--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 아가방 가을 골지 레깅스 아기 유아 바지 남아 여아 속바지 신생 쫄바지 베이비 키즈 아가방 레깅스/쫄바지_01 치치골지레깅스 그린_80 출산/육아 > 유아동의류 > 레깅스 - text: 라고 세일러맨투맨 23겨울 아동복 아동 키즈 주니어 여아 JS_옐로 출산/육아 > 유아동의류 > 티셔츠 - text: 여아 드레스 원피스 겨울왕국2 캐주얼 안나 공주 원픽4 샴페인_120 출산/육아 > 유아동의류 > 공주드레스 - text: '[뉴발란스키즈]뉴키모 보이 다운(NK9PD4105U)100~160Size Black/110 출산/육아 > 유아동의류 > 점퍼' - text: 데일리베베 겨울 뽀글이점퍼 유아집업 아기집업 주니어 토끼_JM 출산/육아 > 유아동의류 > 점퍼 metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: true base_model: mini1013/master_domain 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: accuracy value: 1.0 name: Accuracy --- # 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:** 27 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 | | | 26.0 | | | 17.0 | | | 6.0 | | | 14.0 | | | 23.0 | | | 10.0 | | | 16.0 | | | 20.0 | | | 19.0 | | | 0.0 | | | 15.0 | | | 13.0 | | | 3.0 | | | 22.0 | | | 9.0 | | | 24.0 | | | 4.0 | | | 2.0 | | | 21.0 | | | 5.0 | | | 25.0 | | | 8.0 | | | 18.0 | | | 12.0 | | | 11.0 | | | 7.0 | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 1.0 | ## 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_bc19") # Run inference preds = model("데일리베베 겨울 뽀글이점퍼 유아집업 아기집업 주니어 토끼_JM 출산/육아 > 유아동의류 > 점퍼") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 7 | 15.2902 | 36 | | Label | Training Sample Count | |:------|:----------------------| | 0.0 | 70 | | 1.0 | 70 | | 2.0 | 20 | | 3.0 | 70 | | 4.0 | 70 | | 5.0 | 70 | | 6.0 | 70 | | 7.0 | 20 | | 8.0 | 70 | | 9.0 | 70 | | 10.0 | 70 | | 11.0 | 70 | | 12.0 | 70 | | 13.0 | 70 | | 14.0 | 70 | | 15.0 | 70 | | 16.0 | 70 | | 17.0 | 70 | | 18.0 | 70 | | 19.0 | 70 | | 20.0 | 70 | | 21.0 | 70 | | 22.0 | 70 | | 23.0 | 70 | | 24.0 | 70 | | 25.0 | 20 | | 26.0 | 70 | ### Training Hyperparameters - batch_size: (256, 256) - num_epochs: (30, 30) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 50 - 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 | |:-------:|:-----:|:-------------:|:---------------:| | 0.0029 | 1 | 0.499 | - | | 0.1471 | 50 | 0.4995 | - | | 0.2941 | 100 | 0.4977 | - | | 0.4412 | 150 | 0.4739 | - | | 0.5882 | 200 | 0.3318 | - | | 0.7353 | 250 | 0.2867 | - | | 0.8824 | 300 | 0.1873 | - | | 1.0294 | 350 | 0.1056 | - | | 1.1765 | 400 | 0.0747 | - | | 1.3235 | 450 | 0.0675 | - | | 1.4706 | 500 | 0.0391 | - | | 1.6176 | 550 | 0.0156 | - | | 1.7647 | 600 | 0.0067 | - | | 1.9118 | 650 | 0.004 | - | | 2.0588 | 700 | 0.0029 | - | | 2.2059 | 750 | 0.0018 | - | | 2.3529 | 800 | 0.0019 | - | | 2.5 | 850 | 0.0018 | - | | 2.6471 | 900 | 0.0006 | - | | 2.7941 | 950 | 0.0004 | - | | 2.9412 | 1000 | 0.0004 | - | | 3.0882 | 1050 | 0.0003 | - | | 3.2353 | 1100 | 0.0004 | - | | 3.3824 | 1150 | 0.0003 | - | | 3.5294 | 1200 | 