Push model using huggingface_hub.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 바나나보트 태닝 드라이 오일 선스프레이 선스크린 SPF15 170g 코코넛 오일 옵션없음 아프로
+- text: 바나나보트 딥 태닝 스프레이 오일 SPF4 236ml Deep Tanning Spray 옵션없음 제이프로젝트
+- text: PGA 워터 선스프레이 80ml 워터프루프 골프 물놀이용 뿌리는 선크림 옵션없음 그레이스환
+- text: 알롱 컨디셔닝 알로에젤 알로에 수딩젤 500ml 컨디셔닝 수딩젤 500ml 메리앤
+- text: '[해외] Devoted Creations 블랙 옵세션 블랙 브론저 13.5온스 (212588) 옵션없음 올 스토어'
+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: accuracy
+      value: 0.7966457023060797
+      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:** 7 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                                                                                                                                                                                                                                |
+|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 6.0   | <ul><li>'태닝오일 150ml 코코넛오일 모든순간 구릿빛피부 2WBF81AD8 옵션없음 씨티몰'</li><li>'Bronzer Tanning Lotion by 디자이너스킨 400ml 옵션없음 라이브프롬잇'</li><li>'New Sunshine Australian Gold 브론즈 가속기 241g(8.5온스) 옵션없음 비포유'</li></ul>                                     |
+| 5.0   | <ul><li>'라이크듀 90%알로에베라 듀 수딩젤 겔, 250ml, 1개 250ml × 1개 하이블리스'</li><li>'라디발 어린이 애프터선 로션 200ml 옵션없음 Held Farm e.K.'</li><li>'쿨 다운 알로에베라 젤 비건 After Sun 케어 코코아 버터 to Soothe & Hydrate Sun Bum 미국직구 여름휴가 자외선차단 선케어 227g 1개 옵션없음 자대'</li></ul> |
+| 1.0   | <ul><li>'SVR 선시큐어 SPF50 포켓 스프레이 20ml 옵션없음 주식회사하늘'</li><li>'베이비가닉스 선스프레이 SPF50 177ml x 2개 옵션없음 유어 페이버릿'</li><li>'선스프레이 청광 진주 마린 제이엠솔루션 선 스프레이 펄 옵션없음 유토피아'</li></ul>                                                                     |
+| 0.0   | <ul><li>'가히 에어리핏 런닝맨 썬스틱 14g 1+1 옵션없음 ㈜코리아테크'</li><li>'[글루타넥스][이주연PICK] UV 글로우밤 (11g) 1+1 (11g+11g) 넥서스파마 주식회사'</li><li>'AHC 마스터즈 에어리치 선스틱 10g 1개 (SPF50+/PA++++) J4W_03)선스틱 10g 1개 (주)카버코리아'</li></ul>                                 |
+| 4.0   | <ul><li>'최대 49% / 달바 비건 톤업 선쿠션 15gX2개 [단품] 톤업 선쿠션 15g(1개) 주식회사 달바글로벌'</li><li>'지엘리 백탁 뭉침 없는 온가족 올투게더 쿨링 선쿠션 지엘리 선쿠션 본품 81%할인 주식회사 브랜더스윈'</li><li>'라운드랩 자작나무 수분 선쿠션 15g(SPF50+) 옵션없음 쑤기쓰마켓'</li></ul>                                    |
+| 3.0   | <ul><li>'파이진 피에스아이 선 블록 옵션없음 비코빅(VIKOBIG)'</li><li>'[당일출고] 셀퓨전씨 레이저 UV 썬스크린 50ml(신형, SPF50+) 옵션없음 제이에이치컴퍼니'</li><li>'비욘드 피토 아쿠아 트루 워터 선 베이스 80ml 옵션없음 에이티씨무역'</li></ul>                                                                 |
+| 2.0   | <ul><li>'셀이야 피부보호 선케어 2종세트 (무기자차 선크림+다크닝없는 블링커버 비비BB)'</li><li>'라로제 썸머 3종 클린 선스틱 SPF 50 + 선크림 SPF 50+ + 멀티오일 세트'</li><li>'[선케어SET]온그리디언츠 선케어 2종 세트(톤업선로션+선스틱)'</li></ul>                                                                |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.7966   |
+
+## 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_bt7_test")
+# Run inference
+preds = model("알롱 컨디셔닝 알로에젤 알로에 수딩젤 500ml 컨디셔닝 수딩젤 500ml 메리앤")
+```
+
+<!--
+### 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   | 4   | 10.1504 | 24  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 20                    |
+| 1.0   | 10                    |
+| 2.0   | 17                    |
+| 3.0   | 28                    |
+| 4.0   | 20                    |
+| 5.0   | 15                    |
+| 6.0   | 23                    |
+
+### Training Hyperparameters
+- batch_size: (512, 512)
+- num_epochs: (40, 40)
+- 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0769  | 1    | 0.4951        | -               |
+| 3.8462  | 50   | 0.2316        | -               |
+| 7.6923  | 100  | 0.0262        | -               |
+| 11.5385 | 150  | 0.0136        | -               |
+| 15.3846 | 200  | 0.0047        | -               |
+| 19.2308 | 250  | 0.0001        | -               |
+| 23.0769 | 300  | 0.0001        | -               |
+| 26.9231 | 350  | 0.0001        | -               |
+| 30.7692 | 400  | 0.0001        | -               |
+| 34.6154 | 450  | 0.0           | -               |
+| 38.4615 | 500  | 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_bt_test",
+  "architectures": [
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+  "model_type": "roberta",
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+  "num_hidden_layers": 12,
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+  "position_embedding_type": "absolute",
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+  "transformers_version": "4.44.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

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+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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model.safetensors

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modules.json

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+  }
+]

sentence_bert_config.json

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+  "do_lower_case": false
+}

special_tokens_map.json

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tokenizer_config.json

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