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--- |
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base_model: klue/roberta-base |
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library_name: setfit |
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metrics: |
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- metric |
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pipeline_tag: text-classification |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: 올비고 천연 저자극 어성초 때비누 목욕 샤워 비누 어성초때비누 1개 (주) 솔루미랩 |
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- text: 폴미첼 XTG 왁스 100ml 엑스티지 11203582 옵션없음 그리드 |
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- text: 아요델 콜라겐 리프팅 아이크림 20ml 6개 옵션없음 건강드림 |
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- text: 존슨즈 콘스타치 파우더 피부 분칠 아기엉덩이 아기 옵션없음 에이치제이컴퍼니 |
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- text: '[NEW] 3CE 드롭 글로우 젤 3.8g (+글로시파우치) (도착보장) MILDER (주)난다' |
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inference: true |
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model-index: |
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- name: SetFit with klue/roberta-base |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: metric |
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value: 0.9036363636363637 |
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name: Metric |
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--- |
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# SetFit with klue/roberta-base |
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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. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [klue/roberta-base](https://huggingface.co/klue/roberta-base) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 13 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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## Evaluation |
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### Metrics |
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| Label | Metric | |
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|:--------|:-------| |
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| **all** | 0.9036 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("mini1013/master_item_bt_setfit") |
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# Run inference |
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preds = model("아요델 콜라겐 리프팅 아이크림 20ml 6개 옵션없음 건강드림") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:-------|:----| |
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| Word count | 3 | 9.8015 | 33 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 1229 | |
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| 1 | 559 | |
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| 2 | 654 | |
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| 3 | 1528 | |
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| 4 | 563 | |
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| 5 | 677 | |
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| 6 | 1157 | |
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| 7 | 563 | |
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| 8 | 1037 | |
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| 9 | 1034 | |
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| 10 | 219 | |
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| 11 | 544 | |
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| 12 | 671 | |
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### Training Hyperparameters |
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- batch_size: (512, 512) |
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- num_epochs: (20, 20) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 40 |
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- body_learning_rate: (2e-05, 2e-05) |
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- head_learning_rate: 2e-05 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:-------:|:-----:|:-------------:|:---------------:| |
