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 +1,243 @@
+---
+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: 동아제약 가그린 오리지널 가글 750ml (1개) 가그린 오리지널 820ml L스토어
+- text: 스켈링 입냄새 스케일러 치석제거기 구강청결기 치아 별이 빛나는 하늘 보라색 사치(sachi)
+- text: 텅브러쉬 4개세트 혀클리너 입냄새제거 혀백태제거 혀칫솔 i MinSellAmount 펀키보이
+- text: '[갤러리아] 폴리덴트 의치 부착재 민트향 70g x5개  한화갤러리아(주)'
+- text: 애터미 치약 프로폴리스 200g 입냄새 제거 미백 콜마  플렉스세븐
+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: 0.9477272727272728
+      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:** 10 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                                                                                                                                                                                                                  |
+|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 9.0   | <ul><li>'롤리팝 에디슨 항균 혀클리너 4종 퍼플 파랑새랑'</li><li>'텅브러쉬 혀클리너 입냄새제거 백태제거 혀칫솔 MinSellAmount 펀키보이'</li><li>'[생활도감] 혀클리너 세트 그린2개+네이비2개 주식회사 생활도감'</li></ul>                                                                        |
+| 2.0   | <ul><li>'셀프 가정용 스테인레스 스케일링 치석제거기 청소 도구 304 핑크 6종 세트 주식회사 클라우드'</li><li>'도구 치경 제거 편도석 제거기 입똥 편도결석 목똥 셀프 발광 귀걸이x수납함 로얄산티아고'</li><li>'소형 구취 측정기 테스트기 휴대용 냄새 악취 호흡 구강 입냄새측정기 자가진단 자가 가스 표준모델 _ 검정 행복초지'</li></ul>             |
+| 0.0   | <ul><li>'존슨앤존슨 구강청결 리스테린 쿨민트 250ml 후레쉬버스트 250ml - 1개 디아크코리아'</li><li>'일회용 여행용 가그린 라임10g 1개 휴대용 오리지널 가글스틱 오리지널 1개 예그린스페이스'</li><li>'가그린 제로 1200ML  쓱1day배송'</li></ul>                                                      |
+| 4.0   | <ul><li>'투스노트 화이트닝겔 하루 2번 30분 투자로 누런이를 하얗게 투스노트 화이트닝겔 2주분 주식회사 네이처폴'</li><li>'루치펠로 미스틱포레스트 치약 180g 5개  원라이브팩토리'</li><li>'대형 치아모형 치아 모델 구조 인체 구강 치과 C. 구강 2배 확대(하아 제거 가능) 마켓 스페이스토끼'</li></ul>                             |
+| 8.0   | <ul><li>'미소덴탈 교정장치보관함 교정기케이스 교정기통 교정기보관함-옐로우 (주)톡톡그린'</li><li>'성심 덴트크린 틀니세정제 36개입 2개 교정기 세척  희망메디'</li><li>'폴리덴트 맥스 씰 의치 부착재(의치 접착���) 70gx5개+샘플 1개  더마켓'</li></ul>                                                         |
+| 6.0   | <ul><li>'백선생 왕타칫솔 베이직 스톤 10P  왕타'</li><li>'켄트칫솔 클래식 6개입 부드러운 칫솔 미세모 치아관리 어금니 치과칫솔 켄트 클래식 6개_켄트 탄 초극세모 1개(랜덤)_치간칫솔 8개입 1세트(레드 0.7mm) (주)지로인터내셔널'</li><li>'쿤달 딥 클린 탄력 항균 이중미세모 칫솔 부드러운모, 16입, 1개 구분 : 부드러운모 슈팅배송'</li></ul> |
+| 3.0   | <ul><li>'오랄비 P&G 왁스치실 민트향 50m 01.왁스 치실 민트향 50m TH상사'</li><li>'오랄비 C자형 일회용 치실 30개입 1팩 NEW)치실C자 30개입[O121] 한국피앤지판매유한회사'</li><li>'오랄비 왁스치실 (50m 1개) 민트 디엔지유통'</li></ul>                                                      |
+| 5.0   | <ul><li>'LG생활건강 죽염 명약원 골든프로폴리스 치약 플러스 120g MinSellAmount 오늘도연구소'</li><li>'엘지생활건강 죽염 잇몸고 치약 120g 1개  유니스'</li><li>'센소다인 오리지널 플러스 치약 100g 1개 dm  다임커머스'</li></ul>                                                           |
+| 7.0   | <ul><li>'[유한양행]닥터버들 치약+칫솔 여행용세트 6개  신세계몰'</li><li>'[유한양행]닥터버들 휴대용 칫솔치약세트 1개  신세계몰'</li><li>'투톤 휴대용 칫솔 치약 케이스 캡슐형 답례품 투톤용 칫솔통 보관함 홀더 칫솔캡 캡슐칫 화이트블루 쏭리빙'</li></ul>                                                          |
+| 1.0   | <ul><li>'일제 형상기억 마우스피스  아리더샾'</li><li>'혀용 코골이 방지 용품 대책용 마우스피스 8 개 세트 이와이리테일(EY리테일)'</li><li>'이갈이방지 치아 앞니 보호 유지 셀프 마우스피스 교정 2단계 코스모스'</li></ul>                                                                            |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9477 |
+
+## 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_lh4")
+# Run inference
+preds = model("애터미 치약 프로폴리스 200g 입냄새 제거 미백 콜마  플렉스세븐")
+```
+
+<!--
+### 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   | 10.026 | 23  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 50                    |
