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Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
Model card Files Community
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +1372 -0
  3. config.json +29 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +4 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +51 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +66 -0
  13. vocab.txt +0 -0
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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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: 고급누빔포인트방석커버 면방석 가구/인테리어>홈데코>쿠션/방석>방석커버
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+ - text: PVC Terrazzo 방수 자체 접착 벽지 침실 벽 거실 장식 비닐 가구 접촉 용지 가구/인테리어>DIY자재/용품>벽지>띠벽지
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+ - text: 클레마티스 오로라화병 가구/인테리어>인테리어소품>화병
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+ - text: 나비장 고가구 클래식 공예 금박 은박 콘솔 수납장 가구/인테리어>수납가구>나비장
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+ - text: 선반 받침대 테이블 책상 책장 프린터 철제 수납장 컴퓨터 정리 가구/인테리어>서재/사무용가구>책상>책상소품
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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+ library_name: setfit
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+ inference: true
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+ base_model: klue/roberta-base
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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: accuracy
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+ value: 1.0
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with klue/roberta-base
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+
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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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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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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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+
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+ ## Model Details
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+
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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:** 17 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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+
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+ ### Model Sources
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+
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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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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
65
+ | 0.0 | <ul><li>'축사 난방 판넬 송아지 애완견 개집 전기 열선 가열 -미디엄 가구/인테리어>DIY자재/용품>바닥재>필름난방'</li><li>'냉장고 시트지 리폼 필름지 싱크대 에어컨 더블 도어 스티커 가구/인테리어>DIY자재/용품>시트지>단색시트지'</li><li>'이코리브 사각형 전선 정리 몰딩 쫄대 1M 1P 학원 가구/인테리어>DIY자재/용품>몰딩'</li></ul> |
66
+ | 14.0 | <ul><li>'홈메이드 보드라운 비정형 러그 거실 카페트 매트 캐시미어 82번 LINE 가구/인테리어>카페트/러그>러그'</li><li>'테라스꾸미기 캠핑용매트 발판매트 월컴 가구/인테리어>카페트/러그>발매트'</li><li>'러그 발매트 화장실 현관 침실 주방 주방매트 대형 미끄럼방지 캠핑 PVC 가구/인테리어>카페트/러그>발매트'</li></ul> |
67
+ | 9.0 | <ul><li>'소화기 안내 아크릴 디자인 표지판 문패 가구/인테리어>인테리어소품>디자인문패'</li><li>'우체통 유럽 철재 전원주택 스탠드 우체통 인테리어 -1 2m 레터 박스 A 가구/인테리어>인테리어소품>우체통'</li><li>'빈티지 우체통 빨간 벽걸이 엔틱 카페 우편함 가구/인테리어>인테리어소품>우체통'</li></ul> |