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| 0.0006 | 1 | 0.3164 | - | |
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| 0.0307 | 50 | 0.3066 | - | |
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| 0.0613 | 100 | 0.2384 | - | |
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| 0.0920 | 150 | 0.226 | - | |
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| 0.1226 | 200 | 0.2162 | - | |
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| 0.1533 | 250 | 0.2202 | - | |
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| 0.1839 | 300 | 0.1973 | - | |
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| 0.2146 | 350 | 0.1818 | - | |
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| 0.2452 | 400 | 0.1629 | - | |
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| 0.2759 | 450 | 0.1734 | - | |
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| 0.3066 | 500 | 0.1624 | - | |
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| 0.3372 | 550 | 0.1435 | - | |
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| 0.3679 | 600 | 0.1433 | - | |
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| 0.3985 | 650 | 0.1259 | - | |
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| 0.4292 | 700 | 0.1175 | - | |
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| 0.4598 | 750 | 0.1201 | - | |
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| 0.4905 | 800 | 0.0958 | - | |
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| 0.5212 | 850 | 0.0938 | - | |
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| 0.5518 | 900 | 0.0784 | - | |
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| 0.5825 | 950 | 0.081 | - | |
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| 0.6131 | 1000 | 0.0673 | - | |
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| 0.6438 | 1050 | 0.0755 | - | |
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| 0.6744 | 1100 | 0.0498 | - | |
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| 0.7051 | 1150 | 0.0676 | - | |
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| 0.7357 | 1200 | 0.0474 | - | |
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| 0.7664 | 1250 | 0.0557 | - | |
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| 0.7971 | 1300 | 0.0384 | - | |
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| 0.8277 | 1350 | 0.0415 | - | |
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| 0.8584 | 1400 | 0.0415 | - | |
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| 0.8890 | 1450 | 0.0393 | - | |
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| 0.9197 | 1500 | 0.0333 | - | |
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| 0.9503 | 1550 | 0.0231 | - | |
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| 0.9810 | 1600 | 0.0162 | - | |
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| 1.0116 | 1650 | 0.024 | - | |
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| 1.0423 | 1700 | 0.0178 | - | |
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| 1.0730 | 1750 | 0.0175 | - | |
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| 1.1036 | 1800 | 0.0112 | - | |
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| 1.1343 | 1850 | 0.0109 | - | |
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| 1.1649 | 1900 | 0.0085 | - | |
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| 1.1956 | 1950 | 0.01 | - | |
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| 1.2262 | 2000 | 0.0076 | - | |
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| 1.2569 | 2050 | 0.0068 | - | |
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| 1.2876 | 2100 | 0.009 | - | |
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| 1.3182 | 2150 | 0.0066 | - | |
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| 1.3489 | 2200 | 0.0069 | - | |
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| 1.3795 | 2250 | 0.0034 | - | |
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| 1.4102 | 2300 | 0.0033 | - | |
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| 1.4408 | 2350 | 0.005 | - | |
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| 1.4715 | 2400 | 0.004 | - | |
