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 +1,237 @@
+---
+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: '[매장정품] 로우퀘스트 베리어 인핸싱 클렌저 190ml, 1개 옵션없음 타이거커머스'
+- text: 마녀공장 갈락토미 엔자임 필링젤75ML 포장 없음 옵션없음 주식회사 지에스원(GS ONE CO.,LTD.)
+- text: 멘톨비누 바디바 어성초 등드름 수제 목욕 비누 대용량 125g 옵션없음 위컴퍼니
+- text: (트러블 피부 추천) 샵벨르 딥포어젤 100ml x 추가 100ml_엘리시어 20ml 럽스킨(LUVSKIN)
+- text: 바이오뷰텍 바이오옵틱스 아이크린 리드 클리너 30매 1021641 옵션없음 굿데이
+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: 0.752
+      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:** 12 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                                                                                                                                                                                                               |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 5.0   | <ul><li>'셀리맥스 프레시 클렌징오일 150ml, 1개 옵션없음 (주)앱솔브랩'</li><li>'시세이도 티스 딥 오프 오일 320ml 1개 옵션없음 디제이커머스(DJ커머스)'</li><li>'이니스프리 애플씨드 클렌징 오일 150mL 옵션없음 (SJ)이커머스'</li></ul>                                                        |
+| 9.0   | <ul><li>'식물나라 제주 탄산수 딥 클렌징 티슈 100매 옵션없음 (주)노아드'</li><li>'셀리맥스 수정화장 패드 30매 옵션없음 원더케이 주식회사'</li><li>'스트라이덱스 센시티브 패드 90매 최신제조정품 유통기한2026년. 옵션없음 셀러N제니스 컴퍼니'</li></ul>                                                     |
+| 3.0   | <ul><li>'티나솝 국화비누 조문답례품 장례 49재 수제비누 2구_필요없습니다 티나솝'</li><li>'부케가르니 딥 퍼퓸 비누 베이비 파우더 100g 옵션없음 도매창고'</li><li>'바이오티크 바질앤파슬리 비누 150g 오렌지필150g (주)밸루스'</li></ul>                                                             |
+| 10.0  | <ul><li>'[본사제품최신제조] 인셀덤 엑티브 클린업 파우더 90g 폼 클렌징 가루 효소 세안제 옵션없음 미라클'</li><li>'동국제약 센텔리안24 마데카 엔자임 클렌징 파우더 60g x 1개 동국제약 센텔리안'</li><li>'인셀덤 엑티브 클린업 파우더 90g 래디언솜 앰플 아이코스메틱'</li></ul>                                      |
+| 2.0   | <ul><li>'[당일출고] 청미정 알로에 발효 클렌징 밀크 200ml 옵션없음 현영'</li><li>'청미정 알로에 발효 클렌징 밀크 200ml 1개 동의함 블랙마이클'</li><li>'청미정 알로에 발효 클렌징 밀크 200ml 옵션없음 옐로우로켓'</li></ul>                                                                 |
+| 0.0   | <ul><li>'바이오더마 센시비오 아이 립앤아이리무버 125ml 옵션없음 (주)노아드'</li><li>'오큐소프트 리드 스크럽 플러스+클린징패드120매 옵션없음 아이리움헬스케어'</li><li>'오큐소프트 리드스크럽 플러스 눈꺼풀세정제 눈세척 눈청소 눈기름샘 마이봄샘 다래끼 아이클린 11203414 오큐소프트 리드스크럽플러스 오큐소프트 리드스크럽플러스 메이써니'</li></ul> |
+| 11.0  | <ul><li>'마미레시피 황유자 고마쥬 클렌저 100ml 1개 옵션없음 건강드림'</li><li>'[당일출고] 맥스클리닉 퓨리티톡 브라이트닝 오일 폼 310ml 옵션없음 현영'</li><li>'정케이스 프리메라 바하 버블 필링 클렌저 200ml 옵션없음 맥스베스트'</li></ul>                                                        |
+| 8.0   | <ul><li>'칠전몰 녹차 클렌징크림 500g 화장지우기 클렌징로션 클렌징밀크 옵션없음 칠전'</li><li>'MP 크린징 크림 순한 클렌저 화장 지우기 촉촉한 클렌징 노폐물제거 300ml 묵은각질 옵션없음 민트펌킨'</li><li>'위아리턴 딥클렌징크림 300g 남성클렌징폼 옵션없음 위아리턴'</li></ul>                                       |
+| 6.0   | <ul><li>'바이오더마 하이드라비오 H2O 500ml 옵션없음 웨일인블루'</li><li>'바이오더마 센시비오 H2O 500ml 크레알린 옵션없음 주식회사 더블유에스엠와이'</li><li>'바이오더마 센시비오 H2O 500ml 펌프형 옵션없음 조은스마트'</li></ul>                                                             |
+| 7.0   | <ul><li>'셀라딕스 아크네 컨트롤 131 젤 클렌저 110ml 1개 옵션없음 (주)이삼오구'</li><li>'코스알엑스 약산성 굿모닝 젤 클렌저 150ml 9792539 옵션없음 에필로리아'</li><li>'보태니컬 키네틱스 퓨리파잉 젤 클렌저 500ml 옵션없음 엔크라이드'</li></ul>                                                |
