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: JCP 애플 펜슬 1세대 USB-C Apple Pencil 어뎁터 포함 (MQLY3KH/A)  주식회사 제이씨엠컴퍼니
+- text: 힐링쉴드 갤럭시탭S9 울트라 ARAG 고화질 저반사 액정보호필름1매 후면1매  (주) 힐링쉴드코리아
+- text: 다이아큐브 아이패드 프로 13 M4 (2024) 9H PET 슬림강화유리 깨지지않는 액정보호필름, 간편부착 2매 6H 고투명 방탄 2매
+    뷰티코리아(Beauti korea)
+- text: 뷰씨 갤럭시탭S6 라이트 10.4인치 강화유리필름(2매) 강화유리필름(2매구성) 주식회사 오토스마트
+- text: 갤럭시탭A9 슈페리어 저반사 액정보호필름  (주) 폰트리
+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: 0.9694656488549618
+      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:** 4 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                                                                                                                                                                                                                                                                                                        |
+|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 3     | <ul><li>'와콤 KP-501E 표준그립펜 인튜어스 프로 펜 와콤펜  에이엠스토어'</li><li>'Apple 애플 펜슬 2세대 미국정품 MU8F2KH/A (3-5일배송)  굿웍스코리아 유한책임회사'</li><li>'교체형 갤탭 볼펜심 펜촉 탭S7 펜슬 S펜 라미 (G428) 블랙 몽실왕자A'</li></ul>                                                                                                                                |
+| 0     | <ul><li>'코끼리리빙 아이패드 갤럭시탭S 마그네틱 드로잉 필기 스탠드 거치대 P2WA-3419 12.9(2018/2020/2021/2022)_그레이 주식회사예스대현'</li><li>'뷰씨 갤럭시탭 아이패드 태블릿 거치대 침대 책상 틈새 고정 블랙 주식회사 오토스마트'</li><li>'알파플랜 휴대용 태블릿 거치대 스탠드 갤럭시탭 아이패드 ATH01 매트블랙 주식회사 로리스토어'</li></ul>                                                                               |
+| 2     | <ul><li>'뷰씨 아이패드 에어 6세대 11인치 M2 종이 질감 저반사 액정 보호 필름 에어6세대 11인치 (저반사)종이질감필름 제이포레스트'</li><li>'아이패드 에어 6세대 11 종이질감 Light 액정보호필름1매 후면1매  주식회사 스마트'</li><li>'아이패드 프로 3세대 12.9인치 지문방지 종이질감 액정보호필름 아이패드 프로 3세대 12.9_종이질감 액정보호필름 1매 주식회사 제이앤에이'</li></ul>                                                                |
+| 1     | <ul><li>'Apple 아이패드 에어 스마트 폴리오 (iPad Air 4,5세대용) - 다크 체리 (MNA43FE/A) 다크 체리 MNA43FE/A (주)블루박스 (Blue Box Co., Ltd)'</li><li>'[N페이적립+커피쿠폰] ESR 아이패드 프로13 폴리오 케이스 프로13_네이비 EC587 주식회사 샘빌'</li><li>'뷰씨 갤럭시탭 S8플러스 / S7플러스 / S7 FE 12.4인치 보디가드 투명범퍼 케이스 갤럭시탭S8+/S7+/S7 FE(공용)_보디가드ㅣ투명 광주스마트폰친구 아이폰 사설수리센터점'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9695 |
+
+## 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_el22")
+# Run inference
+preds = model("갤럭시탭A9 슈페리어 저반사 액정보호필름  (주) 폰트리")
+```
+
+<!--
+### 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   | 6   | 12.075 | 34  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 50                    |
+| 1     | 50                    |
+| 2     | 50                    |
+| 3     | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0312  | 1    | 0.4959        | -               |
+| 1.5625  | 50   | 0.0683        | -               |
+| 3.125   | 100  | 0.0002        | -               |
+| 4.6875  | 150  | 0.0001        | -               |
+| 6.25    | 200  | 0.0001        | -               |
+| 7.8125  | 250  | 0.0           | -               |
+| 9.375   | 300  | 0.0           | -               |
+| 10.9375 | 350  | 0.0           | -               |
+| 12.5    | 400  | 0.0           | -               |
+| 14.0625 | 450  | 0.0           | -               |
+| 15.625  | 500  | 0.0           | -               |
+| 17.1875 | 550  | 0.0           | -               |
+| 18.75   | 600  | 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_el",
+  "architectures": [
+    "RobertaModel"
+  ],
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+  "bos_token_id": 0,
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+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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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+}