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| 1.5021 | 2450 | 0.0014 | - | |
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| 1.5328 | 2500 | 0.0034 | - | |
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| 1.5635 | 2550 | 0.0026 | - | |
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| 1.5941 | 2600 | 0.003 | - | |
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| 1.6248 | 2650 | 0.0047 | - | |
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| 1.6554 | 2700 | 0.0019 | - | |
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| 1.6861 | 2750 | 0.0009 | - | |
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| 1.7167 | 2800 | 0.004 | - | |
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| 1.7474 | 2850 | 0.0006 | - | |
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| 1.7781 | 2900 | 0.0022 | - | |
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| 1.8087 | 2950 | 0.0033 | - | |
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| 1.8394 | 3000 | 0.0006 | - | |
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| 1.8700 | 3050 | 0.0021 | - | |
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| 1.9007 | 3100 | 0.0008 | - | |
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| 1.9313 | 3150 | 0.0037 | - | |
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| 1.9620 | 3200 | 0.0038 | - | |
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| 1.9926 | 3250 | 0.0013 | - | |
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| 2.0233 | 3300 | 0.0021 | - | |
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| 2.0540 | 3350 | 0.0008 | - | |
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| 2.0846 | 3400 | 0.0018 | - | |
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| 2.1153 | 3450 | 0.0011 | - | |
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| 2.1459 | 3500 | 0.0006 | - | |
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| 2.1766 | 3550 | 0.0003 | - | |
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| 2.2072 | 3600 | 0.0002 | - | |
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| 2.2379 | 3650 | 0.0002 | - | |
|
| 2.2685 | 3700 | 0.0001 | - | |
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| 2.2992 | 3750 | 0.0003 | - | |
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| 2.3299 | 3800 | 0.0005 | - | |
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| 2.3605 | 3850 | 0.0027 | - | |
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| 2.3912 | 3900 | 0.0004 | - | |
|
| 2.4218 | 3950 | 0.0018 | - | |
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| 2.4525 | 4000 | 0.0006 | - | |
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| 2.4831 | 4050 | 0.0002 | - | |
|
| 2.5138 | 4100 | 0.0001 | - | |
|
| 2.5445 | 4150 | 0.0008 | - | |
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| 2.5751 | 4200 | 0.0001 | - | |
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| 2.6058 | 4250 | 0.0002 | - | |
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| 2.6364 | 4300 | 0.0007 | - | |
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| 2.6671 | 4350 | 0.0002 | - | |
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| 2.6977 | 4400 | 0.0027 | - | |
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| 2.7284 | 4450 | 0.0002 | - | |
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| 2.7590 | 4500 | 0.0003 | - | |
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| 2.7897 | 4550 | 0.001 | - | |
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| 2.8204 | 4600 | 0.0001 | - | |
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| 2.8510 | 4650 | 0.0015 | - | |
|
| 2.8817 | 4700 | 0.003 | - | |
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| 2.9123 | 4750 | 0.0002 | - | |
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| 2.9430 | 4800 | 0.0019 | - | |
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| 2.9736 | 4850 | 0.0018 | - | |
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| 3.0043 | 4900 | 0.0002 | - | |
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| 3.0349 | 4950 | 0.0001 | - | |