68
+ | 13.0 | <ul><li>'베스트슬립 M2 올가닉 메달리스트 매트리스 K 가구/인테리어>침실가구>매트리스>킹매트리스'</li><li>'화장대 의자 골드가구 등받이 인테리어 화장대 의자 가구/인테리어>침실가구>화장대>화장대의자'</li><li>'플레인홈 포렐 모던 거실 협탁 580 가구/인테리어>침실가구>협탁'</li></ul> |
69
+ | 6.0 | <ul><li>'패딩 리본 키링 2컬러 가방꾸미기 가꾸 뜨개가방 부자재 가구/인테리어>수예>뜨개질>패키지'</li><li>'어린이집 신체활동 공룡도안 전신 안전거울 아기안전거울 아기 가구/인테리어>수예>퀼트/펠트>도안'</li><li>'5색재봉실 레인보우 컬러 바느질 퀼트 자수 원사 재봉 십자수 공예 가구/인테리어>수예>자수>실/바늘'</li></ul> |
70
+ | 5.0 | <ul><li>'다용도실 베란다 창고 정리 선반 세탁실 선반장 사무실 철제 키큰 가구/인테리어>수납가구>선반'</li><li>'도서 잡지 사무실 분류 보관함 회전 신문 홍보물 전단지 스탠딩 가구/인테리어>수납가구>잡지꽂이'</li><li>'프랑코홈 프랑코 리빙박스 62L 2개 SET 대용량 플라스틱 수납 정리함 FRANCO 가구/인테리어>수납가구>공간박스'</li></ul> |
71
+ | 11.0 | <ul><li>'비비엔다 포르페 냉감 아이싱 맥스 아기 쿨패드 유아특대형 슈퍼싱글 퀸 킹 가구/인테리어>침구단품>패드>싱글/슈퍼싱글패드'</li><li>'매트리스커버높은 방수 토퍼 매트리스 커버 싱글 퀸 가구/인테리어>침구단품>매트/침대커버>싱글/슈퍼싱글침대커버'</li><li>'모던하우스 리아 소프트워싱 차렵이불 Q 가구/인테리어>침구단품>차렵이불'</li></ul> |
72
+ | 1.0 | <ul><li>'데코라인 본테 높은 600 피규어 진열장 DHP006 가구/인테리어>거실가구>장식장'</li><li>'현대의료기 모짜르트 4인 SF 카우치 컴포트 황토숯볼 흙소파 가구/인테리어>거실가구>소파>흙/돌소파'</li><li>'오브민 접이식 폴딩 이동식 노트북 책상800 소파 사이드테이블 가구/인테리어>거실가구>테이블>사이드테이블'</li></ul> |
73
+ | 4.0 | <ul><li>'쿠션 던지기 리넨 베개 커버 귀여운 눈송이 케이스 새해 장식 가구/인테리어>솜류>쿠션솜'</li><li>'바디필로우 대 롱쿠션 캔디솜 더크린 가구/인테리어>솜류>쿠션솜'</li><li>'힐튼 퀼팅베개 계절베개 숙면베개 필로우 29859062 가구/인테리어>솜류>베개솜/속통>거위털/오리털베개솜'</li></ul> |
74
+ | 16.0 | <ul><li>'안고자는인형 대형 고양이 롱 바디필로우 수면 쿠션 모찌 찰떡 가구/인테리어>홈데코>쿠션/방석>대쿠션/대방석'</li><li>'냉감 애견패드 강아지 고양이 쿨매트 듀라론 2P 눕자 40X60 가구/인테리어>홈데코>쿠션/방석>대쿠션/대방석'</li><li>'3개 홀더 스마트폰 bob 거치대 썬바이저 차량용 자동차 가구/인테리어>홈데코>쿠션/방석>팔꿈치/손목쿠션'</li></ul> |
75
+ | 3.0 | <ul><li>'프린터거치대 프린터선반 다이 수납장 테이블 선반 -블랙 3단프레임 가구/인테리어>서재/사무용가구>책상>책상소품'</li><li>'카파맥스 플러스 3단 책꽂이 연두 가구/인테리어>서재/사무용가구>책꽂이'</li><li>'포밍 테이블 1800 사무용 회의실 책상 다용도 작업대 가구/인테리어>서재/사무용가구>사무/교구용가구>회의테이블'</li></ul> |
76
+ | 8.0 | <ul><li>'수영장 썬베드 야외 의자 비치 해변 호텔 펜션 테라스 리조트 침대 가구/인테리어>아웃도어가구>야외의자'</li><li>'평벤치 학교 의자 공원 운동장 야외 휴게소 병원 벤 -1 8m U자형 가구/인테리어>아웃도어가구>야외벤치'</li><li>'야외조립창고 컨테이너 농막수납장 공구보관함 잡화 유형 A 가구/인테리어>아웃도어가구>기타아웃도어가구'</li></ul> |
77
+ | 7.0 | <ul><li>'귀여운 로봇 낮은수납장 깊은 정리 이동식 3단 틈새 가구/인테리어>아동/주니어가구>수납장'</li><li>'엘레아 메리다 800 5단 서랍장 가구/인테리어>아동/주니어가구>서랍장'</li><li>'장바구니 일룸 에디키즈 장난감 정리대 가구/인테리어>아동/주니어가구>수납장'</li></ul> |
78
+ | 15.0 | <ul><li>'창안애 브러쉬 25mm 알루미늄 블라인드 30 x 30 가구/인테리어>커튼/블라인드>블라인드'</li><li>'광목로만쉐이드 작은 동물 커튼 욕실 3D 디지털 침실 구름로만쉐이드 가구/인테리어>커튼/블라인드>로만셰이드'</li><li>'에그박스 2단 슬라이딩 가구/인테리어>커튼/블라인드>로만셰이드'</li></ul> |
79
+ | 12.0 | <ul><li>'마이하우스 알러지케어 스타패치 핑크 키즈 차렵이불 풀세트 S/SS 가구/인테리어>침구세트>매트커버세트>슈퍼싱글매트커버세트'</li><li>'올리비아데코 베리메리 60수 아사 요차렵세트 Q 가구/인테리어>침구세트>요이불세트>2/3인용'</li><li>'레노마홈 스마일워싱 차렵이불 베개세트 Q 사계절 가구/인테리어>침구세트>이불베개세트>슈퍼싱글이불베개세트'</li></ul> |
80
+ | 2.0 | <ul><li>'슬립앤슬립 마스터유닛2 5분할 베개 소프트 LE1215376644 가구/인테리어>베개>메모���폼베개'</li><li>'일자목베개 쿨젤 목편한 숙면 기능성 쿨링 메모리폼 여름 베개 1개 가구/인테리어>베개>메모리폼베개'</li><li>'스패로우 스프링 필로우 메모리폼 베개 가구/인테리어>베개>메모리폼베개'</li></ul> |
81
+ | 10.0 | <ul><li>'라자가구 위드 고메 1600 주방수납장 세트 홈카페형 nk008 가구/인테리어>주방가구>주방수납장'</li><li>'레트로하우스 코케 고무나무 원목 접이식 확장형 식탁 테이블 1600 가구/인테리어>주방가구>식탁/의자>식탁테이블'</li><li>'UNKNOWN 호환 리빙코리아 리빙웰 프리미엄 오븐 석쇠 OV250 가구/인테리어>주방가구>기타주방가구'</li></ul> |
82
+
83
+ ## Evaluation
84
+
85
+ ### Metrics
86
+ | Label | Accuracy |
87
+ |:--------|:---------|
88
+ | **all** | 1.0 |
89
+
90
+ ## Uses
91
+
92
+ ### Direct Use for Inference
93
+
94
+ First install the SetFit library:
