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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+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 매일유업 앱솔루트 센서티브 1단계 900g x 1개 [음료] 차음료_비락식혜175ml30캔 출산/육아 > 분유 > 국내분유
+- text: Hipp 힙 콤비오틱 유기농 1단계 800g [육아] 분유_Hipp 힙 콤비오틱 유기농 3단계 800g 출산/육아 > 분유 > 수입분유
+- text: 남양유업 아이엠마더 액상 3단계 240ml x96개  출산/육아 > 분유 > 국내분유
+- text: 일동후디스 프리미엄 산양분유 3단계 800g x 1개 [육아] 분유_파스퇴르 무항생제 위드맘 3단계 750g 출산/육아 > 분유 >
+    국내분유
+- text: 일동후디스 프리미엄 산양분유 1단계 800g x 1개 [음료] 탄산음료_웰치스제로오렌지355ml24캔 출산/육아 > 분유 > 국내분유
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: mini1013/master_domain
+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: accuracy
+      value: 1.0
+      name: Accuracy
+---
+
+# 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                                                                                                                                                                                                                                                                                                                             |
+|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 2.0   | <ul><li>'셀렉스 매일 마시는 프로틴 12l 160ml × 48개 출산/육아 > 분유 > 특수분유'</li><li>'일동후디스 초유밀플러스2단계 1캔(1gx90포))  출산/육아 > 분유 > 특수분유'</li><li>'gvp 스마트폰 카드포켓 스마트링블랙  출산/육아 > 분유 > 특수분유'</li></ul>                                                                                                                                                      |
+| 0.0   | <ul><li>'매일유업 앱솔루트 명작 2FL 액상 2단계 240ml 24개 x2개  출산/육아 > 분유 > 국내분유'</li><li>'매일유업 앱솔루트 센서티브 1단계 900g x 1개 [라면] 봉지라면_얼큰한 너구리 120g 20개 출산/육아 > 분유 > 국내분유'</li><li>'매일유업 앱솔루트 센서티브 1단계 900g x 1개 [음료] 우유두유_삼육검은콩앤칼슘파우치190ml40팩 출산/육아 > 분유 > 국내분유'</li></ul>                                                                              |
+| 1.0   | <ul><li>'힙 압타밀 HA 뢰벤짠 밀라산 홀레 퇴퍼 베바 세레락 프레 2단계 콤비오틱 무전분 산양 [퇴퍼] Töpfer_퇴퍼 락타나 600g (최대8통)_[1통] xPRE Topfer 출산/육아 > 분유 > 수입분유'</li><li>'뉴트리시아 압타밀 프로누트라 어드밴스 2단계 800g [음료] 탄산음료_데미소다피치250ml30캔 출산/육아 > 분유 > 수입분유'</li><li>'퇴퍼 홀레 뢰벤짠 힙 노발락 압타밀 무전분 AR 킨더밀쉬 압타밀 오가닉(New)_오가닉 2 800g 1통_◆dm4056631003169_1◆ 출산/육아 > 분유 > 수입분유'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 1.0      |
+
+## 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_bc6")
+# Run inference
+preds = model("남양유업 아이엠마더 액상 3단계 240ml x96개  출산/육아 > 분유 > 국내분유")
+```
+
+<!--
+### 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   | 7   | 14.9429 | 30  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 70                    |
+| 1.0   | 70                    |
+| 2.0   | 70                    |
+
+### Training Hyperparameters
+- batch_size: (256, 256)
+- num_epochs: (30, 30)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 50
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch   | Step | Training Loss | Validation Loss |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0238  | 1    | 0.4943        | -               |
+| 1.1905  | 50   | 0.4806        | -               |
+| 2.3810  | 100  | 0.1671        | -               |
+| 3.5714  | 150  | 0.0003        | -               |
+| 4.7619  | 200  | 0.0           | -               |
+| 5.9524  | 250  | 0.0           | -               |
+| 7.1429  | 300  | 0.0           | -               |
+| 8.3333  | 350  | 0.0           | -               |
+| 9.5238  | 400  | 0.0           | -               |
+| 10.7143 | 450  | 0.0           | -               |
+| 11.9048 | 500  | 0.0           | -               |
+| 13.0952 | 550  | 0.0           | -               |
+| 14.2857 | 600  | 0.0           | -               |
+| 15.4762 | 650  | 0.0           | -               |
+| 16.6667 | 700  | 0.0           | -               |
+| 17.8571 | 750  | 0.0           | -               |
+| 19.0476 | 800  | 0.0           | -               |
+| 20.2381 | 850  | 0.0           | -               |
+| 21.4286 | 900  | 0.0           | -               |
+| 22.6190 | 950  | 0.0           | -               |
+| 23.8095 | 1000 | 0.0           | -               |
+| 25.0    | 1050 | 0.0           | -               |
+| 26.1905 | 1100 | 0.0           | -               |
+| 27.3810 | 1150 | 0.0           | -               |
+| 28.5714 | 1200 | 0.0           | -               |
+| 29.7619 | 1250 | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0
+- Sentence Transformers: 3.3.1
+- Transformers: 4.44.2
+- PyTorch: 2.2.0a0+81ea7a4
+- Datasets: 3.2.0
+- Tokenizers: 0.19.1
+
+## 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_bc",
+  "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": "4.44.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

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+    "transformers": "4.44.2",
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+  },
+  "prompts": {},
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+  "similarity_fn_name": "cosine"
+}

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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+}