오토 펜슬, 03호 브라운, 1개 옵션없음 플래너'</li><li>'[랩시리즈](신세계 강남점)NEW 안티에이지 맥스 LS 워터로션 200ml 옵션없음 주식회사 에스에스지닷컴'</li></ul> |
| 9 | <ul><li>'비건이펙트 클린 앤 글로우 청보리 LHA 젤 클렌저 205ml 기획 (+토너패드 4eA ) 도매가능 옵션없음 앱스'</li><li>'S.NATURE 에스네이처 아쿠아 라이스 약산성 클렌징폼 160ml 8809506310680 259493 NONE 냥냥홀릭'</li><li>'히스토랩 워터맥스 밀크 클렌저 1200ml 옵션없음 히트마켓'</li></ul> |
| 6 | <ul><li>'1/1+1 스틸 마스카라 내추럴 롱래쉬 볼륨 워터프루프 메탈 마스카라 01 블랙x2 와이우'</li><li>'생로랑 GLOSS VOLUPTE LIPGLOSS 206 0.20 OZ BOX리스 와이프선물 옵션없음 남인터내셔널'</li><li>'프롬메디 초고속 속눈썹영양제 하이퍼 큐어 래쉬 세럼 10ml 하이퍼 큐어 래쉬 세럼 1개 (주)에디스'</li></ul> |
| 4 | <ul><li>'헤라 메이크업 픽서 80ml 메이크업 고정 스프레이 옵션없음 (주) 성은'</li><li>'Candy doll 캔디돌 브라이트 퓨어 베이스 옵션없음 WORLD TRADING CO., LTD'</li><li>'[국내매장판] 베네피트 프라이머 모공프라이머 더포어페셔널 모공 커버 지우개 7.5ml 프라이머 미니 + 슈퍼세터 미니 + 파우치 하이블랭크'</li></ul> |
| 8 | <ul><li>'[시효 17번 앰플] 한로 감국꽃 아이 링클 케어 앰플 20ml 옵션없음 주식회사 로시안'</li><li>'CEPOLAB 세포랩 바이오제닉 에센스 클렙스 오리지널 90% 30ml 옵션없음 주식회사 아워스'</li><li>'호주산 포포크림 30g 3개입 멀티밤 파파야오일 옵션없음 코지(KOZZY)'</li></ul> |
| 5 | <ul><li>'필리밀리 코 쉐딩브러시 857 옵션없음 뉴베이스'</li><li>'휴대용 화장품 소분 용기 여행용 공병 세트 샴푸 스프레이 거품 튜브 파스텔 핑크 친절한 이사장'</li><li>'타투커버 컨실러 흉터 방송 타투 분장 가리기 문신 점 5. 자연색 2개 아바니'</li></ul> |
| 7 | <ul><li>'디보티드 크리에이션 포춘 브론저 태닝 로션 382.7g 13온스 옵션없음 비포유'</li><li>'알롱 컨디셔닝 알로에젤 알로에 수딩젤 500ml 컨디셔닝 수딩젤 500ml 메리앤'</li><li>'헤라 선 메이트 프로텍터 50ml 옵션없음 언더커버 빌리어네어'</li></ul> |
| 3 | <ul><li>'시어 버터 드라이 스킨 핸드 크림 150ml 옵션없음 뉴글로벌'</li><li>'이탈왁스 하드 너바나 아로마틱스파 라벤더1kg 옵션없음 파인뷰티'</li><li>'에바스 블루 로즈마인 샤워코롱 185ml O 옵션없음 와이케이비 (YKB) 상사'</li></ul> |
| 10 | <ul><li>'조 말론 라임 바질 앤 만다린 카 디퓨저 카트리지 1pc 261795 상품 상세설명 참조'</li><li>'에르메스 트래블퍼퓸 3종세트 C 옵션없음 씨앤비코퍼레이션'</li><li>'룸 디퓨저 코리앤더 200ml CL13965000200 투명_F 라부르켓(L:A BRUKET AB)/(주)신세계인터내셔날, 서울특별시 강남구 도산대로 449, 소비자상담실: 1644-4490'</li></ul> |
| 11 | <ul><li>'아모스 스타일 익스프레션 홀딩 글레이즈 300ml 옵션없음 정품몰'</li><li>'Hayashi 하야시 시스템 디자인 트리플 플레이 볼류마이징 무스 7oz x 2개 2개입 유럽기준'</li><li>'새한 체리 미라클 피니쉬 수퍼하드 스프레이 240ml 옵션없음 도매백'</li></ul> |
| 2 | <ul><li>'[1+1] 물이 필요없는 디디에즈 병풀 겔 모델링팩 20회분+팩도구세트 병풀_머드 주식회사 예스나인'</li><li>'DIY 페인팅 코스프레 흰색 베니스 고양이 얼굴 종이 마스크, 도색되지 않은 10 개 옵션없음 글로젠'</li><li>'베몽테스 엑소가 필러 모델링 마스크 10회분 피부 탄력 엑소가 필러 모델링 마스크 xtt 주식회사 스킨몽(Skinmong co.,ltd.)'</li></ul> |
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.9036 |
## 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 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_item_bt_setfit")
# Run inference
preds = model("아요델 콜라겐 리프팅 아이크림 20ml 6개 옵션없음 건강드림")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 9.8015 | 33 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 1229 |
| 1 | 559 |
| 2 | 654 |
| 3 | 1528 |
| 4 | 563 |
| 5 | 677 |
| 6 | 1157 |
| 7 | 563 |
| 8 | 1037 |
| 9 | 1034 |
| 10 | 219 |
| 11 | 544 |
| 12 | 671 |
