--- base_model: klue/roberta-base library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 밀크바오밥 오리지널 샴푸 베이비파우더 1L 09_트리트먼트 화이트머스크 1000ml (#M)화장품/미용>헤어케어>샴푸 AD > Naverstore > 화장품/미용 > 헤어케어 > 샴푸 > 약산성샴푸 - text: 무코타염색제 7박스+3박스+정품 트리트먼트 50g 1.카키브라운 (#M)바디/헤어>바디케어>바디케어세트 Gmarket > 뷰티 > 바디/헤어 > 바디케어 > 바디케어세트 - text: 1+1세트~(컨센+릴렉스마스크100ml) 에스테티카 데미지 케어 컨센트레이트 120ml (열활성 열보호 에센스) 정품 + 릴렉스마스크100ml 1개 (#M)쿠팡 홈>싱글라이프>샤워/세안>헤어에센스 Coupang > 뷰티 > 헤어 > 헤어에센스/오일 > 헤어에센스 - text: 헤드스파7 트리트먼트 더 프리미엄 210ml + 210ml MinSellAmount (#M)바디/헤어>헤어케어>헤어트리트먼트 Gmarket > 뷰티 > 바디/헤어 > 헤어케어 > 헤어트리트먼트 - text: 헤어플러스 실크 코팅 트리트먼트 50ml 4개 실크 코팅 트리트먼트 50ml 4개 위메프 > 생활·주방·반려동물 > 바디/헤어 > 샴푸/린스/헤어케어 > 트리트먼트;위메프 > 생활·주방·반려동물 > 바디/헤어 > 샴푸/린스/헤어케어;위메프 > 뷰티 > 바디/헤어 > 샴푸/린스/헤어케어 > 샴푸/린스;(#M)위메프 > 생활·주방용품 > 바디/헤어 > 샴푸/린스/헤어케어 > 트리트먼트 위메프 > 뷰티 > 바디/헤어 > 샴푸/린스/헤어케어 > 트리트먼트 inference: true model-index: - name: SetFit with klue/roberta-base results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.8206115779645191 name: Accuracy --- # SetFit with klue/roberta-base This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [klue/roberta-base](https://huggingface.co/klue/roberta-base) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 2 classes ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 1 | | | 0 | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.8206 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_cate_top_bt13_9") # Run inference preds = model("무코타염색제 7박스+3박스+정품 트리트먼트 50g 1.카키브라운 (#M)바디/헤어>바디케어>바디케어세트 Gmarket > 뷰티 > 바디/헤어 > 바디케어 > 바디케어세트") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 14 | 23.76 | 98 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 50 | | 1 | 50 | ### Training Hyperparameters - batch_size: (64, 64) - num_epochs: (30, 30) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 100 - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-------:|:----:|:-------------:|:---------------:| | 0.0064 | 1 | 0.4326 | - | | 0.3185 | 50 | 0.3579 | - | | 0.6369 | 100 | 0.2616 | - | | 0.9554 | 150 | 0.0326 | - | | 1.2739 | 200 | 0.0 | - | | 1.5924 | 250 | 0.0 | - | | 1.9108 | 300 | 0.0 | - | | 2.2293 | 350 | 0.0 | - | | 2.5478 | 400 | 0.0 | - | | 2.8662 | 450 | 0.0 | - | | 3.1847 | 500 | 0.0 | - | | 3.5032 | 550 | 0.0 | - | | 3.8217 | 600 | 0.0 | - | | 4.1401 | 650 | 0.0 | - | | 4.4586 | 700 | 0.0 | - | | 4.7771 | 750 | 0.0 | - | | 5.0955 | 800 | 0.0 | - | | 5.4140 | 850 | 0.0 | - | | 