95
+
96
+ ```bash
97
+ pip install setfit
98
+ ```
99
+
100
+ Then you can load this model and run inference.
101
+
102
+ ```python
103
+ from setfit import SetFitModel
104
+
105
+ # Download from the 🤗 Hub
106
+ model = SetFitModel.from_pretrained("mini1013/master_item_fi")
107
+ # Run inference
108
+ preds = model("클레마티스 오로라화병 가구/인테리어>인테리어소품>화병")
109
+ ```
110
+
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+ <!--
112
+ ### Downstream Use
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+
114
+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
118
+ ### Out-of-Scope Use
119
+
120
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
122
+
123
+ <!--
124
+ ## Bias, Risks and Limitations
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+
126
+ *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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+
129
+ <!--
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+ ### Recommendations
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+
132
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
135
+ ## Training Details
136
+
137
+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
139
+ |:-------------|:----|:-------|:----|
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+ | Word count | 2 | 8.9412 | 24 |
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+
142
+ | Label | Training Sample Count |
143
+ |:------|:----------------------|
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+ | 0.0 | 980 |
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+ | 1.0 | 280 |
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+ | 2.0 | 133 |
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+ | 3.0 | 350 |
148
+ | 4.0 | 349 |
149
+ | 5.0 | 840 |
150
+ | 6.0 | 490 |
151
+ | 7.0 | 893 |
152
+ | 8.0 | 420 |
153
+ | 9.0 | 1516 |
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+ | 10.0 | 420 |
155
+ | 11.0 | 890 |
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+ | 12.0 | 270 |
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+ | 13.0 | 629 |
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+ | 14.0 | 402 |
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+ | 15.0 | 700 |
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+ | 16.0 | 210 |
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+
162
+ ### Training Hyperparameters
163
+ - batch_size: (256, 256)
164
+ - num_epochs: (30, 30)
165
+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 50
168
+ - body_learning_rate: (2e-05, 1e-05)
169
+ - head_learning_rate: 0.01
170
+ - loss: CosineSimilarityLoss
171
+ - distance_metric: cosine_distance
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+ - margin: 0.25
173
+ - end_to_end: False
174
+ - use_amp: False
175
+ - warmup_proportion: 0.1
176
+ - l2_weight: 0.01
177
+ - seed: 42
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+ - eval_max_steps: -1
179
+ - load_best_model_at_end: False
180
+
181
+ ### Training Results
182
+ | Epoch | Step | Training Loss | Validation Loss |
183
+ |:-------:|:-----:|:-------------:|:---------------:|
184
+ | 0.0005 | 1 | 0.4481 | - |
185
+ | 0.0262 | 50 | 0.4495 | - |
186
+ | 0.0524 | 100 | 0.426 | - |
187
+ | 0.0786 | 150 | 0.3955 | - |
188
+ | 0.1048 | 200 | 0.3284 | - |
189
+ | 0.1310 | 250 | 0.2654 | - |
