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 +1,219 @@
+---
+base_model: klue/roberta-base
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 맥퀸뉴욕 페이크 업 3색 쉐딩+쉐딩브러쉬 컨투어링 섀딩(신규색상 출시!) 쉐딩세트(뉴트럴브라운) 주식회사 웃는생각컴퍼니
+- text: LOMA 너리싱 컨디셔너 1L 옵션없음 엠브로(M.bro)
+- text: KYTRSTX face Towels 옵션없음 부자오타쿠
+- text: 바이레도 블랑쉬 헤어미스트 퍼퓸 75ml 75ml 피제이인터내셔날(주)
+- text: 쏘내추럴 시그니처 페이스 오일 30ml 1개 옵션없음 건강드림
+inference: true
+model-index:
+- name: SetFit with klue/roberta-base
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.494948348280168
+      name: Accuracy
+---
+
+# SetFit with klue/roberta-base
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) 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:** [klue/roberta-base](https://huggingface.co/klue/roberta-base)
+- **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:** 13 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                                                                                                                                                                                                                                                                |
+|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1.0   | <ul><li>'셀프 젤네일 세트 홈 키트 로나네일'</li><li>'네일팁패디팁겸용 양면테이프 24p 1장 말랑팁전용 네일파츠대장'</li><li>'뷰젤 오빠탑젤 5개 세트 논와이프 오빠탑 옵션없음 더 뷰티 (THE BEAUTY)'</li></ul>                                                                                                                            |
+| 7.0   | <ul><li>'엘로엘 2024 시즌8 팡팡 빅선쿠션 S8 스마일썬쿠션 본품 25g 옵션없음 더블아이'</li><li>'바나나보트 에프터썬 알로에젤 수딩젤 473ml 3개입 바나나보트 알로에젤 473ml/3개입 스테디세일러'</li><li>'PGA TOUR 선몬랩 피토 워터 선스프레이 80ML A07 옵션없음 장희스토어'</li></ul>                                                                          |
+| 12.0  | <ul><li>'톤28 머리감을거리 S21 검은콩 참숯 약산성 고체샴푸 100g 1개 100g × 옵션없음 지엘디'</li><li>'[클렌징대전(클렌징밤 )] 로픈 바오밥 세라마이드LPP 프리미엄 헤어트리트먼트 베이비파우더향 1000g 옵션없음 (주)우신뷰티'</li><li>'닥터그라프트 스칼프 탈모 토닉 100ml 탈모 두피 케어 옵션없음 인터넷시장'</li></ul>                                                        |
+| 2.0   | <ul><li>'한율 달빛유자 수면팩 100ml (튜브형) 옵션없음 고마이플로'</li><li>'골프 피부진정 패치 5매 아이패치 하이드로겔 등산 옵션없음 아이템코리아주식회사(Item KOREA Inc.)'</li><li>'[1+1] 아나프노 스포츠 마사지 크림 온열찜질 근육완화 100ml 옵션없음 윈드샵(WIND SHOP)'</li></ul>                                                                       |
+| 8.0   | <ul><li>'정품 달바 화이트 트러플 로얄 인텐시브 세럼 160ml 1개 옵션없음 (주)준광아이티'</li><li>'스킨푸드 미나리 패드 토너 닥토 닦토 60매 옵션없음 찬이네마켓'</li><li>'슈슈블리 맥 MAC 프렙 프라임 픽스 픽서 플러스 미스트 100ml 1021720 일반형 메가랜드'</li></ul>                                                                                      |
+| 6.0   | <ul><li>'(유통기한 임박)투쿨포스쿨 아트 클래스 매지컬 픽싱 마스카라 7g 2호 다크브라운(24.04까지) 리앤햇'</li><li>'누즈 무스 케어 치크 16ml 1021961 02핑크타퍼_동의합니다. 굿데이'</li><li>'하트퍼센트 도트 온 무드 립펜슬 20 Colors, 03 오트베이지, 1개 옵션없음 어바웃팩토리'</li></ul>                                                                     |
+| 0.0   | <ul><li>'사타구니 가려움 습진 연고 약 백선 완선 연고 낭습증 20ml 2개 옵션없음 비솔루션'</li><li>'니베아 맨 센서티브 쉐이빙 젤 200ml 옵션없음 네고장터'</li><li>'AHC 온리포맨 스킨케어 2종세트(토너+로션) AHC 공식스토어'</li></ul>                                                                                                            |
+| 4.0   | <ul><li>'미샤 골드토핑 모이스트 레이어링 스타터 30ml 옵션없음 위너플렉스'</li><li>'레브론 컬러 스테이 프레스토 파우더 N820 1개(x1) 옵션없음 Thanks Auction'</li><li>'[국내매장판] 베네피트 프라이머 모공프라이머 더포어페셔널 모공 커버 지우개 7.5ml 프라이머 미니 + 슈퍼세터 미니 + 파우치 하이블랭크'</li></ul>                                                         |
+| 9.0   | <ul><li>'아임프롬 피그 스크럽 마스크 120g 5개 옵션없음 건강드림'</li><li>'바이오더마 센시비오 H2O 500ml 옵션없음 브이브이에스'</li><li>'바이오더마 센시비오 H2O 500ml (펌프형) 옵션없음 나오스코리아 유한회사'</li></ul>                                                                                                                  |
