--- 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: '[제너럴아이디어 WOMAN] 하찌 볼레로 니트 세트 [3COL] / WBC3L05518SET BLUE_FREE 지아이홀딩스' - text: 핫슈트 다이어트 여자 땀복 헬스복 트레이닝 운동복 지투 라운드 세트 HS6004 S_S 주식회사 사람사랑 - text: '[해외정품] 바버 데브론 퀼팅자켓LQU1012BK91 Lt Trench_UK10 위너12' - text: '[갤러리아] [여]NEW 포플린 셔츠(05343901)(343901)(한화갤러리아㈜ 센터시티) 01 다크그린_M 한화갤러리아(주)' - text: (SOUP)(신세계마산점)숲 라이더형 무스탕 (SZBMU90) 블랙_66 신세계백화점 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: 0.7890421327054075 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:** 21 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 | |:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 15.0 | | | 5.0 | | | 7.0 | | | 10.0 | | | 3.0 | | | 0.0 | | | 16.0 | | | 4.0 | | | 20.0 | | | 11.0 | | | 17.0 | | | 18.0 | | | 2.0 | | | 19.0 | | | 14.0 | | | 12.0 | | | 13.0 | | | 6.0 | | | 9.0 | | | 1.0 | | | 8.0 | | ## Evaluation ### Metrics | Label | Metric | |:--------|:-------| | **all** | 0.7890 | ## 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_ap3") # Run inference preds = model("(SOUP)(신세계마산점)숲 라이더형 무스탕 (SZBMU90) 블랙_66 신세계백화점") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 3 | 9.6448 | 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 | 50 | | 13.0 | 50 | | 14.0 | 50 | | 15.0 | 50 | | 16.0 | 50 | | 17.0 | 50 | | 18.0 | 50 | | 19.0 | 50 | | 20.0 | 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 | |:-------:|:----:|:-------------:|:---------------:| | 0.0061 | 1 | 0.3795 | - | | 0.3030 | 50 | 0.296 | - | | 0.6061 | 100 | 0.2248 | - | | 0.9091 | 150 | 0.1494 | - | | 1.2121 | 200 | 0.0913 | - | | 1.5152 | 250 | 0.061 | - | | 1.8182 | 300 | 0.0322 | - | | 2.1212 | 350 | 0.0243 | - | | 2.4242 | 400 | 0.0152 | - | | 2.7273 | 450 | 0.0134 | - | | 3.0303 | 500 | 0.0056 | - | | 3.3333 | 550 | 0.0026 | - | | 3.6364 | 600 | 0.0016 | - | | 3.9394 | 650 | 0.0066 | - | | 4.2424 | 700 | 0.0044 | - | | 4.5455 | 750 | 0.0025 | - | | 4.8485 | 800 | 0.0023 | - | | 5.1515 | 850 | 0.0023 | - | | 5.4545 | 900 | 0.0008 | - | | 5.7576 | 950 | 0.0023 | - | | 6.0606 | 1000 | 0.0005 | - | | 6.3636 | 1050 | 0.0015 | - | | 6.6667 | 1100 | 0.0006 | - | | 6.9697 | 1150 | 0.0003 | - | | 7.2727 | 1200 | 0.0003 | - | | 7.5758 | 1250 | 0.0003 | - | | 7.8788 | 1300 | 0.0002 | - | | 8.1818 | 1350 | 0.0004 | - | | 8.4848 | 1400 | 0.0002 | - | | 8.7879 | 1450 | 0.0002 | - | | 9.0909 | 1500 | 0.0002 | - | | 9.3939 | 1550 | 0.0002 | - | | 9.6970 | 1600 | 0.0001 | - | | 10.0 | 1650 | 0.0001 | - | | 10.3030 | 1700 | 0.0002 | - | | 10.6061 | 1750 | 0.0001 | - | | 10.9091 | 1800 | 0.0001 | - | | 11.2121 | 1850 | 0.0002 | - | | 11.5152 | 1900 | 0.0002 | - | | 11.8182 | 1950 | 0.0002 | - | | 12.1212 | 2000 | 0.0001 | - | | 12.4242 | 2050 | 0.0001 | - | | 12.7273 | 2100 | 0.0001 | - | | 13.0303 | 2150 | 0.0001 | - | | 13.3333 | 2200 | 0.0001 | - | | 13.6364 | 2250 | 0.0001 | - | | 13.9394 | 2300 | 0.0001 | - | | 14.2424 | 2350 | 0.0001 | - | | 14.5455 | 2400 | 0.0001 | - | | 14.8485 | 2450 | 0.0001 | - | | 15.1515 | 2500 | 0.0001 | - | | 15.4545 | 2550 | 0.0001 | - | | 15.7576 | 2600 | 0.0001 | - | | 16.0606 | 2650 | 0.0001 | - | | 16.3636 | 2700 | 0.0001 | - | | 16.6667 | 2750 | 0.0001 | - | | 16.9697 | 2800 | 0.0001 | - | | 17.2727 | 2850 | 0.0001 | - | | 17.5758 | 2900 | 0.0001 | - | | 17.8788 | 2950 | 0.0001 | - | | 18.1818 | 3000 | 0.0001 | - | | 18.4848 | 3050 | 0.0001 | - | | 18.7879 | 3100 | 0.0001 | - | | 19.0909 | 3150 | 0.0001 | - | | 19.3939 | 3200 | 0.0001 | - | | 19.6970 | 3250 | 0.0001 | - | | 20.0 | 3300 | 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} } ```