+| 4.0   | <ul><li>'여행용세트 애경 2호 유럽여행 옵션없음 아빠나이거사줘'</li><li>'동인비 클렌징 2종 미니세트 동인비'</li><li>'아크웰 감초수 피에이치 클렌징 젤 폼 + 버블 프리 젤 2종 세트 아크웰 ACWELL'</li></ul>                                                                              |
+| 1.0   | <ul><li>'메디필 엑스트라 슈퍼9 플러스 2.0 250ml 듀얼화장솜40매 1022076 옵션없음 메가랜드'</li><li>'셀리맥스 바디 브라이트닝 패드 60매 / 색소 침착 미백 케어 팔꿈치 무릎 바디 브라이트닝 패드 1개 (주)앱솔브랩'</li><li>'메디스코 약초필링 키트 해초 얼굴각질제거 파우더1g솔루션8ml 시카크림 옵션없음 사과컴퍼니'</li></ul>      |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.752    |
+
+## 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_bt9_test")
+# Run inference
+preds = model("멘톨비누 바디바 어성초 등드름 수제 목욕 비누 대용량 125g 옵션없음 위컴퍼니")
+```
+
+<!--
+### 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   | 9.0359 | 19  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 20                    |
+| 1.0   | 27                    |
+| 2.0   | 20                    |
+| 3.0   | 27                    |
+| 4.0   | 15                    |
+| 5.0   | 20                    |
+| 6.0   | 20                    |
+| 7.0   | 18                    |
+| 8.0   | 20                    |
+| 9.0   | 22                    |
+| 10.0  | 17                    |
+| 11.0  | 25                    |
+
+### 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 |
+|:-----:|:----:|:-------------:|:---------------:|
+| 0.04  | 1    | 0.4829        | -               |
+| 2.0   | 50   | 0.3452        | -               |
+| 4.0   | 100  | 0.0756        | -               |
+| 6.0   | 150  | 0.0458        | -               |
+| 8.0   | 200  | 0.0343        | -               |
+| 10.0  | 250  | 0.0208        | -               |
+| 12.0  | 300  | 0.0066        | -               |
+| 14.0  | 350  | 0.0021        | -               |
+| 16.0  | 400  | 0.0005        | -               |
+| 18.0  | 450  | 0.0004        | -               |
+| 20.0  | 500  | 0.0003        | -               |
+| 22.0  | 550  | 0.0003        | -               |
+| 24.0  | 600  | 0.0002        | -               |
+| 26.0  | 650  | 0.0002        | -               |
+| 28.0  | 700  | 0.0002        | -               |
+| 30.0  | 750  | 0.0002        | -               |
+| 32.0  | 800  | 0.0002        | -               |
+| 34.0  | 850  | 0.0002        | -               |
+| 36.0  | 900  | 0.0002        | -               |
+| 38.0  | 950  | 0.0002        | -               |
+| 40.0  | 1000 | 0.0002        | -               |
+
+### 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"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "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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+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.2.0a0+81ea7a4"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

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+size 442494816

model_head.pkl

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modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "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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+    "lstrip": false,
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+      "lstrip": false,
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+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}