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--- |
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base_model: mini1013/master_domain |
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library_name: setfit |
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metrics: |
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- accuracy |
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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: 이브로쉐 모링가 리프레시 헤어 식초 400ml 1개 옵션없음 주식회사 다올연구소 |
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- text: Hair Identifier Spray for Face Shaving 2024 Skin Dermaplaning Moisturizing |
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and Care Dermaplaner 2 PC 옵션없음 젠틀스토어 |
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- text: 수앤 오리진 블랙 단백질샴푸700ml,4개 옵션없음 다부자 |
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- text: 클로란 퀴닌 에델바이스 두피 세럼 100ml 옵션없음 스루치로 유한책임회사 |
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- text: 이브로쉐 리프레쉬 헤어식초(모링가) 400ml 옵션없음 스루치로 유한책임회사 |
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inference: true |
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model-index: |
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- name: SetFit with mini1013/master_domain |
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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: 0.6042402826855123 |
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name: Accuracy |
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--- |
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# SetFit with mini1013/master_domain |
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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 [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) 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:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) |
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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:** 8 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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| 6.0 | <ul><li>'CHI 실크 인퓨전 12 Fl oz (관부가세포함) 옵션없음 제이글로벌컴퍼니'</li><li>'아모스 리페어 샤인 모이스트 에센스 100ml 옵션없음 티비'</li><li>'BAO H LAB Hair Loss Care Ampoule 바오에이치랩 탈모케어앰플 옵션없음 주식회사 바오젠'</li></ul> | |
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| 7.0 | <ul><li>'커리쉴 프레스티지 실키 3종 옵션없음 (주)커리쉴'</li><li>'미쟝센 퍼펙트 매직 스트레이트 샴푸&트리트먼트&세럼 3종 세트+트리트먼트 30ml 아모레퍼시픽'</li><li>'[르도암 공식]르도암 카멜리아 헤어 2종 세트(샴푸+트리트먼트) LEDOAM1935'</li></ul> | |
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| 0.0 | <ul><li>'실키드 검은콩 코팅 탈모펜슬™ / 머리숱앰플 두피앰플 산후탈모 서리태 비건 에센스 홈 1개 (1개월) 탈모펜슬™ 주식회사 팀오브라만차(Team of la mancha Corp.)'</li><li>'에버미라클 200ml EM 풀라무 토너 스칼프 토닉 8W98E7F225 옵션없음 파워몰'</li><li>'포티샤 모발강화 두피세럼 100ml/르네휘테르 옵션없음 롯데쇼핑(주)'</li></ul> | |
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| 4.0 | <ul><li>'[클렌징대전(클렌징밤 )] 로픈 바오밥 세라마이드LPP 프리미엄 헤어트리트먼트 베이비파우더향 1000g 옵션없음 (주)우신뷰티'</li><li>'허벌리스테 헤어 리페어세럼 150ml 1개 + 헤어 마스크 500ml - 1개 옵션없음 복슬강아지'</li><li>'[백화점 정품] 모로칸오일 오리지널 오일 트리트먼트 100ml 제3자 배송관련 개인정보활용에 동의함 버니버즈'</li></ul> | |
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| 2.0 | <ul><li>'헤드앤숄더 시트러스 레몬 샴푸 750ml 옵션없음 포에이치제이'</li><li>'아렌 일진 산성샴푸펌컬러 1000ml 옵션없음 해문인터내셔널'</li><li>'물없이쓰는샴푸 물없이머리감는 입원준비물 노워시 옵션없음 해피2데이'</li></ul> | |
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| 5.0 | <ul><li>'바이오테닉스 홈케어 매직헬프 바이-페이즈 리컨디셔너 60ml 비너스 클리닉 옵션없음 주식회사 위즈온컴퍼니'</li><li>'[바이레도] 블랑쉬 헤어퍼퓸 75ml 화이트_F 푸치코리아 유한책임회사'</li><li>'바이레도 집시 워터 헤어퍼퓸 75ml 백화점 상품 옵션없음 코코스팜'</li></ul> | |
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| 1.0 | <ul><li>'케라시스린스 퍼퓸 체리블라썸 1000ml 옵션없음 땡그리나'</li><li>'[갤러리아] [비건 NEW] 진저 스캘프 케어 대용량 컨디셔너 400ML(한화갤러리아㈜ 광교점) 옵션없음 한화갤러리아(주)'</li><li>'케라시스 스위트 앤 플라워리 퍼퓸 린스 1L 옵션없음 해피쭈몰'</li></ul> | |
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| 3.0 | <ul><li>'모비88 아데노신 특허등록 탈모토닉 볼륨업 비듬 제거 옵션없음 달이커머스'</li><li>'힐텀 어성초 맥주효모 토닉 120ml 옵션없음 현스 마켓'</li><li>'닥터포헤어 폴리젠 토닉 120ml x 2개 두피 영양공급 탈모증상완화 영양제 코스트코 옵션없음 또또상회'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.6042 | |
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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_cate_bt12_test") |
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# Run inference |
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preds = model("수앤 오리진 블랙 단백질샴푸700ml,4개 옵션없음 다부자") |
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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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## 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 | 4 | 9.25 | 21 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0.0 | 12 | |
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| 1.0 | 23 | |
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| 2.0 | 19 | |
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| 3.0 | 14 | |
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| 4.0 | 18 | |
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| 5.0 | 20 | |
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| 6.0 | 28 | |
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| 7.0 | 18 | |
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### Training Hyperparameters |
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- batch_size: (512, 512) |
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- num_epochs: (50, 50) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 60 |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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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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- l2_weight: 0.01 |
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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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| 0.0556 | 1 | 0.4865 | - | |
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| 2.7778 | 50 | 0.3392 | - | |
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| 5.5556 | 100 | 0.0584 | - | |
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| 8.3333 | 150 | 0.0087 | - | |
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| 11.1111 | 200 | 0.003 | - | |
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| 13.8889 | 250 | 0.0002 | - | |
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| 16.6667 | 300 | 0.0001 | - | |
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| 19.4444 | 350 | 0.0001 | - | |
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| 22.2222 | 400 | 0.0001 | - | |
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| 25.0 | 450 | 0.0001 | - | |
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| 27.7778 | 500 | 0.0001 | - | |
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| 30.5556 | 550 | 0.0 | - | |
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| 33.3333 | 600 | 0.0 | - | |
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| 36.1111 | 650 | 0.0 | - | |
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| 38.8889 | 700 | 0.0 | - | |
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| 41.6667 | 750 | 0.0 | - | |
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| 44.4444 | 800 | 0.0 | - | |
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| 47.2222 | 850 | 0.0 | - | |
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| 50.0 | 900 | 0.0 | - | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.1.0 |
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- Sentence Transformers: 3.3.1 |
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- 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 |
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## Citation |
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### BibTeX |
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```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}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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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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