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 +1,222 @@
+---
+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: '[현대백화점][비비안](RU1260) 40% 가격인하 순면 80수 기본 남성런닝 95 (주)현대백화점'
+- text: 부드러운 터치감 남성 실켓가공 런닝 트렁크 팬티 세트 VMV4183VMP4183N/비너스 브라운_런닝105-팬티105 롯데쇼핑(주)
+- text: '[리더스] 신축성 좋은 복부 코르셋 땀복 남자 바지 (15005144) 블랙_XL 신세계몰'
+- text: 탑텐 탑텐 공용 플란넬 라운지웨어 세트 MSC4UI3001 rva-482878f BE_L(540) 라비아세개
+- text: BYC 남성용 50수 순면 민소매 그랜드 런닝 2호 백색 1매 BYI6035 95 (주)대화언더웨어
+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: 0.8497076023391813
+      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                                                                                                                                                                                                                                                           |
+|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 5.0   | <ul><li>'CK퍼포먼스 24 SUMMER 여름셋업 남여공용 4종 [0001]블랙 90(S) CJONSTYLE_LIVE'</li><li>'CALVIN KLEIN UNDERWEAR 여성 모던 코튼 T팬티_F3786D001 F3786D001 블랙_M 럭스펄스'</li><li>'[갭][갭] 옴므 트렁크 6종 택1 GPMTR1O30T 네이비/L(100) 패션플러스'</li></ul>                                              |
+| 1.0   | <ul><li>'[와코루](신세계마산점)선염 모달 + 면 스판 스트라이프 조끼런닝 삼각 팬티 세트(WMV2378RWMP2378P) 95_100 주식회사 에스에스지닷컴'</li><li>'남자 속옷 등산 스포츠 SET 자전거 축구 스프츠 골프 백색_100 꼬북샵'</li><li>'싸이로컴팩 면모달 선염스트라이프 런닝RU1695T 네이비_100 신세계몰'</li></ul>                                                     |
+| 3.0   | <ul><li>'CJ [리복] 스피드윅 기모 웜에어 상하의 2종 세트 남성 최신상 택일 옵션01.RBMYIEM01_00_100 (주)씨제이이엔엠'</li><li>'아르메데스 남성용 히트기모 발열내의 터틀넥 상의 AR-25 3매 블랙_M (주)아르메데스'</li><li>'[기능성 의류 BEST] 시원한 냉감 기능은 기본! 완벽한 자외선 차단! 기능성 티셔츠/조거팬츠/등산바지/아웃도어 의류 01.TM-MZS303_M_ZZGRY 테슬라_TSLA'</li></ul> |
+| 0.0   | <ul><li>'남자 쿨 티셔츠 남성 냉감 나시티 기능성 반팔티 쿨링 EVE 화이트_100 에브리씽굿'</li><li>'비비안 모다아울렛 비비안 젠토프 텐셀솔리드 기본 반팔런닝 RU1239T 네이비_95 MODA아울렛'</li><li>'탑텐 TOPTEN 남성 쿨에어 크루넥 매쉬 탱크_MSD2UL1201 BK_100 가투투'</li></ul>                                                                    |
+| 2.0   | <ul><li>'니플 나시 남자보정 속옷이너핏여유증커버남성뱃살가리개꼭지가슴압박복가리기티 남자보정나시 보급형/L/화이트 조니멀티샵'</li><li>'하라마키 배워머 더블 배워머 보온복대 남성용 HT-LunesDB-Charcoal-M BESTYOURS'</li><li>'고급 따뜻한 남자 밴딩 기모 레깅스 겨울 발열 내복 바지 보온 타이즈 블랙_2XL 사랑니'</li></ul>                                                  |
+| 4.0   | <ul><li>'[오르시떼](센텀시티점)남성 D123 오니리크 반소매 상하 S 신세계백화점'</li><li>'(신세계마산점)오르시떼남성 D105 브데뜨 긴소매 상하 S 신세계백화점'</li><li>'JAJU 남 라이트 밍크 플리스 파자마 세트 블루 L 리치쇼핑'</li></ul>                                                                                                       |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.8497 |
+
+## 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_ap0")
+# Run inference
+preds = model("[리더스] 신축성 좋은 복부 코르셋 땀복 남자 바지 (15005144) 블랙_XL 신세계몰")
+```
+
+<!--
+### 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   | 9.5967 | 24  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 50                    |
+| 3.0   | 50                    |
+| 4.0   | 50                    |
+| 5.0   | 50                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0213  | 1    | 0.4362        | -               |
+| 1.0638  | 50   | 0.3126        | -               |
+| 2.1277  | 100  | 0.0687        | -               |
+| 3.1915  | 150  | 0.0294        | -               |
+| 4.2553  | 200  | 0.0006        | -               |
+| 5.3191  | 250  | 0.0003        | -               |
+| 6.3830  | 300  | 0.0002        | -               |
+| 7.4468  | 350  | 0.0002        | -               |
+| 8.5106  | 400  | 0.0001        | -               |
+| 9.5745  | 450  | 0.0001        | -               |
+| 10.6383 | 500  | 0.0001        | -               |
+| 11.7021 | 550  | 0.0001        | -               |
+| 12.7660 | 600  | 0.0001        | -               |
+| 13.8298 | 650  | 0.0001        | -               |
+| 14.8936 | 700  | 0.0001        | -               |
+| 15.9574 | 750  | 0.0001        | -               |
+| 17.0213 | 800  | 0.0001        | -               |
+| 18.0851 | 850  | 0.0001        | -               |
+| 19.1489 | 900  | 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_ap",
+  "architectures": [
+    "RobertaModel"
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+  "hidden_act": "gelu",
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+  "initializer_range": 0.02,
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+  "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": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+  "labels": null
+}

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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+}