### 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 | 0.3164 | - |
| 0.0307 | 50 | 0.3066 | - |
| 0.0613 | 100 | 0.2384 | - |
| 0.0920 | 150 | 0.226 | - |
| 0.1226 | 200 | 0.2162 | - |
| 0.1533 | 250 | 0.2202 | - |
| 0.1839 | 300 | 0.1973 | - |
| 0.2146 | 350 | 0.1818 | - |
| 0.2452 | 400 | 0.1629 | - |
| 0.2759 | 450 | 0.1734 | - |
| 0.3066 | 500 | 0.1624 | - |
| 0.3372 | 550 | 0.1435 | - |
| 0.3679 | 600 | 0.1433 | - |
| 0.3985 | 650 | 0.1259 | - |
| 0.4292 | 700 | 0.1175 | - |
| 0.4598 | 750 | 0.1201 | - |
| 0.4905 | 800 | 0.0958 | - |
| 0.5212 | 850 | 0.0938 | - |
| 0.5518 | 900 | 0.0784 | - |
| 0.5825 | 950 | 0.081 | - |
| 0.6131 | 1000 | 0.0673 | - |
| 0.6438 | 1050 | 0.0755 | - |
| 0.6744 | 1100 | 0.0498 | - |
| 0.7051 | 1150 | 0.0676 | - |
| 0.7357 | 1200 | 0.0474 | - |
| 0.7664 | 1250 | 0.0557 | - |
| 0.7971 | 1300 | 0.0384 | - |
| 0.8277 | 1350 | 0.0415 | - |
| 0.8584 | 1400 | 0.0415 | - |
| 0.8890 | 1450 | 0.0393 | - |
| 0.9197 | 1500 | 0.0333 | - |
| 0.9503 | 1550 | 0.0231 | - |
| 0.9810 | 1600 | 0.0162 | - |
| 1.0116 | 1650 | 0.024 | - |
| 1.0423 | 1700 | 0.0178 | - |
| 1.0730 | 1750 | 0.0175 | - |
| 1.1036 | 1800 | 0.0112 | - |
| 1.1343 | 1850 | 0.0109 | - |
| 1.1649 | 1900 | 0.0085 | - |
| 1.1956 | 1950 | 0.01 | - |
| 1.2262 | 2000 | 0.0076 | - |
| 1.2569 | 2050 | 0.0068 | - |
| 1.2876 | 2100 | 0.009 | - |
| 1.3182 | 2150 | 0.0066 | - |
| 1.3489 | 2200 | 0.0069 | - |
| 1.3795 | 2250 | 0.0034 | - |
| 1.4102 | 2300 | 0.0033 | - |
| 1.4408 | 2350 | 0.005 | - |
| 1.4715 | 2400 | 0.004 | - |
| 1.5021 | 2450 | 0.0014 | - |
| 1.5328 | 2500 | 0.0034 | - |
| 1.5635 | 2550 | 0.0026 | - |
| 1.5941 | 2600 | 0.003 | - |
| 1.6248 | 2650 | 0.0047 | - |
| 1.6554 | 2700 | 0.0019 | - |
| 1.6861 | 2750 | 0.0009 | - |
| 1.7167 | 2800 | 0.004 | - |
| 1.7474 | 2850 | 0.0006 | - |
| 1.7781 | 2900 | 0.0022 | - |
| 1.8087 | 2950 | 0.0033 | - |
| 1.8394 | 3000 | 0.0006 | - |
| 1.8700 | 3050 | 0.0021 | - |
| 1.9007 | 3100 | 0.0008 | - |
| 1.9313 | 3150 | 0.0037 | - |
| 1.9620 | 3200 | 0.0038 | - |
| 1.9926 | 3250 | 0.0013 | - |
| 2.0233 | 3300 | 0.0021 | - |
| 2.0540 | 3350 | 0.0008 | - |
| 2.0846 | 3400 | 0.0018 | - |
| 2.1153 | 3450 | 0.0011 | - |
| 2.1459 | 3500 | 0.0006 | - |
| 2.1766 | 3550 | 0.0003 | - |
| 2.2072 | 3600 | 0.0002 | - |
| 2.2379 | 3650 | 0.0002 | - |
| 2.2685 | 3700 | 0.0001 | - |
| 2.2992 | 3750 | 0.0003 | - |
| 2.3299 | 3800 | 0.0005 | - |
| 2.3605 | 3850 | 0.0027 | - |
| 2.3912 | 3900 | 0.0004 | - |
| 2.4218 | 3950 | 0.0018 | - |
| 2.4525 | 4000 | 0.0006 | - |
| 2.4831 | 4050 | 0.0002 | - |
| 2.5138 | 4100 | 0.0001 | - |
| 2.5445 | 4150 | 0.0008 | - |
| 2.5751 | 4200 | 0.0001 | - |
| 2.6058 | 4250 | 0.0002 | - |
| 2.6364 | 4300 | 0.0007 | - |
| 2.6671 | 4350 | 0.0002 | - |