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| 3.0656 | 5000 | 0.001 | - | |
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| 3.0963 | 5050 | 0.0004 | - | |
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| 3.1269 | 5100 | 0.0004 | - | |
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| 3.1576 | 5150 | 0.0003 | - | |
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| 3.1882 | 5200 | 0.0008 | - | |
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| 3.2189 | 5250 | 0.0007 | - | |
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| 3.2495 | 5300 | 0.0008 | - | |
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| 3.2802 | 5350 | 0.0003 | - | |
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| 3.3109 | 5400 | 0.0006 | - | |
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| 3.3415 | 5450 | 0.0047 | - | |
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| 3.3722 | 5500 | 0.0019 | - | |
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| 3.4028 | 5550 | 0.0006 | - | |
|
| 3.4335 | 5600 | 0.0002 | - | |
|
| 3.4641 | 5650 | 0.0001 | - | |
|
| 3.4948 | 5700 | 0.0001 | - | |
|
| 3.5254 | 5750 | 0.0001 | - | |
|
| 3.5561 | 5800 | 0.0001 | - | |
|
| 3.5868 | 5850 | 0.0001 | - | |
|
| 3.6174 | 5900 | 0.0014 | - | |
|
| 3.6481 | 5950 | 0.0001 | - | |
|
| 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 | - | |
|
| 3.9853 | 6500 | 0.0001 | - | |
|
| 4.0159 | 6550 | 0.0 | - | |
|
| 4.0466 | 6600 | 0.0005 | - | |
|
| 4.0773 | 6650 | 0.0004 | - | |
|
| 4.1079 | 6700 | 0.0 | - | |
|
| 4.1386 | 6750 | 0.0001 | - | |
|
| 4.1692 | 6800 | 0.0008 | - | |
|
| 4.1999 | 6850 | 0.0001 | - | |
|
| 4.2305 | 6900 | 0.0039 | - | |
|
| 4.2612 | 6950 | 0.0001 | - | |
|
| 4.2918 | 7000 | 0.0009 | - | |
|
| 4.3225 | 7050 | 0.0005 | - | |
|
| 4.3532 | 7100 | 0.0001 | - | |
|
| 4.3838 | 7150 | 0.0009 | - | |
|
| 4.4145 | 7200 | 0.0 | - | |
|
| 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 | - | |
|
| 4.5984 | 7500 | 0.0 | - | |
|
| 4.6291 | 7550 | 0.0 | - | |
|
| 4.6597 | 7600 | 0.0 | - | |
|
| 4.6904 | 7650 | 0.0005 | - | |
|
| 4.7210 | 7700 | 0.0007 | - | |
|
| 4.7517 | 7750 | 0.0 | - | |
|
| 4.7823 | 7800 | 0.0 | - | |
|
| 4.8130 | 7850 | 0.0005 | - | |
|
| 4.8437 | 7900 | 0.0001 | - | |
|
| 4.8743 | 7950 | 0.0 | - | |
|
| 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 | - | |
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| 5.2115 | 8500 | 0.0 | - | |
|
| 5.2422 | 8550 | 0.0004 | - | |
|
| 5.2728 | 8600 | 0.0001 | - | |
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| 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 | - | |
|
| 5.8553 | 9550 | 0.0001 | - | |
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| 5.8860 | 9600 | 0.0 | - | |
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| 5.9166 | 9650 | 0.0002 | - | |
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| 5.9473 | 9700 | 0.0001 | - | |
|
| 5.9779 | 9750 | 0.0 | - | |
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| 6.0086 | 9800 | 0.0 | - | |
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| 6.0392 | 9850 | 0.0 | - | |
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| 6.0699 | 9900 | 0.0 | - | |
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| 6.1006 | 9950 | 0.0 | - | |
|
| 6.1312 | 10000 | 0.0001 | - | |
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| 6.1619 | 10050 | 0.0 | - | |
|
| 6.1925 | 10100 | 0.0 | - | |
|
| 6.2232 | 10150 | 0.0003 | - | |
|
| 6.2538 | 10200 | 0.0 | - | |
|
| 6.2845 | 10250 | 0.0 | - | |
|
| 6.3151 | 10300 | 0.0 | - | |
|
| 6.3458 | 10350 | 0.0 | - | |
|
| 6.3765 | 10400 | 0.0 | - | |
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| 6.4071 | 10450 | 0.0 | - | |
|
| 6.4378 | 10500 | 0.0 | - | |
|
| 6.4684 | 10550 | 0.0001 | - | |
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| 6.4991 | 10600 | 0.0 | - | |
|
| 6.5297 | 10650 | 0.0001 | - | |
|
| 6.5604 | 10700 | 0.0003 | - | |
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| 6.5910 | 10750 | 0.0 | - | |
|
| 6.6217 | 10800 | 0.0 | - | |
|
| 6.6524 | 10850 | 0.0 | - | |
