--- base_model: mini1013/master_domain library_name: setfit metrics: - metric pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 빌트록스 85mm F1.8 STM AF II 소니 풀프레임 FE-mount 주식회사 에스에이치몰 - text: 185CM 카메라 스마트폰 삼각대 SEL-ML185K 주식회사 셀루미 - text: 켄코 리얼프로 REALPRO UV 필터 43mm(포켓융+렌즈클리너)/JW (주)제이더블피앤엘 - text: 후지필름 XC35mm F2 렌즈 정품 입고완료 구매가능 주식회사 제이에스헤럴드나인 - text: 캐논 RF 100-500mm F4.5 7.1 L IS USM 망원렌즈 VSGO DKL-20 오.케이.굳 주식회사 inference: true model-index: - name: SetFit with mini1013/master_domain results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: metric value: 0.8190048035472349 name: Metric --- # SetFit with mini1013/master_domain 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. 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:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) - **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:** 19 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 | | | 18 | | | 5 | | | 4 | | | 14 | | | 8 | | | 0 | | | 6 | | | 12 | | | 11 | | | 2 | | | 15 | | | 16 | | | 3 | | | 7 | | | 17 | | | 9 | | | 10 | | | 13 | | ## Evaluation ### Metrics | Label | Metric | |:--------|:-------| | **all** | 0.8190 | ## 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_el20") # Run inference preds = model("185CM 카메라 스마트폰 삼각대 SEL-ML185K 주식회사 셀루미") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 3 | 9.9389 | 25 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 50 | | 1 | 50 | | 2 | 50 | | 3 | 50 | | 4 | 50 | | 5 | 50 | | 6 | 50 | | 7 | 50 | | 8 | 50 | | 9 | 50 | | 10 | 50 | | 11 | 50 | | 12 | 50 | | 13 | 50 | | 14 | 50 | | 15 | 50 | | 16 | 50 | | 17 | 50 | | 18 | 50 | ### Training Hyperparameters - batch_size: (512, 512) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 40 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-------:|:----:|:-------------:|:---------------:| | 0.0067 | 1 | 0.4972 | - | | 0.3356 | 50 | 0.3162 | - | | 0.6711 | 100 | 0.178 | - | | 1.0067 | 150 | 0.1148 | - | | 1.3423 | 200 | 0.0596 | - | | 1.6779 | 250 | 0.0503 | - | | 2.0134 | 300 | 0.0288 | - | | 2.3490 | 350 | 0.03 | - | | 2.6846 | 400 | 0.0227 | - | | 3.0201 | 450 | 0.0219 | - | | 3.3557 | 500 | 0.022 | - | | 3.6913 | 550 | 0.0127 | - | | 4.0268 | 600 | 0.007 | - | | 4.3624 | 650 | 0.0098 | - | | 4.6980 | 700 | 0.0037 | - | | 5.0336 | 750 | 0.0044 | - | | 5.3691 | 800 | 0.0041 | - | | 5.7047 | 850 | 0.0003 | - | | 6.0403 | 900 | 0.0022 | - | | 6.3758 | 950 | 0.0002 | - | | 6.7114 | 1000 | 0.0053 | - | | 7.0470 | 1050 | 0.0006 | - | | 7.3826 | 1100 | 0.0006 | - | | 7.7181 | 1150 | 0.0003 | - | | 8.0537 | 1200 | 0.0002 | - | | 8.3893 | 1250 | 0.0002 | - | | 8.7248 | 1300 | 0.0002 | - | | 9.0604 | 1350 | 0.0002 | - | | 9.3960 | 1400 | 0.0002 | - | | 9.7315 | 1450 | 0.0001 | - | | 10.0671 | 1500 | 0.0002 | - | | 10.4027 | 1550 | 0.0002 | - | | 10.7383 | 1600 | 0.0001 | - | | 11.0738 | 1650 | 0.0002 | - | | 11.4094 | 1700 | 0.0001 | - | | 11.7450 | 1750 | 0.0001 | - | | 12.0805 | 1800 | 0.0001 | - | | 12.4161 | 1850 | 0.0001 | - | | 12.7517 | 1900 | 0.0001 | - | | 13.0872 | 1950 | 0.0001 | - | | 13.4228 | 2000 | 0.0001 | - | | 13.7584 | 2050 | 0.0001 | - | | 14.0940 | 2100 | 0.0001 | - | | 14.4295 | 2150 | 0.0001 | - | | 14.7651 | 2200 | 0.0001 | - | | 15.1007 | 2250 | 0.0001 | - | | 15.4362 | 2300 | 0.0001 | - | | 15.7718 | 2350 | 0.0001 | - | | 16.1074 | 2400 | 0.0001 | - | | 16.4430 | 2450 | 0.0001 | - | | 16.7785 | 2500 | 0.0001 | - | | 17.1141 | 2550 | 0.0001 | - | | 17.4497 | 2600 | 0.0001 | - | | 17.7852 | 2650 | 0.0001 | - | | 18.1208 | 2700 | 0.0001 | - | | 18.4564 | 2750 | 0.0001 | - | | 18.7919 | 2800 | 0.0001 | - | | 19.1275 | 2850 | 0.0001 | - | | 19.4631 | 2900 | 0.0001 | - | | 19.7987 | 2950 | 0.0001 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 3.1.1 - Transformers: 4.46.1 - PyTorch: 2.4.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.20.0 ## 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} } ```