| 2.6977 | 4400 | 0.0027 | - |
| 2.7284 | 4450 | 0.0002 | - |
| 2.7590 | 4500 | 0.0003 | - |
| 2.7897 | 4550 | 0.001 | - |
| 2.8204 | 4600 | 0.0001 | - |
| 2.8510 | 4650 | 0.0015 | - |
| 2.8817 | 4700 | 0.003 | - |
| 2.9123 | 4750 | 0.0002 | - |
| 2.9430 | 4800 | 0.0019 | - |
| 2.9736 | 4850 | 0.0018 | - |
| 3.0043 | 4900 | 0.0002 | - |
| 3.0349 | 4950 | 0.0001 | - |
| 3.0656 | 5000 | 0.001 | - |
| 3.0963 | 5050 | 0.0004 | - |
| 3.1269 | 5100 | 0.0004 | - |
| 3.1576 | 5150 | 0.0003 | - |
| 3.1882 | 5200 | 0.0008 | - |
| 3.2189 | 5250 | 0.0007 | - |
| 3.2495 | 5300 | 0.0008 | - |
| 3.2802 | 5350 | 0.0003 | - |
| 3.3109 | 5400 | 0.0006 | - |
| 3.3415 | 5450 | 0.0047 | - |
| 3.3722 | 5500 | 0.0019 | - |
| 3.4028 | 5550 | 0.0006 | - |
| 3.4335 | 5600 | 0.0002 | - |
| 3.4641 | 5650 | 0.0001 | - |
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| 3.6174 | 5900 | 0.0014 | - |
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| 3.6787 | 6000 | 0.0002 | - |
| 3.7094 | 6050 | 0.0 | - |
| 3.7400 | 6100 | 0.0001 | - |
| 3.7707 | 6150 | 0.0002 | - |
| 3.8013 | 6200 | 0.0002 | - |
| 3.8320 | 6250 | 0.0017 | - |
| 3.8627 | 6300 | 0.0015 | - |
| 3.8933 | 6350 | 0.0008 | - |
| 3.9240 | 6400 | 0.0001 | - |
| 3.9546 | 6450 | 0.0003 | - |
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| 4.1386 | 6750 | 0.0001 | - |
| 4.1692 | 6800 | 0.0008 | - |
| 4.1999 | 6850 | 0.0001 | - |
| 4.2305 | 6900 | 0.0039 | - |
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| 4.4451 | 7250 | 0.0002 | - |
| 4.4758 | 7300 | 0.0 | - |
| 4.5064 | 7350 | 0.0 | - |
| 4.5371 | 7400 | 0.0 | - |
| 4.5677 | 7450 | 0.0 | - |
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| 4.9050 | 8000 | 0.0 | - |
| 4.9356 | 8050 | 0.0001 | - |
| 4.9663 | 8100 | 0.0011 | - |
| 4.9969 | 8150 | 0.0001 | - |
| 5.0276 | 8200 | 0.0006 | - |
| 5.0582 | 8250 | 0.0018 | - |
| 5.0889 | 8300 | 0.0 | - |
| 5.1196 | 8350 | 0.0001 | - |
| 5.1502 | 8400 | 0.0001 | - |
| 5.1809 | 8450 | 0.0002 | - |
| 5.2115 | 8500 | 0.0 | - |
| 5.2422 | 8550 | 0.0004 | - |
| 5.2728 | 8600 | 0.0001 | - |
| 5.3035 | 8650 | 0.0 | - |
| 5.3342 | 8700 | 0.0 | - |
| 5.3648 | 8750 | 0.0001 | - |
| 5.3955 | 8800 | 0.0001 | - |
| 5.4261 | 8850 | 0.0001 | - |
| 5.4568 | 8900 | 0.0 | - |
| 5.4874 | 8950 | 0.0001 | - |
| 5.5181 | 9000 | 0.0015 | - |
| 5.5487 | 9050 | 0.0018 | - |
| 5.5794 | 9100 | 0.0001 | - |
| 5.6101 | 9150 | 0.0001 | - |
| 5.6407 | 9200 | 0.0015 | - |
| 5.6714 | 9250 | 0.0 | - |
| 5.7020 | 9300 | 0.0004 | - |
| 5.7327 | 9350 | 0.0001 | - |
| 5.7633 | 9400 | 0.0019 | - |
| 5.7940 | 9450 | 0.0019 | - |
| 5.8246 | 9500 | 0.0001 | - |
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| 5.8860 | 9600 | 0.0 | - |