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| 6.6830 | 10900 | 0.0 | - | |
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| 6.7137 | 10950 | 0.0 | - | |
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| 6.7443 | 11000 | 0.0 | - | |
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| 6.7750 | 11050 | 0.0 | - | |
|
| 6.8056 | 11100 | 0.0001 | - | |
|
| 6.8363 | 11150 | 0.0 | - | |
|
| 6.8670 | 11200 | 0.0 | - | |
|
| 6.8976 | 11250 | 0.0 | - | |
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| 6.9283 | 11300 | 0.0 | - | |
|
| 6.9589 | 11350 | 0.0002 | - | |
|
| 6.9896 | 11400 | 0.0006 | - | |
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| 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 | - | |
|
| 7.3268 | 11950 | 0.0 | - | |
|
| 7.3574 | 12000 | 0.0 | - | |
|
| 7.3881 | 12050 | 0.0 | - | |
|
| 7.4188 | 12100 | 0.0 | - | |
|
| 7.4494 | 12150 | 0.0 | - | |
|
| 7.4801 | 12200 | 0.0002 | - | |
|
| 7.5107 | 12250 | 0.0 | - | |
|
| 7.5414 | 12300 | 0.0 | - | |
|
| 7.5720 | 12350 | 0.0001 | - | |
|
| 7.6027 | 12400 | 0.0 | - | |
|
| 7.6334 | 12450 | 0.0001 | - | |
|
| 7.6640 | 12500 | 0.0 | - | |
|
| 7.6947 | 12550 | 0.0 | - | |
|
| 7.7253 | 12600 | 0.0 | - | |
|
| 7.7560 | 12650 | 0.0 | - | |
|
| 7.7866 | 12700 | 0.0 | - | |
|
| 7.8173 | 12750 | 0.0 | - | |
|
| 7.8479 | 12800 | 0.0 | - | |
|
| 7.8786 | 12850 | 0.0 | - | |
|
| 7.9093 | 12900 | 0.0 | - | |
|
| 7.9399 | 12950 | 0.0 | - | |
|
| 7.9706 | 13000 | 0.0 | - | |
|
| 8.0012 | 13050 | 0.0001 | - | |
|
| 8.0319 | 13100 | 0.0 | - | |
|
| 8.0625 | 13150 | 0.0001 | - | |
|
| 8.0932 | 13200 | 0.0013 | - | |
|
| 8.1239 | 13250 | 0.0005 | - | |
|
| 8.1545 | 13300 | 0.0 | - | |
|
| 8.1852 | 13350 | 0.0 | - | |
|
| 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 | - | |
|
| 8.5224 | 13900 | 0.0 | - | |
|
| 8.5530 | 13950 | 0.0 | - | |
|
| 8.5837 | 14000 | 0.0 | - | |
|
| 8.6143 | 14050 | 0.0 | - | |
|
| 8.6450 | 14100 | 0.0 | - | |
|
| 8.6757 | 14150 | 0.0 | - | |
|
| 8.7063 | 14200 | 0.0 | - | |
|
| 8.7370 | 14250 | 0.0001 | - | |
|
| 8.7676 | 14300 | 0.0 | - | |
|
| 8.7983 | 14350 | 0.0 | - | |
|
| 8.8289 | 14400 | 0.0 | - | |
|
| 8.8596 | 14450 | 0.0 | - | |
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| 8.8903 | 14500 | 0.0 | - | |
|
| 8.9209 | 14550 | 0.0 | - | |
|
| 8.9516 | 14600 | 0.0 | - | |
|
| 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 | - | |
|
| 9.1355 | 14900 | 0.0 | - | |
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| 9.1662 | 14950 | 0.0 | - | |
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| 9.1968 | 15000 | 0.0 | - | |
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| 9.2275 | 15050 | 0.0001 | - | |
|
| 9.2581 | 15100 | 0.0 | - | |
|
| 9.2888 | 15150 | 0.0 | - | |
|
| 9.3194 | 15200 | 0.0 | - | |
|
| 9.3501 | 15250 | 0.0 | - | |
|
| 9.3807 | 15300 | 0.0 | - | |
|
| 9.4114 | 15350 | 0.0 | - | |
|
| 9.4421 | 15400 | 0.0 | - | |
|
| 9.4727 | 15450 | 0.0 | - | |
|
| 9.5034 | 15500 | 0.0 | - | |
|
| 9.5340 | 15550 | 0.0 | - | |
|
| 9.5647 | 15600 | 0.0 | - | |
|
| 9.5953 | 15650 | 0.0 | - | |
|
| 9.6260 | 15700 | 0.0009 | - | |
|
| 9.6567 | 15750 | 0.0 | - | |
|
| 9.6873 | 15800 | 0.0 | - | |
|
| 9.7180 | 15850 | 0.0 | - | |
|
| 9.7486 | 15900 | 0.0 | - | |
|
| 9.7793 | 15950 | 0.0 | - | |
|
| 9.8099 | 16000 | 0.0 | - | |
|
| 9.8406 | 16050 | 0.0 | - | |
|
| 9.8712 | 16100 | 0.0001 | - | |
|
| 9.9019 | 16150 | 0.0 | - | |
|
| 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 | - | |
|
| 10.8216 | 17650 | 0.0 | - | |
|
| 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 | - | |
|
| 11.2201 | 18300 | 0.0 | - | |
|
| 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 | - | |
|
| 11.4347 | 18650 | 0.0 | - | |
|
| 11.4654 | 18700 | 0.0 | - | |
|
| 11.4960 | 18750 | 0.0 | - | |
|
| 11.5267 | 18800 | 0.0 | - | |
|
| 11.5573 | 18850 | 0.0 | - | |
|
| 11.5880 | 18900 | 0.0 | - | |
|
| 11.6186 | 18950 | 0.0 | - | |
|
| 11.6493 | 19000 | 0.0 | - | |
|
| 11.6800 | 19050 | 0.0 | - | |
|
| 11.7106 | 19100 | 0.0 | - | |
|
| 11.7413 | 19150 | 0.0 | - | |
|
| 11.7719 | 19200 | 0.0 | - | |
|
| 11.8026 | 19250 | 0.0 | - | |
