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: 진저 스캘프 케어 컨디셔너 400ML 옵션없음 (주)씨제이이엔엠
+- text: 일본 츠바키 프리미엄 리페어 마스크 리필 150g(2개 세트), 상한머리 탄머리 복구 클리닉 옵션없음 포이뷰티
+- text: 카디뷰 아사이오일 220ML 옵션없음 다이뜨라 마켓
+- text: 르네휘테르 포티샤 두피 세럼 100ml 1개 동의 스티커에일리언
+- text: 닥터방기원 랩 탈모 트리트먼트 1000ml 옵션없음 아레나스
+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: 0.598939929328622
+      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                                                                                                                                                                                                                           |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 6.0   | <ul><li>'[가는모발용 앰플] 케라스타즈 아미넥실 포스 R 6ml 42개입 옵션없음 (주)엠에이치프로페셔날'</li><li>'아모스 컬링에센스 2X 150ml 대량주문 환영 옵션없음 최저가사이트'</li><li>'Milbon Creative Style 웨이브 디파이닝 크림 1 119g(4.2온스) 옵션없음 세렌몰1'</li></ul>                                     |
+| 7.0   | <ul><li>'[2+1] 먼스앤데이즈 르 부케 헤어케어 (샴푸 500ml x 2개 +컨디셔너 500ml x 1개) 먼스앤데이즈공식스토어'</li><li>'밀크바오밥 세라 샴푸+트리트먼트 화이트머스크 1200ml 밀크바오밥'</li><li>'미쟝센 퍼펙트 매직 스트레이트 샴푸&트리트먼트&세럼 3종 세트+트리트먼트 30ml 아모레퍼시픽'</li></ul>                             |
+| 0.0   | <ul><li>'수산농원 국내산 어성초 두피케어 발모팩 스프레이 200ml 무농약건어성초200g 올바른마켓'</li><li>'바이브랩두피앰플 헤어 스칼프 앤 브로우 앰플 15ml 1개 옵션없음 더모아스포츠'</li><li>'AROMATICA 티트리 퓨리파잉 토닉 958g33온스 파라벤 실리콘 황산염 프리 비건 1) 02 티트리 토닉 수예쁠연'</li></ul>                         |
+| 4.0   | <ul><li>'[클렌징대전(클렌징밤 )] 로픈 바오밥 세라마이드LPP 프리미엄 헤어트리트먼트 베이비파우더향 1000g 옵션없음 (주)우신뷰티'</li><li>'케라스타즈 뉴트리티브 마스퀸텐스 리슈 200ml 3474637155001 옵션없음 퓨쳐 디엠 (FUTURE DIEM)'</li><li>'Vitamins 비타민 케라틴 퍼플 블루 헤어 마스크 250ml x 2개 옵션없음 명원박'</li></ul> |
+| 2.0   | <ul><li>'모다모다 제로그레이 블랙샴푸 300g 3개 옵션없음 주식회사 아이프리홀딩스'</li><li>'소리쟁이 가려움 각질 비듬 천연 두피케어 소리쟁이샴푸500ml(지성/중성용) 주식회사 미래정보산업'</li><li>'톤28 머리감을거리 S21 검은콩 참숯 약산성 고체샴푸 100g 1개 100g × 옵션없음 지엘디'</li></ul>                                    |
+| 5.0   | <ul><li>'바이레도 집시워터 헤어퍼퓸 75 ml 옵션없음 블루밍컴퍼니'</li><li>'아모스 녹차실감 볼류마이징 미스트 140ml 옵션없음 주식회사 케이스코'</li><li>'웰코스 뮤겐스 내추럴 밸런스 투페이스 100ml 옵션없음 청구미용재료'</li></ul>                                                                            |
+| 1.0   | <ul><li>'아모스 프로베이직 칼라 앤 펌 컨디셔너 1000g 옵션없음 제이제이코스메틱'</li><li>'아베다 인바티 어드밴스드 씨크닝 컨디셔너 1000ml 1021530 옵션없음 메가랜드'</li><li>'교보문고 골피아 도브 컨디셔너 데일리샤인 660ml (3개) 린스 옵션없음 디원'</li></ul>                                                     |
+| 3.0   | <ul><li>'모비88 아데노신 특허등록 탈모토닉 볼륨업 비듬 제거 옵션없음 달이커머스'</li><li>'애터미 생모단수 200ml 옵션없음 에브리테일'</li><li>'닥터그라프트 스칼프 탈모 토닉 100ml 탈모 두피 케어 옵션없음 인터넷시장'</li></ul>                                                                              |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.5989   |
+
+## 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_bt12_test")
+# Run inference
+preds = model("카디뷰 아사이오일 220ML 옵션없음 다이뜨라 마켓")
+```
+
+<!--
+### 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   | 9.25   | 21  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 12                    |
+| 1.0   | 23                    |
+| 2.0   | 19                    |
+| 3.0   | 14                    |
+| 4.0   | 18                    |
+| 5.0   | 20                    |
+| 6.0   | 28                    |
+| 7.0   | 18                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0667  | 1    | 0.485         | -               |
+| 3.3333  | 50   | 0.2919        | -               |
+| 6.6667  | 100  | 0.051         | -               |
+| 10.0    | 150  | 0.0096        | -               |
+| 13.3333 | 200  | 0.0075        | -               |
+| 16.6667 | 250  | 0.0021        | -               |
+| 20.0    | 300  | 0.0001        | -               |
+| 23.3333 | 350  | 0.0001        | -               |
+| 26.6667 | 400  | 0.0001        | -               |
+| 30.0    | 450  | 0.0001        | -               |
+| 33.3333 | 500  | 0.0001        | -               |
+| 36.6667 | 550  | 0.0001        | -               |
+| 40.0    | 600  | 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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+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "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",
+    "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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+    "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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