| 5.9166 | 9650 | 0.0002 | - |
| 5.9473 | 9700 | 0.0001 | - |
| 5.9779 | 9750 | 0.0 | - |
| 6.0086 | 9800 | 0.0 | - |
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| 6.0699 | 9900 | 0.0 | - |
| 6.1006 | 9950 | 0.0 | - |
| 6.1312 | 10000 | 0.0001 | - |
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| 6.1925 | 10100 | 0.0 | - |
| 6.2232 | 10150 | 0.0003 | - |
| 6.2538 | 10200 | 0.0 | - |
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| 6.3151 | 10300 | 0.0 | - |
| 6.3458 | 10350 | 0.0 | - |
| 6.3765 | 10400 | 0.0 | - |
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| 6.4684 | 10550 | 0.0001 | - |
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| 6.5297 | 10650 | 0.0001 | - |
| 6.5604 | 10700 | 0.0003 | - |
| 6.5910 | 10750 | 0.0 | - |
| 6.6217 | 10800 | 0.0 | - |
| 6.6524 | 10850 | 0.0 | - |
| 6.6830 | 10900 | 0.0 | - |
| 6.7137 | 10950 | 0.0 | - |
| 6.7443 | 11000 | 0.0 | - |
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| 6.8056 | 11100 | 0.0001 | - |
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| 6.9589 | 11350 | 0.0002 | - |
| 6.9896 | 11400 | 0.0006 | - |
| 7.0202 | 11450 | 0.0 | - |
| 7.0509 | 11500 | 0.0009 | - |
| 7.0815 | 11550 | 0.001 | - |
| 7.1122 | 11600 | 0.0003 | - |
| 7.1429 | 11650 | 0.0003 | - |
| 7.1735 | 11700 | 0.0 | - |
| 7.2042 | 11750 | 0.0 | - |
| 7.2348 | 11800 | 0.0 | - |
| 7.2655 | 11850 | 0.0 | - |
| 7.2961 | 11900 | 0.0001 | - |
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| 7.3574 | 12000 | 0.0 | - |
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| 7.4801 | 12200 | 0.0002 | - |
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| 7.5720 | 12350 | 0.0001 | - |
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| 7.6640 | 12500 | 0.0 | - |
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| 7.9706 | 13000 | 0.0 | - |
| 8.0012 | 13050 | 0.0001 | - |
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| 8.0625 | 13150 | 0.0001 | - |
| 8.0932 | 13200 | 0.0013 | - |
| 8.1239 | 13250 | 0.0005 | - |
| 8.1545 | 13300 | 0.0 | - |
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| 8.2158 | 13400 | 0.0 | - |
| 8.2465 | 13450 | 0.0 | - |
| 8.2771 | 13500 | 0.0014 | - |
| 8.3078 | 13550 | 0.0 | - |
| 8.3384 | 13600 | 0.0 | - |
| 8.3691 | 13650 | 0.0003 | - |
| 8.3998 | 13700 | 0.0 | - |
| 8.4304 | 13750 | 0.0 | - |
| 8.4611 | 13800 | 0.0 | - |
| 8.4917 | 13850 | 0.0 | - |
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| 8.5837 | 14000 | 0.0 | - |
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| 8.6757 | 14150 | 0.0 | - |
| 8.7063 | 14200 | 0.0 | - |
| 8.7370 | 14250 | 0.0001 | - |
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| 8.8289 | 14400 | 0.0 | - |
