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: ULG 미용가위 스테인리스 셀프 헤어 앞머리 숱 전문가용 280870 Gold 아스가르드4
+- text: 마사지 안마 흡착 허리 어깨 목 실리콘 U자형 옵션없음 고르다몰
+- text: 관리 눈썹면도기 면도 미용 니켄 일자형 눈썹칼 옵션없음 프렌드리빙
+- text: 샤이세이프 눈썹 면도기3P 3813 칼 미용칼 접이식 정리 휴대용 옵션없음 모든다팜
+- text: 롤온공병 50ml 화장품 휴대용 여행용 소분 케이스 옵션없음 쇼핑하우스
+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: 0.6862416107382551
+      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                                                                                                                                                                                                   |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 2.0   | <ul><li>'실리콘 섹시 패치 여성 가리개 니플 밴드 패드 유두 남성 상품선택_플라워 오네몰'</li><li>'왁싱 스트립 부직포 페이퍼 제포 무슬린천 컷팅형 100매 옵션없음 리얼뷰티'</li><li>'남성 매너 여성 가리개 밴드 패치 가슴 살색 꼭지 원형(2P) 삼티아고'</li></ul>                                     |
+| 0.0   | <ul><li>'더림 유기농 율무 추출물 150ml 150ml 아이앤비 바이오랩'</li><li>'카페인 커피 샴푸바만들기 교육용 수제비누 키트 DIY 자원순환 업사이클링 옵션없음 처음(CHOEUM)'</li><li>'솔루빌라이저 1 리터 옵션없음 주식회사 월터엔터프라이즈'</li></ul>                                        |
+| 6.0   | <ul><li>'고양이귀 세안 헤어밴드 5p세트 KD-8679 목욕용 세면 샤워용 극세사 옵션없음 초이스리테일 5'</li><li>'루시피 클립 위빙콤(색상랜덤) + 파마 실리콘 밴드(20개입) 옵션없음 가리유통'</li><li>'액세서리 사우나헤어캡 자동차 95760 A7CC1 B4 뷰 카메라 기아 세라토 백업 Dark Grey 셀로스중랑'</li></ul> |
+| 5.0   | <ul><li>'NEW갸름마스크턱볼살용 얼굴 마스크 턱볼살 TYPE 2 옵션없음 포켐폼'</li><li>'도자기 괄사(마사지도구)-바다수영냥 스퀘어 옵션없음 루아링'</li><li>'에이브 면분첩 - 중형 옵션없음 하민하이'</li></ul>                                                                    |
+| 7.0   | <ul><li>'물방울 퍼퓸 20ml 향수 용기 공병 니치 향수병 스프레이 스크류타입 퍼퓸 32.디지오 투명 50ml-1개 주식회사 강군'</li><li>'미니 까멜리아 공병 용기 휴대용 키링 립밤 케이스 옵션없음 코뿔소앵글'</li><li>'산리오 캐릭터 휴대용 스프레이 공병 시나모롤 쿠로미 포차코 헬로키티 이루리잡화점'</li></ul>            |
+| 3.0   | <ul><li>'눈썹정리칼 눈썹 트리��� 세트 스틸 클립 빗 가위 3개 3PC 벤자민 잡화점'</li><li>'접이식 눈썹 면도기 눈썹칼 눈썹정리 xuG DFP 잘드는 기본 GGH 옵션없음 리치오토'</li><li>'쪽집게 전문 스테인리스 스틸 고품질 보석 족집게, DIY 다이아몬드 주얼리 제작 도구 02 elbow 마이나인쓰'</li></ul>          |
+| 4.0   | <ul><li>'2주지속 리얼 문신 팔손가락 타투스티커 티안나는 반영구 방수 헤나 문신 나비 세트 A6 ( 2장세트 ) 에테르넬'</li><li>'산리오 쁘띠 타투 스티커 캐릭터 09 핑크 프렌즈 옵션없음 보라나마루'</li><li>'타투스티커 타투 휴가 나비타투 5종 A형 B형 컬러 나비 꽃 A형(5장) 태흥정밀'</li></ul>                |
+| 1.0   | <ul><li>'브러쉬세트 눈썹 1/2/3Pcs 윤곽 브러시 각도 얇은 플랫 라이너 휴대용 전문 눈 2pcs Set 리마104'</li><li>'아이브로우브러쉬 8pcs Cardcaptor 세트 파운데이션 섀도우 브로우 Pincel 8pcs_CHINA 드림비정선'</li><li>'실버 고급립솔 립붓 옵션없음 엠디와이'</li></ul>               |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.6862   |
+
+## 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_bt5_test")
+# Run inference
+preds = model("마사지 안마 흡착 허리 어깨 목 실리콘 U자형 옵션없음 고르다몰")
+```
+
+<!--
+### 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   | 10.0538 | 20  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 12                    |
+| 1.0   | 12                    |
+| 2.0   | 12                    |
+| 3.0   | 19                    |
+| 4.0   | 20                    |
+| 5.0   | 27                    |
+| 6.0   | 13                    |
+| 7.0   | 15                    |
+
+### 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    | 0.4878        | -               |
+| 3.8462  | 50   | 0.236         | -               |
+| 7.6923  | 100  | 0.0277        | -               |
+| 11.5385 | 150  | 0.0102        | -               |
+| 15.3846 | 200  | 0.0003        | -               |
+| 19.2308 | 250  | 0.0001        | -               |
+| 23.0769 | 300  | 0.0001        | -               |
+| 26.9231 | 350  | 0.0001        | -               |
+| 30.7692 | 400  | 0.0001        | -               |
+| 34.6154 | 450  | 0.0001        | -               |
+| 38.4615 | 500  | 0.0001        | -               |
+
+### 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,
+  "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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+{
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+    "transformers": "4.44.2",
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+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "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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+}