+| 3.0   | 50                    |
+| 4.0   | 50                    |
+| 5.0   | 50                    |
+| 6.0   | 50                    |
+| 7.0   | 50                    |
+| 8.0   | 50                    |
+| 9.0   | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0127  | 1    | 0.4686        | -               |
+| 0.6329  | 50   | 0.2751        | -               |
+| 1.2658  | 100  | 0.1179        | -               |
+| 1.8987  | 150  | 0.0739        | -               |
+| 2.5316  | 200  | 0.0687        | -               |
+| 3.1646  | 250  | 0.0466        | -               |
+| 3.7975  | 300  | 0.0591        | -               |
+| 4.4304  | 350  | 0.0232        | -               |
+| 5.0633  | 400  | 0.0125        | -               |
+| 5.6962  | 450  | 0.0134        | -               |
+| 6.3291  | 500  | 0.0152        | -               |
+| 6.9620  | 550  | 0.0175        | -               |
+| 7.5949  | 600  | 0.0118        | -               |
+| 8.2278  | 650  | 0.007         | -               |
+| 8.8608  | 700  | 0.0003        | -               |
+| 9.4937  | 750  | 0.0002        | -               |
+| 10.1266 | 800  | 0.0001        | -               |
+| 10.7595 | 850  | 0.0001        | -               |
+| 11.3924 | 900  | 0.0001        | -               |
+| 12.0253 | 950  | 0.0001        | -               |
+| 12.6582 | 1000 | 0.0001        | -               |
+| 13.2911 | 1050 | 0.0001        | -               |
+| 13.9241 | 1100 | 0.0001        | -               |
+| 14.5570 | 1150 | 0.0001        | -               |
+| 15.1899 | 1200 | 0.0001        | -               |
+| 15.8228 | 1250 | 0.0001        | -               |
+| 16.4557 | 1300 | 0.0001        | -               |
+| 17.0886 | 1350 | 0.0001        | -               |
+| 17.7215 | 1400 | 0.0001        | -               |
+| 18.3544 | 1450 | 0.0001        | -               |
+| 18.9873 | 1500 | 0.0           | -               |
+| 19.6203 | 1550 | 0.0           | -               |
+
+### 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

@@ -0,0 +1,29 @@
+{
+  "_name_or_path": "mini1013/master_item_lh",
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "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": "4.46.1",
+  "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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+{
+  "normalize_embeddings": false,
+  "labels": null
+}

model.safetensors

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+size 442494816

model_head.pkl

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modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "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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+{
+  "bos_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
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+    "single_word": false
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+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+  "added_tokens_decoder": {
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+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
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+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
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+  "do_lower_case": false,
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+  "model_max_length": 512,
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}