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| 8.9209 | 14550 | 0.0 | - |
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| 8.9822 | 14650 | 0.0005 | - |
| 9.0129 | 14700 | 0.0001 | - |
| 9.0435 | 14750 | 0.0001 | - |
| 9.0742 | 14800 | 0.0 | - |
| 9.1048 | 14850 | 0.0 | - |
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| 9.1968 | 15000 | 0.0 | - |
| 9.2275 | 15050 | 0.0001 | - |
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| 9.3194 | 15200 | 0.0 | - |
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| 9.5340 | 15550 | 0.0 | - |
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| 9.6260 | 15700 | 0.0009 | - |
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| 9.7486 | 15900 | 0.0 | - |
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| 9.8099 | 16000 | 0.0 | - |
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| 9.8712 | 16100 | 0.0001 | - |
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| 9.9326 | 16200 | 0.0007 | - |
| 9.9632 | 16250 | 0.0001 | - |
| 9.9939 | 16300 | 0.0002 | - |
| 10.0245 | 16350 | 0.0001 | - |
| 10.0552 | 16400 | 0.0 | - |
| 10.0858 | 16450 | 0.0 | - |
| 10.1165 | 16500 | 0.0 | - |
| 10.1471 | 16550 | 0.0 | - |
| 10.1778 | 16600 | 0.0003 | - |
| 10.2085 | 16650 | 0.0003 | - |
| 10.2391 | 16700 | 0.0 | - |
| 10.2698 | 16750 | 0.0001 | - |
| 10.3004 | 16800 | 0.0 | - |
| 10.3311 | 16850 | 0.001 | - |
| 10.3617 | 16900 | 0.0 | - |
| 10.3924 | 16950 | 0.0 | - |
| 10.4231 | 17000 | 0.0 | - |
| 10.4537 | 17050 | 0.0 | - |
| 10.4844 | 17100 | 0.0 | - |
| 10.5150 | 17150 | 0.0 | - |
| 10.5457 | 17200 | 0.0 | - |
| 10.5763 | 17250 | 0.0 | - |
| 10.6070 | 17300 | 0.0 | - |
| 10.6376 | 17350 | 0.0 | - |
| 10.6683 | 17400 | 0.0013 | - |
| 10.6990 | 17450 | 0.0 | - |
| 10.7296 | 17500 | 0.0 | - |
| 10.7603 | 17550 | 0.0 | - |
| 10.7909 | 17600 | 0.0 | - |
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| 10.8522 | 17700 | 0.0 | - |
| 10.8829 | 17750 | 0.0 | - |
| 10.9135 | 17800 | 0.0 | - |
| 10.9442 | 17850 | 0.0 | - |
| 10.9749 | 17900 | 0.0 | - |
| 11.0055 | 17950 | 0.0 | - |
| 11.0362 | 18000 | 0.0 | - |
| 11.0668 | 18050 | 0.0001 | - |
| 11.0975 | 18100 | 0.0 | - |
| 11.1281 | 18150 | 0.0 | - |
| 11.1588 | 18200 | 0.0 | - |
| 11.1895 | 18250 | 0.0 | - |
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| 11.2508 | 18350 | 0.0004 | - |
| 11.2814 | 18400 | 0.0 | - |
| 11.3121 | 18450 | 0.0 | - |
| 11.3427 | 18500 | 0.0 | - |
| 11.3734 | 18550 | 0.0 | - |
| 11.4040 | 18600 | 0.0 | - |
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| 11.5573 | 18850 | 0.0 | - |
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| 12.1704 | 19850 | 0.0 | - |
| 12.2011 | 19900 | 0.0 | - |
| 12.2318 | 19950 | 0.0 | - |