5.7325 | 900 | 0.0 | - | | 6.0510 | 950 | 0.0 | - | | 6.3694 | 1000 | 0.0 | - | | 6.6879 | 1050 | 0.0 | - | | 7.0064 | 1100 | 0.0 | - | | 7.3248 | 1150 | 0.0 | - | | 7.6433 | 1200 | 0.0 | - | | 7.9618 | 1250 | 0.0 | - | | 8.2803 | 1300 | 0.0 | - | | 8.5987 | 1350 | 0.0 | - | | 8.9172 | 1400 | 0.0 | - | | 9.2357 | 1450 | 0.0 | - | | 9.5541 | 1500 | 0.0 | - | | 9.8726 | 1550 | 0.0 | - | | 10.1911 | 1600 | 0.0 | - | | 10.5096 | 1650 | 0.0 | - | | 10.8280 | 1700 | 0.0 | - | | 11.1465 | 1750 | 0.0 | - | | 11.4650 | 1800 | 0.0 | - | | 11.7834 | 1850 | 0.0 | - | | 12.1019 | 1900 | 0.0 | - | | 12.4204 | 1950 | 0.0 | - | | 12.7389 | 2000 | 0.0 | - | | 13.0573 | 2050 | 0.0 | - | | 13.3758 | 2100 | 0.0 | - | | 13.6943 | 2150 | 0.0 | - | | 14.0127 | 2200 | 0.0 | - | | 14.3312 | 2250 | 0.0 | - | | 14.6497 | 2300 | 0.0 | - | | 14.9682 | 2350 | 0.0 | - | | 15.2866 | 2400 | 0.0 | - | | 15.6051 | 2450 | 0.0 | - | | 15.9236 | 2500 | 0.0 | - | | 16.2420 | 2550 | 0.0 | - | | 16.5605 | 2600 | 0.0 | - | | 16.8790 | 2650 | 0.0 | - | | 17.1975 | 2700 | 0.0 | - | | 17.5159 | 2750 | 0.0 | - | | 17.8344 | 2800 | 0.0 | - | | 18.1529 | 2850 | 0.0 | - | | 18.4713 | 2900 | 0.0 | - | | 18.7898 | 2950 | 0.0 | - | | 19.1083 | 3000 | 0.0 | - | | 19.4268 | 3050 | 0.0 | - | | 19.7452 | 3100 | 0.0 | - | | 20.0637 | 3150 | 0.0 | - | | 20.3822 | 3200 | 0.0 | - | | 20.7006 | 3250 | 0.0 | - | | 21.0191 | 3300 | 0.0 | - | | 21.3376 | 3350 | 0.0 | - | | 21.6561 | 3400 | 0.0 | - | | 21.9745 | 3450 | 0.0 | - | | 22.2930 | 3500 | 0.0 | - | | 22.6115 | 3550 | 0.0 | - | | 22.9299 | 3600 | 0.0 | - | | 23.2484 | 3650 | 0.0 | - | | 23.5669 | 3700 | 0.0 | - | | 23.8854 | 3750 | 0.0 | - | | 24.2038 | 3800 | 0.0 | - | | 24.5223 | 3850 | 0.0 | - | | 24.8408 | 3900 | 0.0 | - | | 25.1592 | 3950 | 0.0 | - | | 25.4777 | 4000 | 0.0 | - | | 25.7962 | 4050 | 0.0 | - | | 26.1146 | 4100 | 0.0 | - | | 26.4331 | 4150 | 0.0 | - | | 26.7516 | 4200 | 0.0 | - | | 27.0701 | 4250 | 0.0 | - | | 27.3885 | 4300 | 0.0 | - | | 27.7070 | 4350 | 0.0 | - | | 28.0255 | 4400 | 0.0 | - | | 28.3439 | 4450 | 0.0 | - | | 28.6624 | 4500 | 0.0 | - | | 28.9809 | 4550 | 0.0 | - | | 29.2994 | 4600 | 0.0 | - | | 29.6178 | 4650 | 0.0 | - | | 29.9363 | 4700 | 0.0 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0 - Sentence Transformers: 3.3.1 - Transformers: 4.44.2 - PyTorch: 2.2.0a0+81ea7a4 - Datasets: 3.2.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```