190
+ | 0.1572 | 300 | 0.209 | - |
191
+ | 0.1833 | 350 | 0.1354 | - |
192
+ | 0.2095 | 400 | 0.0875 | - |
193
+ | 0.2357 | 450 | 0.0572 | - |
194
+ | 0.2619 | 500 | 0.0427 | - |
195
+ | 0.2881 | 550 | 0.0289 | - |
196
+ | 0.3143 | 600 | 0.0222 | - |
197
+ | 0.3405 | 650 | 0.0143 | - |
198
+ | 0.3667 | 700 | 0.0101 | - |
199
+ | 0.3929 | 750 | 0.0092 | - |
200
+ | 0.4191 | 800 | 0.0068 | - |
201
+ | 0.4453 | 850 | 0.0066 | - |
202
+ | 0.4715 | 900 | 0.0046 | - |
203
+ | 0.4976 | 950 | 0.0049 | - |
204
+ | 0.5238 | 1000 | 0.0046 | - |
205
+ | 0.5500 | 1050 | 0.0039 | - |
206
+ | 0.5762 | 1100 | 0.0038 | - |
207
+ | 0.6024 | 1150 | 0.0034 | - |
208
+ | 0.6286 | 1200 | 0.0029 | - |
209
+ | 0.6548 | 1250 | 0.0017 | - |
210
+ | 0.6810 | 1300 | 0.0011 | - |
211
+ | 0.7072 | 1350 | 0.0009 | - |
212
+ | 0.7334 | 1400 | 0.0006 | - |
213
+ | 0.7596 | 1450 | 0.0005 | - |
214
+ | 0.7858 | 1500 | 0.0004 | - |
215
+ | 0.8119 | 1550 | 0.0004 | - |
216
+ | 0.8381 | 1600 | 0.0003 | - |
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+ | 0.8643 | 1650 | 0.0003 | - |
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+ | 0.8905 | 1700 | 0.0003 | - |
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+ | 0.9167 | 1750 | 0.0002 | - |
220
+ | 0.9429 | 1800 | 0.0002 | - |
221
+ | 0.9691 | 1850 | 0.0002 | - |
222
+ | 0.9953 | 1900 | 0.0002 | - |
223
+ | 1.0215 | 1950 | 0.0002 | - |
224
+ | 1.0477 | 2000 | 0.0002 | - |
225
+ | 1.0739 | 2050 | 0.0002 | - |
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+ | 1.1001 | 2100 | 0.0001 | - |
227
+ | 1.1262 | 2150 | 0.0001 | - |
228
+ | 1.1524 | 2200 | 0.0001 | - |
229
+ | 1.1786 | 2250 | 0.0001 | - |
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+ | 1.2048 | 2300 | 0.0001 | - |
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+ | 1.2310 | 2350 | 0.0001 | - |
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+ | 1.2572 | 2400 | 0.0001 | - |
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+ | 1.2834 | 2450 | 0.0001 | - |
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+ | 1.3096 | 2500 | 0.0001 | - |
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+ | 1.3358 | 2550 | 0.0001 | - |
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+ | 1.3620 | 2600 | 0.0001 | - |
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+ | 1.3882 | 2650 | 0.0001 | - |
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+ | 1.4144 | 2700 | 0.0001 | - |
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+ | 1.4405 | 2750 | 0.0001 | - |
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+ | 1.4667 | 2800 | 0.0001 | - |
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+ | 1.4929 | 2850 | 0.0001 | - |
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+ | 1.5191 | 2900 | 0.0001 | - |
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+ | 1.5453 | 2950 | 0.0001 | - |
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+ | 1.5715 | 3000 | 0.0001 | - |
245
+ | 1.5977 | 3050 | 0.0001 | - |
246
+ | 1.6239 | 3100 | 0.0001 | - |
247
+ | 1.6501 | 3150 | 0.0001 | - |
248
+ | 1.6763 | 3200 | 0.0 | - |
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+ | 1.7025 | 3250 | 0.0001 | - |
250
+ | 1.7287 | 3300 | 0.0 | - |
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+ | 1.7548 | 3350 | 0.0 | - |
252