| 12.2624 | 20000 | 0.0 | - |
| 12.2931 | 20050 | 0.0 | - |
| 12.3237 | 20100 | 0.0 | - |
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| 12.3850 | 20200 | 0.0 | - |
| 12.4157 | 20250 | 0.0 | - |
| 12.4464 | 20300 | 0.0 | - |
| 12.4770 | 20350 | 0.0 | - |
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| 12.5383 | 20450 | 0.0 | - |
| 12.5690 | 20500 | 0.0 | - |
| 12.5996 | 20550 | 0.0 | - |
| 12.6303 | 20600 | 0.0004 | - |
| 12.6609 | 20650 | 0.0 | - |
| 12.6916 | 20700 | 0.0 | - |
| 12.7223 | 20750 | 0.0 | - |
| 12.7529 | 20800 | 0.0 | - |
| 12.7836 | 20850 | 0.0 | - |
| 12.8142 | 20900 | 0.0 | - |
| 12.8449 | 20950 | 0.0 | - |
| 12.8755 | 21000 | 0.0 | - |
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| 12.9368 | 21100 | 0.0 | - |
| 12.9675 | 21150 | 0.0 | - |
| 12.9982 | 21200 | 0.0 | - |
| 13.0288 | 21250 | 0.0 | - |
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| 13.1208 | 21400 | 0.0 | - |
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| 13.4580 | 21950 | 0.0001 | - |
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| 13.5193 | 22050 | 0.0003 | - |
| 13.5500 | 22100 | 0.0001 | - |
| 13.5806 | 22150 | 0.0 | - |
| 13.6113 | 22200 | 0.0 | - |
| 13.6419 | 22250 | 0.0 | - |
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| 13.7339 | 22400 | 0.0019 | - |
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| 14.1018 | 23000 | 0.0 | - |
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| 14.1937 | 23150 | 0.0 | - |
| 14.2244 | 23200 | 0.0 | - |
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| 14.7149 | 24000 | 0.0 | - |
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| 15.0828 | 24600 | 0.0 | - |
| 15.1134 | 24650 | 0.002 | - |
| 15.1441 | 24700 | 0.0 | - |
| 15.1747 | 24750 | 0.0 | - |
| 15.2054 | 24800 | 0.0 | - |
| 15.2361 | 24850 | 0.0 | - |
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| 15.4200 | 25150 | 0.0 | - |
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| 15.5120 | 25300 | 0.0 | - |
| 15.5426 | 25350 | 0.0 | - |
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| 15.6039 | 25450 | 0.0 | - |
| 15.6346 | 25500 | 0.0 | - |
| 15.6652 | 25550 | 0.0 | - |
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| 15.7265 | 25650 | 0.0 | - |
| 15.7572 | 25700 | 0.0 | - |
| 15.7879 | 25750 | 0.0 | - |
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| 15.8492 | 25850 | 0.0 | - |
| 15.8798 | 25900 | 0.0 | - |
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| 15.9411 | 26000 | 0.0 | - |
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| 16.0025 | 26100 | 0.0 | - |
| 16.0331 | 26150 | 0.0 | - |
| 16.0638 | 26200 | 0.0 | - |
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| 16.1864 | 26400 | 0.0 | - |