0.0002 | - | | 3.6765 | 1250 | 0.0003 | - | | 3.8235 | 1300 | 0.0003 | - | | 3.9706 | 1350 | 0.0001 | - | | 4.1176 | 1400 | 0.0003 | - | | 4.2647 | 1450 | 0.0002 | - | | 4.4118 | 1500 | 0.0002 | - | | 4.5588 | 1550 | 0.0002 | - | | 4.7059 | 1600 | 0.0003 | - | | 4.8529 | 1650 | 0.0001 | - | | 5.0 | 1700 | 0.0002 | - | | 5.1471 | 1750 | 0.0002 | - | | 5.2941 | 1800 | 0.0001 | - | | 5.4412 | 1850 | 0.0003 | - | | 5.5882 | 1900 | 0.0002 | - | | 5.7353 | 1950 | 0.0003 | - | | 5.8824 | 2000 | 0.0002 | - | | 6.0294 | 2050 | 0.0003 | - | | 6.1765 | 2100 | 0.0001 | - | | 6.3235 | 2150 | 0.0002 | - | | 6.4706 | 2200 | 0.0001 | - | | 6.6176 | 2250 | 0.0002 | - | | 6.7647 | 2300 | 0.0002 | - | | 6.9118 | 2350 | 0.0002 | - | | 7.0588 | 2400 | 0.0002 | - | | 7.2059 | 2450 | 0.0002 | - | | 7.3529 | 2500 | 0.0001 | - | | 7.5 | 2550 | 0.0001 | - | | 7.6471 | 2600 | 0.0002 | - | | 7.7941 | 2650 | 0.0002 | - | | 7.9412 | 2700 | 0.0002 | - | | 8.0882 | 2750 | 0.0001 | - | | 8.2353 | 2800 | 0.0001 | - | | 8.3824 | 2850 | 0.0002 | - | | 8.5294 | 2900 | 0.0002 | - | | 8.6765 | 2950 | 0.0001 | - | | 8.8235 | 3000 | 0.0003 | - | | 8.9706 | 3050 | 0.0003 | - | | 9.1176 | 3100 | 0.0002 | - | | 9.2647 | 3150 | 0.0002 | - | | 9.4118 | 3200 | 0.0 | - | | 9.5588 | 3250 | 0.0003 | - | | 9.7059 | 3300 | 0.0003 | - | | 9.8529 | 3350 | 0.0001 | - | | 10.0 | 3400 | 0.0001 | - | | 10.1471 | 3450 | 0.0002 | - | | 10.2941 | 3500 | 0.0001 | - | | 10.4412 | 3550 | 0.0002 | - | | 10.5882 | 3600 | 0.0001 | - | | 10.7353 | 3650 | 0.0001 | - | | 10.8824 | 3700 | 0.0002 | - | | 11.0294 | 3750 | 0.0001 | - | | 11.1765 | 3800 | 0.0001 | - | | 11.3235 | 3850 | 0.0002 | - | | 11.4706 | 3900 | 0.0003 | - | | 11.6176 | 3950 | 0.0001 | - | | 11.7647 | 4000 | 0.0002 | - | | 11.9118 | 4050 | 0.0001 | - | | 12.0588 | 4100 | 0.0001 | - | | 12.2059 | 4150 | 0.0002 | - | | 12.3529 | 4200 | 0.0001 | - | | 12.5 | 4250 | 0.0001 | - | | 12.6471 | 4300 | 0.0002 | - | | 12.7941 | 4350 | 0.0003 | - | | 12.9412 | 4400 | 0.0006 | - | | 13.0882 | 4450 | 0.0018 | - | | 13.2353 | 4500 | 0.0011 | - | | 13.3824 | 4550 | 0.0008 | - | | 13.5294 | 4600 | 0.0011 | - | | 13.6765 | 4650 | 0.001 | - | | 13.8235 | 4700 | 0.0003 | - | | 13.9706 | 4750 | 0.0001 | - | | 14.1176 | 4800 | 0.0001 | - | | 14.2647 | 4850 | 0.0001 | - | | 14.4118 | 4900 | 0.0001 | - | | 14.5588 | 4950 | 0.0002 | - | | 14.7059 | 5000 | 0.0002 | - | | 14.8529 | 5050 | 0.0 | - | | 15.0 | 5100 | 0.0 | - | | 15.1471 | 5150 | 0.0 | - | | 15.2941 | 5200 | 0.0 | - | | 15.4412 | 5250 | 0.0 | - | | 15.5882 | 5300 | 0.0 | - | | 15.7353 | 5350 | 0.0 | - | | 15.8824 | 5400 | 0.0 | - | | 16.0294 | 5450 | 0.0 | - | | 16.1765 | 5500 | 0.0 | - | | 16.3235 | 5550 | 0.0 | - | | 16.4706 | 5600 | 0.0 | - | | 16.6176 | 5650 | 0.0 | - | | 16.7647 | 5700 | 0.0 | - | | 16.9118 | 5750 | 0.0 | - | | 17.0588 | 5800 | 0.0 | - | | 17.2059 | 5850 | 0.0 | - | | 17.3529 | 5900 | 0.0 | - | | 17.5 | 5950 | 0.0 | - | | 17.6471 | 6000 | 0.0 | - | | 17.7941 | 6050 | 0.0 | - | | 17.9412 | 6100 | 0.0 | - | | 18.0882 | 6150 | 0.0 | - | | 18.2353 | 6200 | 0.0 | - | | 18.3824 | 6250 | 0.0 | - | | 18.5294 | 6300 | 0.0 | - | | 18.6765 | 6350 | 0.0 | - | | 18.8235 | 6400 | 0.0 | - | | 18.9706 | 6450 | 0.0 | - | | 19.1176 | 6500 | 0.0 | - | | 19.2647 | 6550 | 0.0 | - | | 19.4118 | 6600 | 0.0 | - | | 19.5588 | 6650 | 0.0 | - | | 19.7059 | 6700 | 0.0 | - | | 19.8529 | 6750 | 0.0 | - | | 20.0 | 6800 | 0.0 | - | | 20.1471 | 6850 | 0.0 | - | | 20.2941 | 6900 | 0.0 | - | | 20.4412 | 6950 | 0.0 | - | | 20.5882 | 7000 | 0.0 | - | | 20.7353 | 7050 | 0.0 | - | | 20.8824 | 7100 | 0.0 | - | | 21.0294 | 7150 | 0.0 | - | | 21.1765 | 7200 | 0.0 | - | | 21.3235 | 7250 | 0.0 | - | | 21.4706 | 7300 | 0.0 | - | | 21.6176 | 7350 | 0.0 | - | | 21.7647 | 7400 | 0.0 | - | | 21.9118 | 7450 | 0.0 | - | | 22.0588 | 7500 | 0.0 | - | | 22.2059 | 7550 | 0.0 | - | | 22.3529 | 7600 | 0.0 | - | | 22.5 | 7650 | 0.0 | - | | 22.6471 | 7700 | 0.0 | - | | 22.7941 | 7750 | 0.0 | - | | 22.9412 | 7800 | 0.0 | - | | 23.0882 | 7850 | 0.0 | - | | 23.2353 | 7900 | 0.0 | - | | 23.3824 | 7950 | 0.0 | - | | 23.5294 | 8000 | 0.0 | - | | 23.6765 | 8050 | 0.0 | - | | 23.8235 | 8100 | 0.0 | - | | 23.9706 | 8150 | 0.0 | - | | 24.1176 | 8200 | 0.0 | - | | 24.2647 | 8250 | 0.0 | - | | 24.4118 | 8300 | 0.0 | - | | 24.5588 | 8350 | 0.0 | - | | 24.7059 | 8400 | 0.0 | - | | 24.8529 | 8450 | 0.0 | - | | 25.0 | 8500 | 0.0 | - | | 25.1471 | 8550 | 0.0 | - | | 25.2941 | 8600 | 0.0 | - | | 25.4412 | 8650 | 0.0 | - | | 25.5882 | 8700 | 0.0 | - | | 25.7353 | 8750 | 0.0 | - | | 25.8824 | 8800 | 0.0 | - | | 26.0294 | 8850 | 0.0 | - | | 26.1765 | 8900 | 0.0 | - | | 26.3235 | 8950 | 0.0 | - | | 26.4706 | 9000 | 0.0 | - | | 26.6176 | 9050 | 0.0 | - | | 26.7647 | 9100 | 0.0 | - | | 26.9118 | 9150 | 0.0 | - | | 27.0588 | 9200 | 0.0 | - | | 27.2059 | 9250 | 0.0 | - | | 27.3529 | 9300 | 0.0 | - | | 27.5 | 9350 | 0.0 | - | | 27.6471 | 9400 | 0.0 | - | | 27.7941 | 9450 | 0.0 | - | | 27.9412 | 9500 | 0.0 | - | | 28.0882 | 9550 | 0.0 | - | | 28.2353 | 9600 | 0.0 | - | | 28.3824 | 9650 | 0.0 | - | | 28.5294 | 9700 | 0.0 | - | | 28.6765 | 9750 | 0.0 | - | | 28.8235 | 9800 | 0.0 | - | | 28.9706 | 9850 | 0.0 | - | | 29.1176 | 9900 | 0.0 | - | | 29.2647 | 9950 | 0.0 | - | | 29.4118 | 10000 | 0.0 | - | | 29.5588 | 10050 | 0.0 | - | | 29.7059 | 10100 | 0.0 | - | | 29.8529 | 10150 | 0.0 | - | | 30.0 | 10200 | 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} } ```