+ | 1.7810 | 3400 | 0.0 | - |
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+ | 1.8072 | 3450 | 0.0 | - |
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+ | 1.8334 | 3500 | 0.0 | - |
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+ | 1.8596 | 3550 | 0.0 | - |
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+ | 1.8858 | 3600 | 0.0 | - |
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+ | 1.9120 | 3650 | 0.0 | - |
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+ | 1.9382 | 3700 | 0.0 | - |
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+ | 1.9644 | 3750 | 0.0 | - |
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+ | 1.9906 | 3800 | 0.0 | - |
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+ | 2.0168 | 3850 | 0.0 | - |
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+ | 2.0430 | 3900 | 0.0 | - |
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+ | 2.0691 | 3950 | 0.0 | - |
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+ | 2.0953 | 4000 | 0.0 | - |
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+ | 2.1215 | 4050 | 0.0 | - |
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+ | 2.1477 | 4100 | 0.0 | - |
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+ | 2.1739 | 4150 | 0.0 | - |
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+ | 2.2001 | 4200 | 0.0 | - |
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+ | 2.2263 | 4250 | 0.0 | - |
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+ | 2.2525 | 4300 | 0.0 | - |
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+ | 2.2787 | 4350 | 0.0 | - |
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+ | 2.3049 | 4400 | 0.0 | - |
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+ | 2.3311 | 4450 | 0.0 | - |
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+ | 2.3573 | 4500 | 0.0 | - |
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+ | 2.3834 | 4550 | 0.0 | - |
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+ | 2.4096 | 4600 | 0.0 | - |
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+ | 2.4358 | 4650 | 0.0 | - |
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+ | 2.4620 | 4700 | 0.0 | - |
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+ | 2.4882 | 4750 | 0.0 | - |
280
+ | 2.5144 | 4800 | 0.0 | - |
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+ | 2.5406 | 4850 | 0.0 | - |
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+ | 2.5668 | 4900 | 0.0 | - |
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+ | 2.5930 | 4950 | 0.0 | - |
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+ | 2.6192 | 5000 | 0.0 | - |
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+ | 2.6454 | 5050 | 0.0 | - |
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+ | 2.6716 | 5100 | 0.0 | - |
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+ | 2.6977 | 5150 | 0.0 | - |
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+ | 2.7239 | 5200 | 0.0 | - |
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+ | 2.7501 | 5250 | 0.0 | - |
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+ | 2.7763 | 5300 | 0.0 | - |
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+ | 2.8025 | 5350 | 0.0 | - |
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+ | 2.8287 | 5400 | 0.0 | - |