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| 16.5543 | 27000 | 0.0 | - |
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| 17.1674 | 28000 | 0.0 | - |
| 17.1980 | 28050 | 0.0 | - |
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| 17.3513 | 28300 | 0.0 | - |
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| 17.5353 | 28600 | 0.0 | - |
| 17.5659 | 28650 | 0.0 | - |
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| 17.6579 | 28800 | 0.0 | - |
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| 17.7192 | 28900 | 0.0 | - |
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| 17.7805 | 29000 | 0.0 | - |
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| 17.8418 | 29100 | 0.0 | - |
| 17.8725 | 29150 | 0.0 | - |
| 17.9031 | 29200 | 0.0001 | - |
| 17.9338 | 29250 | 0.0 | - |
| 17.9644 | 29300 | 0.0 | - |
| 17.9951 | 29350 | 0.0 | - |
| 18.0258 | 29400 | 0.0 | - |
| 18.0564 | 29450 | 0.0 | - |
| 18.0871 | 29500 | 0.0 | - |
| 18.1177 | 29550 | 0.0 | - |
| 18.1484 | 29600 | 0.0 | - |
| 18.1790 | 29650 | 0.0 | - |
| 18.2097 | 29700 | 0.0 | - |
| 18.2403 | 29750 | 0.0 | - |
| 18.2710 | 29800 | 0.0 | - |
| 18.3017 | 29850 | 0.0 | - |
| 18.3323 | 29900 | 0.0 | - |
| 18.3630 | 29950 | 0.0 | - |
| 18.3936 | 30000 | 0.0 | - |
| 18.4243 | 30050 | 0.0 | - |
| 18.4549 | 30100 | 0.0 | - |
| 18.4856 | 30150 | 0.0 | - |
| 18.5162 | 30200 | 0.0 | - |
| 18.5469 | 30250 | 0.0 | - |
| 18.5776 | 30300 | 0.0 | - |
| 18.6082 | 30350 | 0.0 | - |
| 18.6389 | 30400 | 0.0 | - |
| 18.6695 | 30450 | 0.0 | - |
| 18.7002 | 30500 | 0.0 | - |
| 18.7308 | 30550 | 0.0 | - |
| 18.7615 | 30600 | 0.0 | - |
| 18.7922 | 30650 | 0.0 | - |
| 18.8228 | 30700 | 0.0 | - |
| 18.8535 | 30750 | 0.0 | - |
| 18.8841 | 30800 | 0.0 | - |
| 18.9148 | 30850 | 0.0 | - |
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| 19.0067 | 31000 | 0.0 | - |
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| 19.1907 | 31300 | 0.0 | - |
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| 19.5279 | 31850 | 0.0 | - |
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| 19.5892 | 31950 | 0.0 | - |
| 19.6199 | 32000 | 0.0 | - |
| 19.6505 | 32050 | 0.0 | - |
| 19.6812 | 32100 | 0.0 | - |
| 19.7118 | 32150 | 0.0 | - |
| 19.7425 | 32200 | 0.0 | - |
| 19.7731 | 32250 | 0.0 | - |
| 19.8038 | 32300 | 0.0 | - |
| 19.8345 | 32350 | 0.0 | - |
| 19.8651 | 32400 | 0.0 | - |
| 19.8958 | 32450 | 0.0 | - |
| 19.9264 | 32500 | 0.0 | - |
| 19.9571 | 32550 | 0.0 | - |
| 19.9877 | 32600 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.45.1
- 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}
}
```
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