--- 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: '[리바이스](강남점) 남성 511 슬림 스트레치 데님 팬츠(04511-4655) 29(74) 신세계백화점' - text: '[헤지스 남성] HZPA2D344N2 네이비 단색 면혼방 일자핏팬츠 82 (32) ' - text: 남성 나일론 고프코어 쇼츠 그레이 (263525EY23) 회색(앤틱실버)_L (주)아이엔에프아이엑스 - text: 멜빵바지 데님 점프수트 코디 남성 스트릿패션 M_블랙 설현닷컴 - text: 뱅뱅 남성 23FW 라이크라 본딩 데님팬츠 3종 남성/캐주얼(하의)_38 NS홈쇼핑 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: 0.7622648207312744 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:** 17 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 | | | 4.0 | | | 15.0 | | | 9.0 | | | 11.0 | | | 3.0 | | | 8.0 | | | 7.0 | | | 6.0 | | | 14.0 | | | 2.0 | | | 13.0 | | | 16.0 | | | 10.0 | | | 0.0 | | | 5.0 | | | 12.0 | | ## Evaluation ### Metrics | Label | Metric | |:--------|:-------| | **all** | 0.7623 | ## 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_ap1") # Run inference preds = model("멜빵바지 데님 점프수트 코디 남성 스트릿패션 M_블랙 설현닷컴") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 3 | 9.8578 | 23 | | Label | Training Sample Count | |:------|:----------------------| | 0.0 | 50 | | 1.0 | 50 | | 2.0 | 50 | | 3.0 | 50 | | 4.0 | 50 | | 5.0 | 50 | | 6.0 | 50 | | 7.0 | 50 | | 8.0 | 50 | | 9.0 | 50 | | 10.0 | 50 | | 11.0 | 50 | | 12.0 | 9 | | 13.0 | 50 | | 14.0 | 50 | | 15.0 | 50 | | 16.0 | 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 | |:-------:|:----:|:-------------:|:---------------:| | 0.0079 | 1 | 0.448 | - | | 0.3937 | 50 | 0.3099 | - | | 0.7874 | 100 | 0.1872 | - | | 1.1811 | 150 | 0.1141 | - | | 1.5748 | 200 | 0.0429 | - | | 1.9685 | 250 | 0.0283 | - | | 2.3622 | 300 | 0.0134 | - | | 2.7559 | 350 | 0.0137 | - | | 3.1496 | 400 | 0.0079 | - | | 3.5433 | 450 | 0.0087 | - | | 3.9370 | 500 | 0.0037 | - | | 4.3307 | 550 | 0.0006 | - | | 4.7244 | 600 | 0.0006 | - | | 5.1181 | 650 | 0.0003 | - | | 5.5118 | 700 | 0.0004 | - | | 5.9055 | 750 | 0.0003 | - | | 6.2992 | 800 | 0.0003 | - | | 6.6929 | 850 | 0.0002 | - | | 7.0866 | 900 | 0.0002 | - | | 7.4803 | 950 | 0.0002 | - | | 7.8740 | 1000 | 0.0002 | - | | 8.2677 | 1050 | 0.0002 | - | | 8.6614 | 1100 | 0.0002 | - | | 9.0551 | 1150 | 0.0003 | - | | 9.4488 | 1200 | 0.0002 | - | | 9.8425 | 1250 | 0.0002 | - | | 10.2362 | 1300 | 0.0002 | - | | 10.6299 | 1350 | 0.0001 | - | | 11.0236 | 1400 | 0.0001 | - | | 11.4173 | 1450 | 0.0001 | - | | 11.8110 | 1500 | 0.0001 | - | | 12.2047 | 1550 | 0.0002 | - | | 12.5984 | 1600 | 0.0001 | - | | 12.9921 | 1650 | 0.0001 | - | | 13.3858 | 1700 | 0.0001 | - | | 13.7795 | 1750 | 0.0001 | - | | 14.1732 | 1800 | 0.0001 | - | | 14.5669 | 1850 | 0.0001 | - | | 14.9606 | 1900 | 0.0001 | - | | 15.3543 | 1950 | 0.0001 | - | | 15.7480 | 2000 | 0.0001 | - | | 16.1417 | 2050 | 0.0001 | - | | 16.5354 | 2100 | 0.0001 | - | | 16.9291 | 2150 | 0.0001 | - | | 17.3228 | 2200 | 0.0001 | - | | 17.7165 | 2250 | 0.0001 | - | | 18.1102 | 2300 | 0.0001 | - | | 18.5039 | 2350 | 0.0001 | - | | 18.8976 | 2400 | 0.0001 | - | | 19.2913 | 2450 | 0.0001 | - | | 19.6850 | 2500 | 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} } ```