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

@@ -0,0 +1,221 @@
+---
+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: '[NEW] 해서린 포어 멜팅 와이드 슈링코팩 4매 옵션없음 휴리빙'
+- text: 썸바이미 레티놀 인텐스 리액티베이팅 마스크 22g [5매입] 옵션없음 (주)페렌벨
+- text: 거즈 옵션없음 국왕컴퍼니
+- text: 아이디 에이지 페이스핏 압박 타이트닝 석고 마스크 석고팩 1박스(4매) 옵션없음 주식회사 케이디와이코스메틱
+- text: 골드 필 오프 마스크 1000g​ 금 고무팩 모델링 럭셔리 호텔 관리실 피부과 팩도구 옵션없음 뷰티렐라
+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: 0.6166365280289331
+      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:** 8 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>'바이오던스 바이오 콜라겐 리얼 딥 마스크 4매 콜라겐마스크팩 저분자 블망몰'</li><li>'[무료배송] 바세린 시트마스크 팩 10매 2개 (Type 선택)[34514957] NS홈쇼핑'</li><li>'폴리머 수화겔 하이드로겔 마스크 팩세트 35gx4P 주식회사 에이에이앤티'</li></ul>                                           |
+| 6.0   | <ul><li>'네슈라 스킨후리 까만 참숯 코팩 48매(6팩) 블랙헤드 옵션없음 하프웨이'</li><li>'일소 코팩 네추럴 마일드 클리어 노우즈 팩 10매 옵션없음 아비스 몰'</li><li>'장영란 아기코세트 멜팅클리어패드 30매 + 모공앰플 30ml 옵션없음 세일러15'</li></ul>                                                     |
+| 5.0   | <ul><li>'비플레인 녹두 포어 클레이 마스크 120ml 1개 옵션없음 주식회사 제이엔씨글로벌'</li><li>'코나피딜 그린박신 옵션없음 미솔피부관리실'</li><li>'브링그린 티트리 시카 포어 클레이 팩 120g 옵션없음 제이오션'</li></ul>                                                                         |
+| 4.0   | <ul><li>'메노킨 30초 퀵 버블 마스크_브라이트 옵션없음 주식회사 포레스트에비뉴'</li><li>'포에이엠 게솔자 여드름 수면 크림팩 좁쌀 화농성 청소년 성인 50ml 3개 구매하기 용가리형'</li><li>'[빌리프] 슈퍼나이츠 멀티 비타민 마스크 점보 배럴 에디션 120 mL 옵션없음 (주)엘지생활건강'</li></ul>                               |
+| 7.0   | <ul><li>'1+1 리숨 280콜라겐 펩타이드 딥 팩 70g 명품보습 티원 바르는 콜라겐 72% 콜라겐팩 1개+스파츌라1개 주식회사 케이에스엠월드'</li><li>'쇼핑윈도 오그체 실면도팩 80g 솜털 제거 참숯 필오프팩 옵션없음 주식회사 네슈라화장품'</li><li>'메디필 레드락토 콜��겐 랩핑마스크 70ml 물광 리프팅 팩 옵션없음 웬디스룸'</li></ul>           |
+| 2.0   | <ul><li>'아비브 약산성 pH 시트 마스크 어성초 핏 5매 (+1매) (순한진정) 옵션없음 토토리즈'</li><li>'녹차 에센스 마스크팩 100매 네이처바이 대용량팩 녹차 에센스 마스크팩 100매 네이처바이 대용 생활꿀템컴퍼니'</li><li>'13 에어뮤즈 멜라이드 패치스탠다드 아이패치 야외 5매 옵션없음 네이비몰'</li></ul>                         |
+| 3.0   | <ul><li>'린제이 프리미엄 쿨 티트리 모델링 마스크 고무팩 820g 1022286 옵션없음 굿데이'</li><li>'린제이 프리미엄 쿨 티트리 모델링 마스크 고무팩 820g 1021782 옵션없음 배스테인'</li><li>'데쌍브르 골드 필 오프 모델링팩 1kg 데쌍브르 골드 필 오프 모델링팩  1kg 마가렛스킨'</li></ul>                            |
+| 0.0   | <ul><li>'마리꼬 인스턴트 수딩 마스크 50ml / 150ml 마마리꼬 인스턴트 수딩 마스크 150ml_마리꼬 튜브형 샘플 3ml (랜덤) 4개 오늘의뷰티'</li><li>'PPC 고주파크림 800ml 1+1 옵션없음 메르헨랩'</li><li>'태국 할아버지 오일 Siang Pure Oil chai 7ml 옵션없음 JOEUN C&C COMPANY LIMITED'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.6166   |
+
+## 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_bt2_test")
+# Run inference
+preds = model("거즈 옵션없음 국왕컴퍼니")
+```
+
+<!--
+### 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   | 9.7453 | 23  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 16                    |
+| 1.0   | 10                    |
+| 2.0   | 42                    |
+| 3.0   | 21                    |
+| 4.0   | 20                    |
+| 5.0   | 19                    |
+| 6.0   | 15                    |
+| 7.0   | 18                    |
+
+### 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0625 | 1    | 0.4744        | -               |
+| 3.125  | 50   | 0.2863        | -               |
+| 6.25   | 100  | 0.0186        | -               |
+| 9.375  | 150  | 0.0002        | -               |
+| 12.5   | 200  | 0.0001        | -               |
+| 15.625 | 250  | 0.0001        | -               |
+| 18.75  | 300  | 0.0001        | -               |
+| 21.875 | 350  | 0.0001        | -               |
+| 25.0   | 400  | 0.0001        | -               |
+| 28.125 | 450  | 0.0001        | -               |
+| 31.25  | 500  | 0.0001        | -               |
+| 34.375 | 550  | 0.0001        | -               |
+| 37.5   | 600  | 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": [
+    "RobertaModel"
+  ],
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+  "eos_token_id": 2,
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+  "hidden_act": "gelu",
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+  "hidden_size": 768,
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+  "model_type": "roberta",
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+  "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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+{
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+    "transformers": "4.44.2",
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+  },
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+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+}

model.safetensors

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model_head.pkl

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modules.json

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+    "type": "sentence_transformers.models.Pooling"
+  }
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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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