--- 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: 소니 WH-CH520 블루투스헤드셋 정품 WH-CH520/BZE 블랙 주식회사 스피티 - text: 코스 스튜디오용 헤드폰 스탠다드 패키징 블랙 풀사이즈 Pro4AA 1) Standard Packaging 제이크루 - text: 브리츠 P510GX 유선이어폰 음악+통화 언더이어 오픈형 (주)엠글로벌스 - text: 브리츠 BZ-MQ7 휴대용 FM라디오 효도라디오 블루투스 스피커 블랙 하나전산 - text: SOUNDCRAFT NOTEPAD-12FX 사운드 크래프트 노트패드12FX 아날로그 믹서/USB 오디오 인터페이스 [공식수입정품] 사운드필 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: 0.7123194792867313 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:** 22 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 | |:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 12 | | | 8 | | | 15 | | | 21 | | | 17 | | | 20 | | | 13 | | | 19 | | | 10 | | | 6 | | | 18 | | | 1 | | | 14 | | | 0 | | | 7 | | | 2 | | | 11 | | | 5 | | | 9 | | | 3 | | | 16 | | | 4 | | ## Evaluation ### Metrics | Label | Metric | |:--------|:-------| | **all** | 0.7123 | ## 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_el14") # Run inference preds = model("브리츠 P510GX 유선이어폰 음악+통화 언더이어 오픈형 (주)엠글로벌스") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 3 | 9.4791 | 33 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 50 | | 1 | 50 | | 2 | 50 | | 3 | 12 | | 4 | 4 | | 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 | | 19 | 13 | | 20 | 50 | | 21 | 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 | |:-------:|:----:|:-------------:|:---------------:| | 0.0065 | 1 | 0.497 | - | | 0.3268 | 50 | 0.3791 | - | | 0.6536 | 100 | 0.2221 | - | | 0.9804 | 150 | 0.1258 | - | | 1.3072 | 200 | 0.0648 | - | | 1.6340 | 250 | 0.0513 | - | | 1.9608 | 300 | 0.0383 | - | | 2.2876 | 350 | 0.0297 | - | | 2.6144 | 400 | 0.0308 | - | | 2.9412 | 450 | 0.0208 | - | | 3.2680 | 500 | 0.0132 | - | | 3.5948 | 550 | 0.0188 | - | | 3.9216 | 600 | 0.0196 | - | | 4.2484 | 650 | 0.0158 | - | | 4.5752 | 700 | 0.0061 | - | | 4.9020 | 750 | 0.009 | - | | 5.2288 | 800 | 0.0107 | - | | 5.5556 | 850 | 0.0048 | - | | 5.8824 | 900 | 0.0024 | - | | 6.2092 | 950 | 0.0077 | - | | 6.5359 | 1000 | 0.0023 | - | | 6.8627 | 1050 | 0.0077 | - | | 7.1895 | 1100 | 0.006 | - | | 7.5163 | 1150 | 0.003 | - | | 7.8431 | 1200 | 0.0046 | - | | 8.1699 | 1250 | 0.0062 | - | | 8.4967 | 1300 | 0.003 | - | | 8.8235 | 1350 | 0.0022 | - | | 9.1503 | 1400 | 0.0004 | - | | 9.4771 | 1450 | 0.0003 | - | | 9.8039 | 1500 | 0.0003 | - | | 10.1307 | 1550 | 0.0022 | - | | 10.4575 | 1600 | 0.0006 | - | | 10.7843 | 1650 | 0.0002 | - | | 11.1111 | 1700 | 0.0002 | - | | 11.4379 | 1750 | 0.0002 | - | | 11.7647 | 1800 | 0.0029 | - | | 12.0915 | 1850 | 0.0002 | - | | 12.4183 | 1900 | 0.0001 | - | | 12.7451 | 1950 | 0.0001 | - | | 13.0719 | 2000 | 0.0001 | - | | 13.3987 | 2050 | 0.0001 | - | | 13.7255 | 2100 | 0.0001 | - | | 14.0523 | 2150 | 0.0002 | - | | 14.3791 | 2200 | 0.0001 | - | | 14.7059 | 2250 | 0.0001 | - | | 15.0327 | 2300 | 0.0001 | - | | 15.3595 | 2350 | 0.0001 | - | | 15.6863 | 2400 | 0.0001 | - | | 16.0131 | 2450 | 0.0002 | - | | 16.3399 | 2500 | 0.0001 | - | | 16.6667 | 2550 | 0.002 | - | | 16.9935 | 2600 | 0.0001 | - | | 17.3203 | 2650 | 0.002 | - | | 17.6471 | 2700 | 0.0001 | - | | 17.9739 | 2750 | 0.0001 | - | | 18.3007 | 2800 | 0.0001 | - | | 18.6275 | 2850 | 0.0001 | - | | 18.9542 | 2900 | 0.0021 | - | | 19.2810 | 2950 | 0.0001 | - | | 19.6078 | 3000 | 0.0001 | - | | 19.9346 | 3050 | 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} } ```