Push model using huggingface_hub.

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+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: 인바디270 체중계 기계 몸무게 측정기 체성분분석기 체지방 헬스장 보건소 병원  헬스가이드
+- text: 오므론 HEM-7156 가정용 자동 전자 혈압측정기  프리윌
+- text: 신장계 체중계 측정기 전자 저울 휴대용 초등학교 표준색상모델(신장및체중)측정70-130cm 될콩샵
+- text: 스마트 바디 스케일 체지방 체중계 인바디 디지털 화이트 멜킨스포츠
+- text: 자동 초음파 신장 체중 재는기계 체지방 측정기 전자 (블루투스 모델) 화이트 동이네마켓
+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: 0.970486965076242
+      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:** 7 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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   | <ul><li>'소형견 디지털 측정기 동물병원 애완동물 신장 측정기 블루 토토네 직구상점'</li><li>'고정밀 GPS 토지측정기 차량탑재형 휴대용 면적재기 8000mAh리튬배터리+데이터공유+3.5인치화 88마켓플레이스'</li><li>'동명 PVC 관절각도계 곤요메타 고니오메타 Goniometer  에스케이솔루션'</li></ul>                                                  |
+| 5.0   | <ul><li>'대형 계근대 농장 고중량 공장 축사 체중계 0.6X0.6m(500kg) 한뉘'</li><li>'레트로체중계 저울 아날로그체중계 대형 눈금 욕실 블랙_기계적 에치제이 컴퍼니'</li><li>'10habit 키재기 장식 3D 아동방 스티커 벽시트 인테리어 판다 10해빗'</li></ul>                                                                      |
+| 0.0   | <ul><li>'고급형 만보 기 시계 손목 실리콘 게 옐로우 랩바이랩'</li><li>'IW 계수기 [3.2cm×4.5cm] 요가 휘트니스 홈트  씨제이상사'</li><li>'오토워킹 흔들기 자동 만보기 만보계 핸드폰 휴대폰 흔들이 블랙]4핸드폰부상 베어링/무음소거 로얄산티아고'</li></ul>                                                                        |
+| 4.0   | <ul><li>'브라운 귀체온계 IRT-6030 +필터21p포함 1년무상AS gx  신세계몰'</li><li>'[브라운 온라인 공식 판매점] 귀체온계 택1 (IRT-6030/6510/6520/6525) 브라운 귀체온계 IRT-6520 바이메드'</li><li>'브라운 체온계 전용 렌즈필터/필터캡/리필커버 1통 20개입  (주)에이엔케어'</li></ul>                                       |
+| 6.0   | <ul><li>'인바디 BPBIO320N 자동혈압계 의자포함 조앤지컴퍼니'</li><li>'HEM-7156 가정용 자동전자혈압계 혈압측정기  프리윌커뮤니케이션'</li><li>'인바디 자동 혈���계 BPBIO320S 프린터형 혈압기 병원 그린 전문혈압측정기 320S 본품만 그레이 (주)창보누리'</li></ul>                                                                |
+| 3.0   | <ul><li>'[3M] 리트만 카디올로지4 (Cardiology Ⅳ) 청진기 (양면) - 6152/6154/6155/6156/6158 퍼플 6156 닥터메디'</li><li>'3M 리트만 클래식2 페디아트릭 청진기 2113 - 소아용 MinSellAmount 닥터메디'</li><li>'MDF 청진기용 이어팁(1조-2개)스롤/라지 선택구매/MDF 청진기 이어 고무팁 large (1조-2개입) 메디스토어'</li></ul> |
+| 2.0   | <ul><li>'Polar OH1 심박수 센서  픽더마인드'</li><li>'Wahoo Fitness 티커 심박수측정기(HRM) 스텔스 그레이  식스퀄리티'</li><li>'Polar H10 심박수 모니터, 블루투스 HRM 가슴 스트랩 - 아이폰 및 안드로이드 호환, 블랙  식스퀄리티'</li></ul>                                                                    |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9705 |
+
+## 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_lh1")
+# Run inference
+preds = model("오므론 HEM-7156 가정용 자동 전자 혈압측정기  프리윌")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set | Min | Median | Max |
+|:-------------|:----|:-------|:----|
+| Word count   | 3   | 9.8693 | 19  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 6                     |
+| 3.0   | 50                    |
+| 4.0   | 50                    |
+| 5.0   | 50                    |
+| 6.0   | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0208  | 1    | 0.4101        | -               |
+| 1.0417  | 50   | 0.2002        | -               |
+| 2.0833  | 100  | 0.0659        | -               |
+| 3.125   | 150  | 0.0326        | -               |
+| 4.1667  | 200  | 0.0277        | -               |
+| 5.2083  | 250  | 0.0304        | -               |
+| 6.25    | 300  | 0.0241        | -               |
+| 7.2917  | 350  | 0.0219        | -               |
+| 8.3333  | 400  | 0.0276        | -               |
+| 9.375   | 450  | 0.0326        | -               |
+| 10.4167 | 500  | 0.0048        | -               |
+| 11.4583 | 550  | 0.0001        | -               |
+| 12.5    | 600  | 0.0001        | -               |
+| 13.5417 | 650  | 0.0001        | -               |
+| 14.5833 | 700  | 0.0001        | -               |
+| 15.625  | 750  | 0.0           | -               |
+| 16.6667 | 800  | 0.0           | -               |
+| 17.7083 | 850  | 0.0           | -               |
+| 18.75   | 900  | 0.0           | -               |
+| 19.7917 | 950  | 0.0           | -               |
+
+### 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "_name_or_path": "mini1013/master_item_lh",
+  "architectures": [
+    "RobertaModel"
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+  "hidden_act": "gelu",
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+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
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+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "BertTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.46.1",
+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {},
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+  "similarity_fn_name": null
+}

config_setfit.json

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+  "normalize_embeddings": false
+}

model.safetensors

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model_head.pkl

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modules.json

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+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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tokenizer_config.json

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+}