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Push model using huggingface_hub.

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README.md ADDED
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+ ---
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+ base_model: mini1013/master_domain
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+ library_name: setfit
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+ metrics:
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+ - metric
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+ pipeline_tag: text-classification
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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: SD 바이오 에스디 코드프리 당뇨검사지 4박스 200매 (유효기간 2025년 03월) 코드프리 200매+알콜솜 100매 엠에스메디칼
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+ - text: 아큐첵 소프트클릭스 채혈기+채혈침 25개 액티브 퍼포마 인스턴트 가이드 란셋 채혈바늘 주식회사 더에스지엠
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+ - text: 녹십자 혈당시험지 당뇨 시험지 그린닥터 50매 시험지100매+체혈침100개 자재스토어
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+ - text: HL 지닥터 혈당시험지 100매 /당뇨측정 검사지 스트립 1_지닥터 혈당시험지 100매+알콜솜100매 헬스라e프
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+ - text: 비디 울트라파인 인슐린 주사기 1박스 100개 328821[31G 8mm 0.5ml]BD 펜니들 주사바늘 울트라파인2 BD 인슐린 31G
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+ 6mm 0.5ml 1박스(324901) 더메디칼샵
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+ inference: true
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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: metric
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+ value: 0.9786747905559787
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+ name: Metric
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+ ---
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+
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+ # SetFit with mini1013/master_domain
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+
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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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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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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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+
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+ ## Model Details
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+
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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:** 3 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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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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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | 1.0 | <ul><li>'프리스타일 리브레 무채혈 연속혈당측정기(24년1월)얼라이브패치1매 거래명세서 광명헬스케어'</li><li>'SD 코드프리 혈당측정기(측정기+채혈기+침10매+파우치)P 스토어알파'</li><li>'올메디쿠스 글루코닥터 탑 혈당계 AGM-4100+파우치+채혈기+채혈침 10개 엠에스메디칼'</li></ul> |
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+ | 2.0 | <ul><li>'에스디 SD 코드프리 측정지|검사지|시험지 100매(25년 2월) 더메디칼샵'</li><li>'바로잰 당뇨검사 혈당시험지 100매(50매x2팩) 사용기한 25년 3월 MinSellAmount 유니프라이스'</li><li>'옵티엄 프리스타일 케톤시험지1박스10매 검사지 혈중 (24년 8월) 메디트리'</li></ul> |
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+ | 0.0 | <ul><li>'비디 울트라파인 인슐린 주사기 1박스 100입 324901 [31G 6mm 0.5ml] BD 펜니들 주사바늘 울트라파인2 BD 인슐린 31G 8mm 3/10ml(0.5단위) 1박스(320440) 더메디칼샵'</li><li>'BD 비디 울트라파인 인슐린 주사기 시린지 31G 6mm 1ml 324903 100입 주식회사 더에스지엠'</li><li>'정림 멸균 일회용 주사기 3cc 23g 25mm 100개입 멸균주사기 10cc 18G 38mm(100ea/pck) (주)케이디상사'</li></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Metric |
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+ |:--------|:-------|
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+ | **all** | 0.9787 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("mini1013/master_cate_lh7")
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+ # Run inference
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+ preds = model("녹십자 혈당시험지 당뇨 시험지 그린닥터 50매 시험지100매+체혈침100개 자재스토어")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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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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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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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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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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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 | 4 | 9.62 | 21 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0.0 | 50 |
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+ | 1.0 | 50 |
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+ | 2.0 | 50 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (512, 512)
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+ - num_epochs: (20, 20)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 40
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+ - body_learning_rate: (2e-05, 2e-05)
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+ - head_learning_rate: 2e-05
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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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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: False
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:-------:|:----:|:-------------:|:---------------:|
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+ | 0.0417 | 1 | 0.4565 | - |
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+ | 2.0833 | 50 | 0.1836 | - |
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+ | 4.1667 | 100 | 0.1645 | - |
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+ | 6.25 | 150 | 0.0004 | - |
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+ | 8.3333 | 200 | 0.0001 | - |
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+ | 10.4167 | 250 | 0.0001 | - |
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+ | 12.5 | 300 | 0.0 | - |
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+ | 14.5833 | 350 | 0.0 | - |
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+ | 16.6667 | 400 | 0.0 | - |
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+ | 18.75 | 450 | 0.0 | - |
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+
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+ ### Framework Versions
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+ - Python: 3.10.12
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+ - SetFit: 1.1.0.dev0
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+ - Sentence Transformers: 3.1.1
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+ - Transformers: 4.46.1
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+ - PyTorch: 2.4.0+cu121
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+ - Datasets: 2.20.0
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+ - Tokenizers: 0.20.0
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+
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+ ## Citation
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+
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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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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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