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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: 진주 선 마린 스프레이 180ml 펄 제이엠솔루션 청광 옵션없음 사구있오 |
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- text: 핑크 레오파드 호피무늬 태닝비키니 바디프로필 몸매 - 표범 표범_M 세일러7 |
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- text: 제이엠솔루션 180ml 스프레이 선 진주 마린 펄 청광 옵션없음 뿔샵 |
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- text: 제주온 큐테라 풋귤 알로에베라 수딩젤 200ml × 1개 구대연구소 |
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- text: BALIBODY SPF 6 카카오 태닝 오일 100ml x 5개 옵션없음 시연마켓 |
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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: 0.7882599580712788 |
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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:** 7 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>'디자이너스킨 올 액세스 태닝로션 여름 크림 옵션없음 라이브프롬잇'</li><li>'코스노리 올웨이즈 핏 바 비키니 코드 바디젤 263003 J 옵션없음 제이피샵온(JPshopon)'</li><li>'Bronzer Tanning Lotion by 디자이너스킨 400ml 옵션없음 라이브프롬잇'</li></ul> | |
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| 5.0 | <ul><li>'썬범 프리미엄 하이드레이팅 애프터 썬 젤 쿨다운 237ml - 썬 젤 쿨다운 237ml x 2개_없음 나대몰'</li><li>'Allurials 알로에 베라 젤 354ml 옵션없음 해외쇼핑 잘왔네'</li><li>'웰더마 지플러스 쿨링 에센스 알로에베라 수딩젤 120g × 3개 120g × 3개 하이블리스'</li></ul> | |
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| 1.0 | <ul><li>'SVR 선시큐어 울트라 라이트 인비저블 선 스프레이 SPF50 200ml 옵션없음 주식회사하늘'</li><li>'선스프레이 청광 진주 마린 제이엠솔루션 선 스프레이 펄 옵션없음 유토피아'</li><li>'청광 선 180ml 스프레이 제이엠솔루션 마린 진주 펄 옵션없음 포뿔샵'</li></ul> | |
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| 0.0 | <ul><li>'큐어 쿨링 선스틱 23g 2개 옵션없음 씨유니'</li><li>'라운드랩 자작나무 수분 선스틱 19g SPF 50+ 자작 수분 선스틱 19g 원스원컴퍼니'</li><li>'AHC 내추럴 퍼펙션 더블 쉴드 선스틱 (파랑)14g 골프 등산 워터프루프 썬크림 옵션없음 위얼드(WEALD)'</li></ul> | |
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| 4.0 | <ul><li>'엘로엘 2024 시즌8 팡팡 빅선쿠션 S8 스마일썬쿠션 본품 25g 옵션없음 더블아이'</li><li>'엘로엘 팡팡 빅 선쿠션 시즌 8 본품25g + 리필25g 옵션없음 미소샵'</li><li>'BRTC 마일드 선쿠션 본품+리필 기획 (디즈니) 옵션없음 지구상사'</li></ul> | |
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| 3.0 | <ul><li>'듀이셀 필터링크림 40ml (SPF50+) 옵션없음 오션컴퍼니'</li><li>'아쿠아선크림40ml+미니폼40ml / 24시간촉촉 비건 무기자차 SPF50+ PA++++ 옵션없음 에프엔지뷰티랩 주식회사'</li><li>'알리코제약 이나벨로 유기,무기자차 혼합자차 톤업 선크림 옵션없음 알리코제약(주)'</li></ul> | |
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| 2.0 | <ul><li>'리쥬란 바이옴 힐러 선케어 2종 세트(바이옴 힐러 선 크림 50mL SPF50+ + 바이옴 힐러 선 밤 19g SPF50+) 리쥬란 힐러'</li><li>'라로제 클린 선스틱 SPF50 18.5g+수분스틱 15ml 세트 라로제 코스메틱'</li><li>'[로우퀘스트] 베리어 인핸싱 선크림(SPF50+PA++++) + 에키네시아 선스틱(SPF50+PA++++) 2종 세트 로우퀘스트'</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** | 0.7883 | |
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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_bt7_test") |
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# Run inference |
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preds = model("제주온 큐테라 풋귤 알로에베라 수딩젤 200ml × 1개 구대연구소") |
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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 | 4 | 10.1504 | 24 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0.0 | 20 | |
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| 1.0 | 10 | |
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| 2.0 | 17 | |
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| 3.0 | 28 | |
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| 4.0 | 20 | |
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| 5.0 | 15 | |
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| 6.0 | 23 | |
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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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| 0.0625 | 1 | 0.4924 | - | |
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| 3.125 | 50 | 0.291 | - | |
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| 6.25 | 100 | 0.0536 | - | |
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| 9.375 | 150 | 0.0011 | - | |
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| 12.5 | 200 | 0.0002 | - | |
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| 15.625 | 250 | 0.0001 | - | |
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| 18.75 | 300 | 0.0001 | - | |
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| 21.875 | 350 | 0.0001 | - | |
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| 25.0 | 400 | 0.0001 | - | |
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| 28.125 | 450 | 0.0001 | - | |
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| 31.25 | 500 | 0.0001 | - | |
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| 34.375 | 550 | 0.0001 | - | |
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| 37.5 | 600 | 0.0001 | - | |
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| 40.625 | 650 | 0.0001 | - | |
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| 43.75 | 700 | 0.0001 | - | |
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| 46.875 | 750 | 0.0001 | - | |
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| 50.0 | 800 | 0.0001 | - | |
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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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