|
| 11.8332 | 19300 | 0.0 | - | |
|
| 11.8639 | 19350 | 0.0 | - | |
|
| 11.8945 | 19400 | 0.0 | - | |
|
| 11.9252 | 19450 | 0.0 | - | |
|
| 11.9559 | 19500 | 0.0 | - | |
|
| 11.9865 | 19550 | 0.0 | - | |
|
| 12.0172 | 19600 | 0.0 | - | |
|
| 12.0478 | 19650 | 0.0 | - | |
|
| 12.0785 | 19700 | 0.0 | - | |
|
| 12.1091 | 19750 | 0.0 | - | |
|
| 12.1398 | 19800 | 0.0 | - | |
|
| 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 | - | |
|
| 12.3544 | 20150 | 0.0 | - | |
|
| 12.3850 | 20200 | 0.0 | - | |
|
| 12.4157 | 20250 | 0.0 | - | |
|
| 12.4464 | 20300 | 0.0 | - | |
|
| 12.4770 | 20350 | 0.0 | - | |
|
| 12.5077 | 20400 | 0.0 | - | |
|
| 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 | - | |
|
| 12.9062 | 21050 | 0.0 | - | |
|
| 12.9368 | 21100 | 0.0 | - | |
|
| 12.9675 | 21150 | 0.0 | - | |
|
| 12.9982 | 21200 | 0.0 | - | |
|
| 13.0288 | 21250 | 0.0 | - | |
|
| 13.0595 | 21300 | 0.0 | - | |
|
| 13.0901 | 21350 | 0.0 | - | |
|
| 13.1208 | 21400 | 0.0 | - | |
|
| 13.1514 | 21450 | 0.0 | - | |
|
| 13.1821 | 21500 | 0.0 | - | |
|
| 13.2128 | 21550 | 0.0 | - | |
|
| 13.2434 | 21600 | 0.0 | - | |
|
| 13.2741 | 21650 | 0.0 | - | |
|
| 13.3047 | 21700 | 0.0 | - | |
|
| 13.3354 | 21750 | 0.0 | - | |
|
| 13.3660 | 21800 | 0.0 | - | |
|
| 13.3967 | 21850 | 0.0 | - | |
|
| 13.4273 | 21900 | 0.0 | - | |
|
| 13.4580 | 21950 | 0.0001 | - | |
|
| 13.4887 | 22000 | 0.0 | - | |
|
| 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 | - | |
|
| 13.6726 | 22300 | 0.0 | - | |
|
| 13.7032 | 22350 | 0.0 | - | |
|
| 13.7339 | 22400 | 0.0019 | - | |
|
| 13.7646 | 22450 | 0.0 | - | |
|
| 13.7952 | 22500 | 0.0 | - | |
|
| 13.8259 | 22550 | 0.0 | - | |
|
| 13.8565 | 22600 | 0.0 | - | |
|
| 13.8872 | 22650 | 0.0 | - | |
|
| 13.9178 | 22700 | 0.0 | - | |
|
| 13.9485 | 22750 | 0.0 | - | |
|
| 13.9792 | 22800 | 0.0 | - | |
|
| 14.0098 | 22850 | 0.0 | - | |
|
| 14.0405 | 22900 | 0.0 | - | |
|
| 14.0711 | 22950 | 0.0 | - | |
|
| 14.1018 | 23000 | 0.0 | - | |
|
| 14.1324 | 23050 | 0.0 | - | |
|
| 14.1631 | 23100 | 0.0 | - | |
|
| 14.1937 | 23150 | 0.0 | - | |
|
| 14.2244 | 23200 | 0.0 | - | |
|
| 14.2551 | 23250 | 0.0 | - | |
|
| 14.2857 | 23300 | 0.0 | - | |
|
| 14.3164 | 23350 | 0.0 | - | |
|
| 14.3470 | 23400 | 0.0 | - | |
|
| 14.3777 | 23450 | 0.0 | - | |
|
| 14.4083 | 23500 | 0.0 | - | |
|
| 14.4390 | 23550 | 0.0 | - | |
|
| 14.4697 | 23600 | 0.0 | - | |
|
| 14.5003 | 23650 | 0.0 | - | |
|
| 14.5310 | 23700 | 0.0 | - | |
|
| 14.5616 | 23750 | 0.0 | - | |
|
| 14.5923 | 23800 | 0.0 | - | |
|
| 14.6229 | 23850 | 0.0 | - | |
|
| 14.6536 | 23900 | 0.0 | - | |
|
| 14.6842 | 23950 | 0.0 | - | |
|
| 14.7149 | 24000 | 0.0 | - | |
|
| 14.7456 | 24050 | 0.0 | - | |
|
| 14.7762 | 24100 | 0.0 | - | |
|
| 14.8069 | 24150 | 0.0 | - | |
|
| 14.8375 | 24200 | 0.0 | - | |
|
| 14.8682 | 24250 | 0.0 | - | |
|
| 14.8988 | 24300 | 0.0 | - | |
|
| 14.9295 | 24350 | 0.0 | - | |
|
| 14.9601 | 24400 | 0.0 | - | |
|
| 14.9908 | 24450 | 0.0 | - | |
|
| 15.0215 | 24500 | 0.0 | - | |
|
| 15.0521 | 24550 | 0.0 | - | |
|
| 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 | - | |
|
| 15.2667 | 24900 | 0.0 | - | |
|
| 15.2974 | 24950 | 0.0 | - | |
|
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| 17.9031 | 29200 | 0.0001 | - | |
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### Framework Versions |
|
- Python: 3.10.12 |
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- SetFit: 1.1.0.dev0 |
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- Sentence Transformers: 3.1.1 |
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- Transformers: 4.45.1 |
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- PyTorch: 2.4.0+cu121 |
|
- Datasets: 2.20.0 |
|
- Tokenizers: 0.20.0 |
|
|
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## Citation |
|
|
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### BibTeX |
|
```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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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}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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