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: '[바다원] 깨끗한 돌김자반볶음 오리지널 40g x 5봉  (주)씨제이이엔엠'
+- text: 쭈꾸미사령부 매운맛 300g 3개 불타는 매운맛 원츄쟈챠
+- text: 냉동 새우 튀김 300g 6미 10미 대용량 업소용 빵가루 왕새우튀김 코코넛쉬림프 360g (30미) 주식회사 더꽃게
+- text: 잇투헤븐 팔당 불 오징어 매운 오징어 볶음 400g 쭈꾸미도사 쭈꾸미볶음 01.팔당불오징어400g 1팩 (주)잇투헤븐
+- text: CJ 명가김 파래김 4g 16입  트릴리어네어스
+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: 0.8689361702127659
+      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:** 6 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>'훈제연어(통) 약1.1kg 냉동연어 필렛 슬라이스 칠레산 HACCP 국내가공 화이트베어 화이트베어 훈제연어슬라이스 ±1.3kg 주식회사 셀피'</li><li>'안동간고등어 80g 10팩(5마리) 동의합니다_80g 10팩(5마리) 델리아마켓'</li><li>'제주 국내산 손질 고등어 2KG 한팩150g이상 11-12팩 3KG(16-19팩) 효명가'</li></ul> |
+| 1.0   | <ul><li>'동원F&B 양반 김치맛 김부각 50g 1개 동원F&B 양반 김치맛 김부각 50g 1개 다팔아스토어'</li><li>'오뚜기 옛날 자른미역 50G  대성상사'</li><li>'환길산업 섬마을 해초샐러드 냉동 해초무침 2kg  제루통상'</li></ul>                                                                 |
+| 0.0   | <ul><li>'Fish Tree 국물용멸치 1.3kg  케이원'</li><li>'Fish Tree 국물용 볶음용 멸치 1.3kg 1kg 뼈건강 깊은맛 육수 대멸치 좋은식감 국물용 멸치 1.3kg 유라너스'</li><li>'Fish Tree 국물용 멸치 1.3kg  이숍'</li></ul>                                                    |
+| 3.0   | <ul><li>'랭킹수산 장어구이 혼합 140gx20팩(데리야끼10매콤10) -인증  제이원무역'</li><li>'올반 대왕 오징어튀김 400g  나라유통'</li><li>'바다愛한끼 이원일 연평도 꽃게 해물탕 760g 소스포함 2팩  (주)티알엔'</li></ul>                                                                 |
+| 5.0   | <ul><li>'날치알 동림 담홍 레드 800G [800G][동림]날치알(골드)(팩) 주식회사 명품씨푸드'</li><li>'날치알 동림 담홍 레드 800G [800G][동림]날치알(레드)(팩) 주식회사 명품씨푸드'</li><li>'날치알 동림 담홍 레드 800G [800gG[코아]날치알[골드] 주식회사 명품씨푸드'</li></ul>                            |
+| 4.0   | <ul><li>'명인오가네 연어장 250g  명인오가네몰'</li><li>'[나브연] 수제 간장 연어장 750g 덜짜�� 주희종'</li><li>'[나브연] 수제 간장 연어장 500g 보통 주희종'</li></ul>                                                                                               |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.8689 |
+
+## 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_fd11")
+# Run inference
+preds = model("CJ 명가김 파래김 4g 16입  트릴리어네어스")
+```
+
+<!--
+### 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   | 9.1164 | 23  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 50                    |
+| 3.0   | 50                    |
+| 4.0   | 50                    |
+| 5.0   | 25                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0233  | 1    | 0.4609        | -               |
+| 1.1628  | 50   | 0.2116        | -               |
+| 2.3256  | 100  | 0.0876        | -               |
+| 3.4884  | 150  | 0.0442        | -               |
+| 4.6512  | 200  | 0.0254        | -               |
+| 5.8140  | 250  | 0.0133        | -               |
+| 6.9767  | 300  | 0.0252        | -               |
+| 8.1395  | 350  | 0.0176        | -               |
+| 9.3023  | 400  | 0.0116        | -               |
+| 10.4651 | 450  | 0.004         | -               |
+| 11.6279 | 500  | 0.0231        | -               |
+| 12.7907 | 550  | 0.0023        | -               |
+| 13.9535 | 600  | 0.0017        | -               |
+| 15.1163 | 650  | 0.0002        | -               |
+| 16.2791 | 700  | 0.0001        | -               |
+| 17.4419 | 750  | 0.0001        | -               |
+| 18.6047 | 800  | 0.0001        | -               |
+| 19.7674 | 850  | 0.0001        | -               |
+
+### 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_fd",
+  "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": {},
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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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+}