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+ | 2.8549 | 5450 | 0.0 | - |
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+ | 2.8811 | 5500 | 0.0 | - |
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+ | 2.9073 | 5550 | 0.0 | - |
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+ | 2.9335 | 5600 | 0.0 | - |
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+ | 2.9597 | 5650 | 0.0 | - |
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+ | 2.9859 | 5700 | 0.0 | - |
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+ | 3.0120 | 5750 | 0.0 | - |
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+ | 3.0382 | 5800 | 0.0 | - |
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+ | 3.0644 | 5850 | 0.0 | - |
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+ | 3.0906 | 5900 | 0.0 | - |
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+ | 3.1168 | 5950 | 0.0 | - |
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+ | 3.1430 | 6000 | 0.0 | - |
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+ | 3.1692 | 6050 | 0.0 | - |
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+ | 3.1954 | 6100 | 0.0 | - |
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+ | 3.2216 | 6150 | 0.0 | - |
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+ | 3.2478 | 6200 | 0.0 | - |
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+ | 3.2740 | 6250 | 0.0 | - |
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+ | 3.3002 | 6300 | 0.0 | - |
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+ | 3.3263 | 6350 | 0.0 | - |
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+ | 3.3525 | 6400 | 0.0 | - |
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+ | 3.3787 | 6450 | 0.0 | - |
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+ | 3.4049 | 6500 | 0.0 | - |
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+ | 3.4311 | 6550 | 0.0 | - |
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+ | 3.4573 | 6600 | 0.0 | - |
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+ | 3.4835 | 6650 | 0.0 | - |
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+ | 3.5097 | 6700 | 0.0 | - |
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+ | 3.5359 | 6750 | 0.0 | - |
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+ | 3.5621 | 6800 | 0.0 | - |
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+ | 3.5883 | 6850 | 0.0 | - |
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+ | 3.6145 | 6900 | 0.0 | - |
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+ | 3.6406 | 6950 | 0.0 | - |
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+ | 3.6668 | 7000 | 0.0 | - |
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+ | 3.6930 | 7050 | 0.0 | - |
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+ | 3.7192 | 7100 | 0.0 | - |
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+ | 3.7454 | 7150 | 0.0 | - |
328
+ | 3.7716 | 7200 | 0.0 | - |
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+ | 3.7978 | 7250 | 0.0 | - |
330
+ | 3.8240 | 7300 | 0.0 | - |
331
+ | 3.8502 | 7350 | 0.0 | - |
332
+ | 3.8764 | 7400 | 0.0 | - |
333
+ | 3.9026 | 7450 | 0.0 | - |
334
+ | 3.9288 | 7500 | 0.0 | - |
335
+ | 3.9550 | 7550 | 0.0 | - |
336
+ | 3.9811 | 7600 | 0.0 | - |
337
+ | 4.0073 | 7650 | 0.0 | - |
