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: 옴므 교체용 가죽 벨트끈 벨트줄 허리띠 벨트 가죽 수동 자동용 22_수동벨트용 이태리가죽 3.3cm_카멜(42인치) 에스컴퍼니
+- text: 여성 여자 패션 와이드 밴딩 벨트 패딩 코트 허리 허리띠 원피스 가디건 코디 패딩벨트 088_(SH30)_아이보리 {SH30-Ivory}
+    스웰swell
+- text: '[1 + 1]쭉쭉스판 늘어나는 밴딩 벨트 남여공용 캐쥬얼 데일리 군용 텍티컬 벨트 01. 늘어나는 벨트 1+1_05. 다크브라운_라이트브라운
+    스토리몰2'
+- text: '[로제이] 정장 캐주얼 가죽 더블 서스펜더 멜빵 NRMGSN011_BL 블랙_free '
+- text: 모두샵 남자 가죽 청바지벨트 캐주얼벨트 허리띠 이니셜각인 7. 브라운 D107_한글(정자체)_보통길이(36까지착용가능) 모두샾
+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: 0.9649836541954232
+      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:** 3 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>'고리 집게 가방 여행용 멜빵 클립 다용도 삼각버클 후크  옐로우몰'</li><li>'패션 여성서스펜더 스트랩 양복 출근룩 정장 코스튬 흰색 폭 2.5cm 120cm 맴매2'</li><li>'패션 여성서스펜더 스트랩 양복 출근룩 정장 코스튬 파란색 흰색 빨간색 줄무늬 폭2.5 120cm 맴매2'</li></ul>                                 |
+| 2.0   | <ul><li>'Basic Leather Belt 네이비_100cm 만달문화여행사'</li><li>'다이에나롤랑 러블리 여자벨트 146276 은장 브라운 FCB0012CM_L 105 네잎클로버마켓'</li><li>'[갤러리아] 헤지스핸드백HJBE2F406W2브라운 스티치장식 소가죽 여성 벨트(타임월드)  한화갤러리아(주)'</li></ul>                         |
+| 0.0   | <ul><li>'(아크테릭스)(공식판매처)(23SS) 컨베이어 벨트 32mm (AENSUX5577) BLACK_SM '</li><li>'[갤러리아] 헤지스핸드백 HJBE2F775BK_ 블랙 빅로고 버클 가죽 자동벨트(타임월드)  한화갤러리아(주)'</li><li>'닥스_핸드백 (선물포장/쇼핑백동봉) 블랙 체크배색 가죽 자동벨트 DBBE3E990BK  롯데백화점2관'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9650 |
+
+## 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_ac3")
+# Run inference
+preds = model("[로제이] 정장 캐주얼 가죽 더블 서스펜더 멜빵 NRMGSN011_BL 블랙_free ")
+```
+
+<!--
+### 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   | 9.6133 | 17  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0417  | 1    | 0.394         | -               |
+| 2.0833  | 50   | 0.0731        | -               |
+| 4.1667  | 100  | 0.0           | -               |
+| 6.25    | 150  | 0.0           | -               |
+| 8.3333  | 200  | 0.0           | -               |
+| 10.4167 | 250  | 0.0           | -               |
+| 12.5    | 300  | 0.0           | -               |
+| 14.5833 | 350  | 0.0           | -               |
+| 16.6667 | 400  | 0.0           | -               |
+| 18.75   | 450  | 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_ac",
+  "architectures": [
+    "RobertaModel"
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+  "bos_token_id": 0,
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+  "eos_token_id": 2,
+  "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": {},
+  "default_prompt_name": null,
+  "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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+}