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: 엔리안 젤 클렌져/ 젤 리무버 120ml 젤 닦는거 2엔리안 젤 리무버120ml 레브숑
+- text: 모스티브 진짜시리즈 컬러젤 진짜화이트 옵션없음 주식회사 코즈랩
+- text: 독일직수입 닥터+ 스피릿 안티 F 오일 12ml 옵션없음 아담앤이브
+- text: 요거트젤네일 젤클렌저 클리너+젤리무버 스톤2알 요거트젤 젤클렌저+젤리무버+스톤2알 백억언니
+- text: '[2+1] 데싱디바 매직프레스 BEST 웨딩 네일 SET 옵션없음 제이씨코리아주식회사'
+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: 0.6072186836518046
+      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                                                                                                                                                                                                                      |
+|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 6.0   | <ul><li>'더젤 랜덤파츠2알 누드톤 시럽 펄 컬러 젤 네일 폴리쉬 매니큐어 더젤 1~99_031.베이비스킨 주식회사 디에이치엠'</li><li>'요거트젤 화양연화 세트 시럽 글리터 젤네일 옵션없음 다즐링네일'</li><li>'5월 프로모션 | 디젤 오로라퀸 프리즘 컬렉션 8종 단품 AQ57 주식회사 사라센'</li></ul>                                     |
+| 5.0   | <ul><li>'네일팁 양면테이프 스티커 24P 1세트 셀프젤네일재료 유키 네일 양면 실리콘 테이프 24ps (주)한국코스텍'</li><li>'다이아미 카누팁 리필팁 네일연장팁 [ 클리어 ]_T26 / 5호 주식회사 시그니처바스켓(SIGNATURE BASKET)'</li><li>'잇템샵 여름네일 페디큐어 네일아트 패디팁 패디큐어 인조발톱 바캉스 페디큐어125 내가원하는잇템샵'</li></ul> |
+| 1.0   | <ul><li>'(신상추가) 젤로젤로 지그재그젤 논와이프튜브젤 손쉬운아트 46종 JT43스윗캐러멜 아리아떼'</li><li>'[웨딩파우더 SET] 웨딩 화이트/웨딩 핑크 옵션없음 제이유인터내셔널'</li><li>'리얼묘해 리얼프린세스 [리얼신데렐라 파우더] 미러파우더 파우더네일 리얼자스민 리얼묘해&세리오'</li></ul>                                         |
+| 0.0   | <ul><li>'과일나라 본체청정연 네일리무버(아세톤) 2023년 리뉴얼 옵션없음 최고상사'</li><li>'루벤스 젤클렌져 1,000ml 옵션없음 별돌이네네일재료'</li><li>'루핀 젤클리너 젤클렌저 500ml 옵션없음 주식회사 코즈랩'</li></ul>                                                                             |
+| 3.0   | <ul><li>'15도 네일 손목 받침대 팔받침대 15도 네일손목받침대 부띠끄코리아'</li><li>'개업 결혼 돌 답례품 프리미엄 블랙 손톱깎이세트 판촉물 인쇄 2.고급 종이포장 스티커 티오티'</li><li>'아크릴 네일 자석 진열대 디스플레이 컬러차트판 24칸 24칸투명아크릴 가재울커머스'</li></ul>                                               |
+| 4.0   | <ul><li>'헤라 네일 키트 헤라 네일 3종 세트 매니큐어 AB당당'</li><li>'셀프 젤네일 세트 홈 키트 로나네일'</li><li>'[위드샨] 맞춤 케어 2종 세트 (3타입 중 택1) 잘 부러지고 약한 손톱(스트랭쓰너+쉴드탑) 주식회사손과발'</li></ul>                                                                       |
+| 2.0   | <ul><li>'Burts Bees 100 천연 레몬 버터 큐티클 크림 17g06온스 틴 케이스 0.6 Ounce Pack of 1_레몬 버터 믿고보는보부상'</li><li>'케라셀 패치 14매 나이트타임 손발톱영양제 손발톱 강화제 옵션없음 행운'</li><li>'보이델라 목초앰플 문제성 발톱 집중 케어 (BEST) [50%] 목초앰플 3개 (주)헤렌'</li></ul>              |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.6072   |
+
+## 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_bt1_test")
+# Run inference
+preds = model("모스티브 진짜시리즈 컬러젤 진짜화이트 옵션없음 주식회사 코즈랩")
+```
+
+<!--
+### 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   | 4   | 9.3955 | 18  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 16                    |
+| 1.0   | 19                    |
+| 2.0   | 21                    |
+| 3.0   | 32                    |
+| 4.0   | 10                    |
+| 5.0   | 16                    |
+| 6.0   | 20                    |
+
+### 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    | 0.5013        | -               |
+| 3.5714  | 50   | 0.2888        | -               |
+| 7.1429  | 100  | 0.0488        | -               |
+| 10.7143 | 150  | 0.0137        | -               |
+| 14.2857 | 200  | 0.0016        | -               |
+| 17.8571 | 250  | 0.0002        | -               |
+| 21.4286 | 300  | 0.0001        | -               |
+| 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,
+  "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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+{
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+    "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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+  "normalize_embeddings": false
+}

model.safetensors

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model_head.pkl

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modules.json

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+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
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+    "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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tokenizer_config.json

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+}