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: '[PS5] 딥 어스 디스크에디션 콘솔 커버 코발트 블루  오진상사(주)'
+- text: '[PS5] 플레이스테이션5 디스크 에디션  오진상사(주)'
+- text: PS4 그란투리스모 스포트 한글판 PlaystationHits  조이게임
+- text: PS4 아이돌마스터 스탈릿 시즌 일반판 새제품 한글판  제이와이게임타운
+- text: '[PS4] 색보이 빅 어드벤처  에이티게임(주)'
+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: 0.7771822358346095
+      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:** 5 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>'[PS4] NBA 2K24 코비 브라이언트 에디션 특전 바우처 有  오진상사(주)'</li><li>'닌텐도 스위치 둘이서 냥코 대전쟁 한글판  게임매니아'</li><li>'닌텐도 마리오 카트 8 디럭스 + 조이콘 휠 패키지 SWITCH 한글판 마리오카트8 디럭스 (+조이콘핸들 세트)_마리오카트8 (+핸들 2개 원형 네온) 주식회사 쇼핑랩스'</li></ul>    |
+| 2     | <ul><li>'[트러스트마스터] T80 Ferrari 488 GTB 에디션  주식회사 투비네트웍스글로벌'</li><li>'트러스트마스터 T300 페라리 Integral 레이싱휠 [PS5, PS4, PC지원]  주식회사 디에스샵(DS SHOP)'</li><li>'레이저코리아 울버린 V2 크로마 Wolverine V2 Chroma 게임 컨트롤러  (주)하이케이넷'</li></ul> |
+| 1     | <ul><li>'[노리박스] 오락실 게임기 분리기통(고급DX팩)  (주)에스와이에스리테일'</li><li>'[XBOX]마이크로 소프트 정식발매 X-BOX series X 1TB 새제품  다음텔레콤'</li><li>'노리박스 32인치 스탠드형 강화유리 오락실게임기 오락기 DX팩(3000게임/720P/3~4인지원) (주)노리박스게임연구소'</li></ul>                |
+| 0     | <ul><li>'PC 삼국지 14 한글판 (스팀코드발송)  (주) 디지털터치'</li><li>'Wizard with a Gun 스팀 PC 뉴 어카운트 (정지X) / 기존계정 가능 기존 계정 스팀 유통할인'</li><li>'철권7 tekken7 PC/스팀 철권7 (코드48시이내발송)  전한수'</li></ul>                                         |
+| 4     | <ul><li>'한국 닌텐도 정품 게임기 스위치 신형 OLED+콘트라 로그콥스+액정강화유리세트 OLED 네온레드블루 색상_OLED본체+뉴슈퍼마리오U디럭스+강화유리 에이지씨'</li><li>'게임&워치 젤다의 전설  주식회사 손오공'</li><li>'닌텐도 스위치 라이트 옐로 동물의 숲 케이스  주식회사 손오공'</li></ul>                              |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.7772 |
+
+## 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_el3")
+# Run inference
+preds = model("[PS4] 색보이 빅 어드벤처  에이티게임(주)")
+```
+
+<!--
+### 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   | 5   | 10.7325 | 23  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 43                    |
+| 1     | 50                    |
+| 2     | 50                    |
+| 3     | 50                    |
+| 4     | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0263  | 1    | 0.496         | -               |
+| 1.3158  | 50   | 0.1186        | -               |
+| 2.6316  | 100  | 0.0532        | -               |
+| 3.9474  | 150  | 0.0398        | -               |
+| 5.2632  | 200  | 0.0002        | -               |
+| 6.5789  | 250  | 0.0001        | -               |
+| 7.8947  | 300  | 0.0001        | -               |
+| 9.2105  | 350  | 0.0001        | -               |
+| 10.5263 | 400  | 0.0001        | -               |
+| 11.8421 | 450  | 0.0001        | -               |
+| 13.1579 | 500  | 0.0001        | -               |
+| 14.4737 | 550  | 0.0001        | -               |
+| 15.7895 | 600  | 0.0           | -               |
+| 17.1053 | 650  | 0.0001        | -               |
+| 18.4211 | 700  | 0.0001        | -               |
+| 19.7368 | 750  | 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": [
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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",
+  "num_attention_heads": 12,
+  "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.Transformer"
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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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+}