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+ | 4.0335 | 7700 | 0.0 | - |
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+ | 4.0597 | 7750 | 0.0 | - |
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+ | 4.0859 | 7800 | 0.0 | - |
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+ | 4.1121 | 7850 | 0.0 | - |
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+ | 4.1383 | 7900 | 0.0 | - |
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+ | 4.1645 | 7950 | 0.0 | - |
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+ | 4.1907 | 8000 | 0.0 | - |
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+ | 4.2169 | 8050 | 0.0 | - |
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+ | 4.2431 | 8100 | 0.0 | - |
347
+ | 4.2693 | 8150 | 0.0 | - |
348
+ | 4.2954 | 8200 | 0.0 | - |
349
+ | 4.3216 | 8250 | 0.0 | - |
350
+ | 4.3478 | 8300 | 0.0 | - |
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+ | 4.3740 | 8350 | 0.0 | - |
352
+ | 4.4002 | 8400 | 0.0 | - |
353
+ | 4.4264 | 8450 | 0.0 | - |
354
+ | 4.4526 | 8500 | 0.0 | - |
355
+ | 4.4788 | 8550 | 0.0 | - |
356
+ | 4.5050 | 8600 | 0.0 | - |
357
+ | 4.5312 | 8650 | 0.0 | - |
358
+ | 4.5574 | 8700 | 0.0 | - |
359
+ | 4.5836 | 8750 | 0.0006 | - |
360
+ | 4.6097 | 8800 | 0.0006 | - |
361
+ | 4.6359 | 8850 | 0.0002 | - |
362
+ | 4.6621 | 8900 | 0.0001 | - |
363
+ | 4.6883 | 8950 | 0.0 | - |
364
+ | 4.7145 | 9000 | 0.0 | - |
365
+ | 4.7407 | 9050 | 0.0 | - |
366
+ | 4.7669 | 9100 | 0.0001 | - |
367
+ | 4.7931 | 9150 | 0.0 | - |
368
+ | 4.8193 | 9200 | 0.0 | - |
369
+ | 4.8455 | 9250 | 0.0 | - |
370
+ | 4.8717 | 9300 | 0.0 | - |
371
+ | 4.8979 | 9350 | 0.0 | - |
372
+ | 4.9240 | 9400 | 0.0 | - |
373
+ | 4.9502 | 9450 | 0.0 | - |
374
+ | 4.9764 | 9500 | 0.0 | - |
375
+ | 5.0026 | 9550 | 0.0 | - |
376
+ | 5.0288 | 9600 | 0.0 | - |
377
+ | 5.0550 | 9650 | 0.0 | - |
378
+ | 5.0812 | 9700 | 0.0 | - |
379
+ | 5.1074 | 9750 | 0.0 | - |
380
+ | 5.1336 | 9800 | 0.0 | - |
381
+ | 5.1598 | 9850 | 0.0 | - |
382
+ | 5.1860 | 9900 | 0.0 | - |
383
+ | 5.2122 | 9950 | 0.0 | - |
384
+ | 5.2383 | 10000 | 0.0 | - |
385
+ | 5.2645 | 10050 | 0.0 | - |
386
+ | 5.2907 | 10100 | 0.0 | - |
387
+ | 5.3169 | 10150 | 0.0 | - |
388
+ | 5.3431 | 10200 | 0.0 | - |
389
+ | 5.3693 | 10250 | 0.0 | - |
390
+ | 5.3955 | 10300 | 0.0 | - |
391
+ | 5.4217 | 10350 | 0.0 | - |
392
+ | 5.4479 | 10400 | 0.0 | - |
393
+ | 5.4741 | 10450 | 0.0 | - |
394
+ | 5.5003 | 10500 | 0.0 | - |
395
+ | 5.5265 | 10550 | 0.0 | - |
396
+ | 5.5526 | 10600 | 0.0 | - |
397
+ | 5.5788 | 10650 | 0.0 | - |
398
+ | 5.6050 | 10700 | 0.0 | - |
399
+ | 5.6312 | 10750 | 0.0 | - |
400
+ | 5.6574 | 10800 | 0.0 | - |
401
+ | 5.6836 | 10850 | 0.0 | - |
402
+ | 5.7098 | 10900 | 0.0 | - |
403
+ | 5.7360 | 10950 | 0.0 | - |
404
+ | 5.7622 | 11000 | 0.0 | - |
405
+ | 5.7884 | 11050 | 0.0 | - |
406
+ | 5.8146 | 11100 | 0.0 | - |
407
+ | 5.8408 | 11150 | 0.0 | - |
408
+ | 5.8669 | 11200 | 0.0 | - |
409
+ | 5.8931 | 11250 | 0.0 | - |
410
+ | 5.9193 | 11300 | 0.0 | - |
411
+ | 5.9455 | 11350 | 0.0 | - |
412
+ | 5.9717 | 11400 | 0.0 | - |
413
+ | 5.9979 | 11450 | 0.0 | - |
414
+ | 6.0241 | 11500 | 0.0 | - |
415
+ | 6.0503 | 11550 | 0.0 | - |
416
+ | 6.0765 | 11600 | 0.0 | - |
417
+ | 6.1027 | 11650 | 0.0 | - |
418
+ | 6.1289 | 11700 | 0.0 | - |
419
+ | 6.1551 | 11750 | 0.0 | - |
420
