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--- |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: 베리네이처 유기농 이유식 큐브 야채 토핑 초기 다진 단호박 45g 유기농_★단품 후기 90g_05.다진 적양배추 출산/육아 > 이유식 |
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> 이유식재료 |
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- text: 처음요리 이유식 유아식 밀키트 세트 초기 중기 후기 완료기 유아식 식단세트 다진야채큐브 05.진죽1_10팩 매일한우식단 1번_베이직(쌀/육수 |
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제외) 출산/육아 > 이유식 > 이유식재료 |
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- text: 루솔 튼튼 어린이 볶음밥 8가지맛 (1팩) LU0723.버섯볶음밥 출산/육아 > 이유식 > 가공이유식 |
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- text: 프레벨롱 국산과일 퓨레 6팩세트 아기퓨레 아기간식 블루베리 2팩+비트 2팩+고구마 2팩 출산/육아 > 이유식 > 가공이유식 |
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- text: 알렉스앤필 6종 스웨덴 유기농 아기 이유식 과일퓨레 당근&망고 출산/육아 > 이유식 > 가공이유식 |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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library_name: setfit |
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inference: true |
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base_model: mini1013/master_domain |
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model-index: |
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- name: SetFit with mini1013/master_domain |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 1.0 |
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name: Accuracy |
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--- |
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# SetFit with mini1013/master_domain |
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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. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 2 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| 1.0 | <ul><li>'이유식 야채 큐브 다진야채 적양배추_유아기 출산/육아 > 이유식 > 이유식재료'</li><li>'오뚜기 어린이카레 80g 출산/육아 > 이유식 > 이유식재료'</li><li>'라온킴 다진야채 매일 만드는 이유식큐브 토핑 초기 중기 후기 완료 연근(껍질제거)_중기 출산/육아 > 이유식 > 이유식재료'</li></ul> | |
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| 0.0 | <ul><li>'[1+1 ] 아기퓨레 과일 무럭무럭 키즈죽 간식 중기 후기 파우치 실온이유식 12개월 단호박 1박스 + 바나나단호박 1박스 출산/육아 > 이유식 > 가공이유식'</li><li>'푸드트리 아기카레 덮밥소스 돌 두돌 아기반찬 유아반찬 유아식 소고기커리 아기덮밥 소스) A07 소고기 순한짜장 출산/육아 > 이유식 > 가공이유식'</li><li>'퓨어잇 아이김 3+3팩 골라담기 파래김/김과자 오가닉 아이김자반 3봉_유기농 김100% 3팩 출산/육아 > 이유식 > 가공이유식'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 1.0 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("mini1013/master_cate_bc25") |
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# Run inference |
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preds = model("알렉스앤필 6종 스웨덴 유기농 아기 이유식 과일퓨레 당근&망고 출산/육아 > 이유식 > 가공이유식") |
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``` |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 8 | 15.4286 | 23 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0.0 | 70 | |
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| 1.0 | 70 | |
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### Training Hyperparameters |
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- batch_size: (256, 256) |
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- num_epochs: (30, 30) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 50 |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- l2_weight: 0.01 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:-------:|:----:|:-------------:|:---------------:| |
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| 0.0357 | 1 | 0.4786 | - | |
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| 1.7857 | 50 | 0.2484 | - | |
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| 3.5714 | 100 | 0.0 | - | |
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| 5.3571 | 150 | 0.0 | - | |
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| 7.1429 | 200 | 0.0 | - | |
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| 8.9286 | 250 | 0.0 | - | |
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| 10.7143 | 300 | 0.0 | - | |
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| 12.5 | 350 | 0.0 | - | |
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| 14.2857 | 400 | 0.0 | - | |
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| 16.0714 | 450 | 0.0 | - | |
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| 17.8571 | 500 | 0.0 | - | |
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| 19.6429 | 550 | 0.0 | - | |
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| 21.4286 | 600 | 0.0 | - | |
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| 23.2143 | 650 | 0.0 | - | |
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| 25.0 | 700 | 0.0 | - | |
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| 26.7857 | 750 | 0.0 | - | |
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| 28.5714 | 800 | 0.0 | - | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.1.0 |
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- Sentence Transformers: 3.3.1 |
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- Transformers: 4.44.2 |
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- PyTorch: 2.2.0a0+81ea7a4 |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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