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: 더샘 커버 퍼펙션 팁 컨실러 6.5g (SPF28) 2.25 샌드 문스타
+- text: 에스티로더 더블웨어 플로리스 하이드레이팅 프라이머 30ml(SPF45) (백화점 정품) 옵션없음 안느의집
+- text: '[빌리프] [24MS]시카 밤 쿠션 핑크 베이지 기본 주식회사 인터파크커머스'
+- text: 미즈온 비건 콜라겐 쿠션 15g(SPF38) 본품 + 리필 21호 체인커머스
+- text: 미샤 컬러 픽스 아이 프라이머 7.5g 옵션없음 위너플렉스
+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: 0.7047413793103449
+      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                                                                                                                                                                                             |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0.0   | <ul><li>'빈토르테 미네랄 CC크림 자외선차단 SPF50+ 30g 옵션없음 토스토'</li><li>'콜라겐 비비크림 50g 23호 옵션없음 심완태'</li><li>'아누아 환해지는 매트벗 글로우 커버 베이지 50ml SPF50 PA++++ 1개 아누아 환해지는 매트벗 글로우 커버 베이지 1 호랑이커머스'</li></ul>              |
+| 3.0   | <ul><li>'스킨푸드 연어 다크서클 컨실러 크림 10g 살몬블루밍 옵션없음 비엠유통'</li><li>'나스 소프트 매트 컴플리트 컨실러 6.2g 커스터드 블루밍컴퍼니'</li><li>'루나 프로 퍼펙팅 스틱 컨실러 6g 2호 내추럴베이지 x 1개 유럽피아'</li></ul>                                          |
+| 1.0   | <ul><li>'[투쿨포스쿨] 아트클래스 블랑 드 베이스 30ml 1호 클리어 블루 주식회사 인터파크커머스'</li><li>'입큰 톤웨어 틴티드 베이스 40ml(SPF35) 소프트레몬 쑤기쓰마켓'</li><li>'미샤 모이스트 레이어링 스타터 (골드 토핑) 30ml 옵션없음 비비드랩'</li></ul>                            |
+| 5.0   | <ul><li>'엘로엘 블랑 커버 크림 스틱 12g x 3개 콜라겐 퍼플 옵션없음 스마링'</li><li>'입생로랑 엉크르 드 뽀 쿠션 14g(SPF24) B10호 본품 주식회사 친칭화무역'</li><li>'클리오 킬 커버 스킨 픽서 쿠션 교체품 리필-21C 란제리 투이제이'</li></ul>                                 |
+| 4.0   | <ul><li>'엘지 더후 공진향 미 투웨이 팩트 1호 SPF30 리필 옵션없음 이프워너'</li><li>'수지코리아 앙쥬 투웨이 케익 트루 베이지 23호 13g 5WC1355A10 옵션없음 원더쇼핑'</li><li>'코티에어스펀 루스 파우더 no.11 35g 옵션없음 초이베스트'</li></ul>                              |
+| 6.0   | <ul><li>'[국내매장판] 베네피트 프라이머 모공프라이머 더포어페셔널 모공 커버 지우개 7.5ml 프라이머 미니 + 슈퍼세터 미니 + 파우치 하이블랭크'</li><li>'[VDL] 엑스퍼트 컬러 프라이머 포 아이즈 세레니티 주식회사 인터파크커머스'</li><li>'바닐라코 프라임 프라이머 하이드레이팅 30ml 옵션없음 비엠유통'</li></ul> |
+| 2.0   | <ul><li>'[에뛰드] 님프 광채 볼류머+컨실러 2종 세트 에뛰드'</li><li>'후 공진향 미 럭셔리 비비 스페셜 세트 267578 옵션없음 펀펀마켓'</li><li>'V&A Beauty 쿠션+앰플+크림 SET 브이앤에이'</li></ul>                                                           |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.7047   |
+
+## 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_bt4_test")
+# Run inference
+preds = model("미샤 컬러 픽스 아이 프라이머 7.5g 옵션없음 위너플렉스")
+```
+
+<!--
+### 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   | 5   | 9.7872 | 19  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 19                    |
+| 1.0   | 21                    |
+| 2.0   | 10                    |
+| 3.0   | 19                    |
+| 4.0   | 28                    |
+| 5.0   | 23                    |
+| 6.0   | 21                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0714  | 1    | 0.4767        | -               |
+| 3.5714  | 50   | 0.2485        | -               |
+| 7.1429  | 100  | 0.0439        | -               |
+| 10.7143 | 150  | 0.0171        | -               |
+| 14.2857 | 200  | 0.0128        | -               |
+| 17.8571 | 250  | 0.0034        | -               |
+| 21.4286 | 300  | 0.0002        | -               |
+| 25.0    | 350  | 0.0001        | -               |
+| 28.5714 | 400  | 0.0001        | -               |
+| 32.1429 | 450  | 0.0001        | -               |
+| 35.7143 | 500  | 0.0001        | -               |
+| 39.2857 | 550  | 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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+  "bos_token_id": 0,
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+  "hidden_act": "gelu",
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+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "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",
+    "pytorch": "2.2.0a0+81ea7a4"
+  },
+  "prompts": {},
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+  "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.Transformer"
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+    "name": "1",
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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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+}