+ | 6.1812 | 11800 | 0.0 | - |
421
+ | 6.2074 | 11850 | 0.0 | - |
422
+ | 6.2336 | 11900 | 0.0 | - |
423
+ | 6.2598 | 11950 | 0.0 | - |
424
+ | 6.2860 | 12000 | 0.0 | - |
425
+ | 6.3122 | 12050 | 0.0 | - |
426
+ | 6.3384 | 12100 | 0.0 | - |
427
+ | 6.3646 | 12150 | 0.0 | - |
428
+ | 6.3908 | 12200 | 0.0 | - |
429
+ | 6.4170 | 12250 | 0.0 | - |
430
+ | 6.4432 | 12300 | 0.0 | - |
431
+ | 6.4694 | 12350 | 0.0 | - |
432
+ | 6.4955 | 12400 | 0.0 | - |
433
+ | 6.5217 | 12450 | 0.0 | - |
434
+ | 6.5479 | 12500 | 0.0 | - |
435
+ | 6.5741 | 12550 | 0.0 | - |
436
+ | 6.6003 | 12600 | 0.0 | - |
437
+ | 6.6265 | 12650 | 0.0 | - |
438
+ | 6.6527 | 12700 | 0.0 | - |
439
+ | 6.6789 | 12750 | 0.0 | - |
440
+ | 6.7051 | 12800 | 0.0 | - |
441
+ | 6.7313 | 12850 | 0.0 | - |
442
+ | 6.7575 | 12900 | 0.0 | - |
443
+ | 6.7837 | 12950 | 0.0 | - |
444
+ | 6.8098 | 13000 | 0.0 | - |
445
+ | 6.8360 | 13050 | 0.0 | - |
446
+ | 6.8622 | 13100 | 0.0 | - |
447
+ | 6.8884 | 13150 | 0.0 | - |
448
+ | 6.9146 | 13200 | 0.0 | - |
449
+ | 6.9408 | 13250 | 0.0 | - |
450
+ | 6.9670 | 13300 | 0.0 | - |
451
+ | 6.9932 | 13350 | 0.0 | - |
452
+ | 7.0194 | 13400 | 0.0 | - |
453
+ | 7.0456 | 13450 | 0.0 | - |
454
+ | 7.0718 | 13500 | 0.0 | - |
455
+ | 7.0980 | 13550 | 0.0 | - |
456
+ | 7.1241 | 13600 | 0.0 | - |
457
+ | 7.1503 | 13650 | 0.0 | - |
458
+ | 7.1765 | 13700 | 0.0 | - |
459
+ | 7.2027 | 13750 | 0.0 | - |
460
+ | 7.2289 | 13800 | 0.0 | - |
461
+ | 7.2551 | 13850 | 0.0 | - |
462
+ | 7.2813 | 13900 | 0.0 | - |
463
+ | 7.3075 | 13950 | 0.0 | - |
464
+ | 7.3337 | 14000 | 0.0 | - |
465
+ | 7.3599 | 14050 | 0.0 | - |
466
+ | 7.3861 | 14100 | 0.0 | - |
467
+ | 7.4123 | 14150 | 0.0 | - |
468
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+
1331
+ ### Framework Versions
1332
+ - Python: 3.10.12
1333
+ - SetFit: 1.1.0
1334
+ - Sentence Transformers: 3.3.1
1335
+ - Transformers: 4.44.2
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+ - PyTorch: 2.2.0a0+81ea7a4
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+ - Datasets: 3.2.0
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+ - Tokenizers: 0.19.1
1339
+
1340
+ ## Citation
1341
+
1342
+ ### BibTeX
1343
+ ```bibtex
1344
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
1345
+ doi = {10.48550/ARXIV.2209.11055},
1346
+ url = {https://arxiv.org/abs/2209.11055},
1347
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
1348
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
1349
+ title = {Efficient Few-Shot Learning Without Prompts},
1350
+ publisher = {arXiv},
1351
+ year = {2022},
1352
+ copyright = {Creative Commons Attribution 4.0 International}
1353
+ }
1354
+ ```
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+
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+ <!--
1357
+ ## Glossary
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+
1359
+ *Clearly define terms in order to be accessible across audiences.*
1360
+ -->
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+
1362
+ <!--
1363
+ ## Model Card Authors
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+
1365
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
1367
+
1368
+ <!--
1369
+ ## Model Card Contact
1370
+
1371
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1372
+ -->
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