+| 10.0  | <ul><li>'소소모소 디퓨저리필 500ml_프레쉬라벤더 _salestrNo:2449_지점명:emartNE.O.002 (주)리빙탑스/해당사항 없음'</li><li>'[로에베](신세계 강남점)아구아 마이애미 오 드 뚜왈렛 100ML 옵션없음 주식회사 에스에스지닷컴'</li><li>'인센스 홀더 미니화병 황동 향 피우기 나그참파 꽂이 (WBC1E2F) 본상품선택 기타/해당사항 없음'</li></ul>                                        |
+| 11.0  | <ul><li>'미쟝센 올뉴 쉽고빠른 거품 염색약 5N 갈색 1개 옵션없음 트레이딩제이'</li><li>'다슈 울트라 홀딩 스칼프 탈모증상완화 헤어스프레이, 50ml, 1개 50ml × 1개 오케이라이딩'</li><li>'한국미용 피앙코 울트라 하드젤 550ml 옵션없음 (주)수유종합유통'</li></ul>                                                                                            |
+| 5.0   | <ul><li>'실리콘 플라스틱뷰러 속눈썹 파마 패드 리프팅 로드 쉴드 3D 컬러 액세서리 어플리케이터 Mgreen 1 pair 카이산몰208'</li><li>'쪽집게 전문 스테인리스 스틸 고품질 보석 족집게, DIY 다이아몬드 주얼리 제작 도구 02 elbow 마이나인쓰'</li><li>'괄사마사지기 원목 코 빗 얼굴 두피 경락 괄사안마기 옵션없음 운호'</li></ul>                                                      |
+| 3.0   | <ul><li>'상떼 아줄렌 수더 겔 500ml 진정 수분 마사지겔 💚아카토너 500ml+버블공병+패드200매_아카마스크2장+상떼체험분최다💗 달링태그(Darling_Tag)'</li><li>'일본 규슈 벳푸 명반온천 가마도지옥 천연입욕제 대자연의편안함 500ml 250ml 유노하나 유황 입욕제 대자연의편안함500ml 명품산업'</li><li>'닥터블리 멍게발팩 발 각질제거 필링 풋마스크 뒤꿈치 갈라짐 제거 1박스(3개입) 주식회사제이에이치코퍼레이션'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.4949   |
+
+## 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_item_bt_test")
+# Run inference
+preds = model("KYTRSTX face Towels 옵션없음 부자오타쿠")
+```
+
+<!--
+### 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   | 9.3971 | 26  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 242                   |
+| 1.0   | 134                   |
+| 2.0   | 161                   |
+| 3.0   | 324                   |
+| 4.0   | 141                   |
+| 5.0   | 130                   |
+| 6.0   | 267                   |
+| 7.0   | 133                   |
+| 8.0   | 257                   |
+| 9.0   | 251                   |
+| 10.0  | 63                    |
+| 11.0  | 117                   |
+| 12.0  | 152                   |
+
+### Training Hyperparameters
+- batch_size: (512, 512)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 10
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0213 | 1    | 0.4192        | -               |
+
+### 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

@@ -0,0 +1,29 @@
+{
+  "_name_or_path": "mini1013/master_domain",
+  "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": "4.44.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "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,
+  "labels": null
+}

model.safetensors

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+oid sha256:820eb225807accee327570b317c8b03235ebd89bcf549189365dfc1ea20cc879
+size 442494816

model_head.pkl

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+size 80895

modules.json

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+    "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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+{
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+    "content": "[CLS]",
+    "lstrip": false,
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+    "content": "[PAD]",
+    "lstrip": false,
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+    "single_word": false
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+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
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+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
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+  "model_max_length": 512,
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+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}