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 +1,244 @@
+---
+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: 이글루캠 S3플러스 2K 300만화소 가정용 CCTV 홈 카메라 홈캠  (주) 트루엔
+- text: 멕시코 조트비누 400g 만능세제 세탁 세제 빨래 기름때 얼룩제거 욕실청소 라코로나 조트비누 400g (블루) 리아앤리브
+- text: 피플연구소 양면방수 매트 돗자리 145x150cm 로지브라운 피크닉 감성 화이트_M 스트림프러덕
+- text: 다우니 울트라 에이프릴 프레시 5.03L [생활] 섬유유연제_피죤 핑크로즈 3.1L x 4개 옐로우로켓
+- text: 창문 자동 롤방충망 상하식 미세 대형 셀프교체 사면 가로300x세로250mm 사면_가로1600mm(1501~1600)_세로600mm(501~600)
+    NK테크
+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: 0.7296620438939007
+      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>'선반형 스텐 점보롤 디스펜서 폰 거치 케이스 유광실버 04_CNDH-03 스텐점보 유광 골드선반 (주)엘에스트레이드'</li><li>'하이브리드 쓰리킹 미트페이퍼 해동지 2롤 선택04.크록스 위생멸균 흡수지 2롤 찐텐마켓'</li><li>'크리넥스 클린케어 아쿠아 메가롤 3겹 50m 30롤 클린소프트 3겹 데코 30m 30롤 메리앤'</li></ul>                                       |
+| 2.0   | <ul><li>'애경 로얄 프로폴리스 에디션 선물세트 추석 선물세트 명절선물세트 샴푸 린스  강성수'</li><li>'로얄 프로폴리스 셀렉션 29호 X 1개 고기능 에디션으로 행복 선물 MinSellAmount 현둘마마'</li><li>'대림바스 카카오 라이언 선물세트 GIFT BOX (샤워기+샤워줄+필터4P+염소제거볼1P) 카카오 선물세트 GIFT BOX [라이언] 바스템'</li></ul>                        |
+| 0.0   | <ul><li>'초소형 카메라 CCTV 무선 미니 감시 초소형 카메라 + 128GB SD카드_(리뷰약속)SD카드 32GB+방수케이스+거치대2종 일레닉'</li><li>'지르콘 멀티 탐지기 HD70 멀티탐색기 ZIRCON 멀티탐지  프랑스달'</li><li>'메모리선택 티피링크 Tapo TC70 200만화소 360도회전 실내무선카메라 홈CCTV 야간흑백전환 선택4 Tapo TC70+메모리카드128G 삼성디앤씨주식회사'</li></ul> |
+| 4.0   | <ul><li>'바운스 건조기 드라이시트 아웃도어후레쉬 160매 1개  에너저틱'</li><li>'피죤 핑크로즈 3.1L 피죤 비앙카 3100ml 1입 주식회사 드림쇼핑'</li><li>'다우니 엑스퍼트 실내건조 섬유유연제 1L 생화향기 코튼퓨어 용기 1L (주)모던컴퍼니'</li></ul>                                                                                  |
+| 8.0   | <ul><li>'엔젤가드 특허 90도 회전 전기모기채 충전식전자파리채 건전지대 01. 특허받은 led회전모기채(충전식 대) 핑크 WOOD파크'</li><li>'ODF169432해피홈 에어넷 걸이형  제이엘 코리아(JL KOREA)'</li><li>'초강력해충킬러전기모기채(특대)  비트테크노'</li></ul>                                                                        |
+| 6.0   | <ul><li>'헨켈 퍼실 파워젤 라벤더 드럼용 리필 1.8L 퍼실 파워젤 드럼용 1.8L(일반/드럼 겸용) 누리플러스'</li><li>'다우니 프리미엄 엑스퍼트 실내건조 세탁세제 액체형 1.9L 08_다우니 코튼 퓨어러브 1L (주)넥스트월드코퍼레이션'</li><li>'애경산업 스파크 찬물에 잘녹는 세탁세제 리필 9.5kg 1개  쇼킹(SHOW KING)'</li></ul>                                  |
+| 3.0   | <ul><li>'쇼핑카트 바퀴달린장바구니 시장바구니캐리어 접이식 손수레 핸드 카트 마트 베이지체크패턴 (타입07) 소형 패턴 8종_체크 곤색 에이오더스(A Orders)'</li><li>'스테인레스 가정용 소형 원형 스퀘어 스탠드 방지 재 01.락 구형 블랙 라지 에이미어블'</li><li>'1초완성 원터치모기장 텐트 침대 사각 아기 대형 창문 2_베이직 블루 2~3인용(200X150) 다샵몰'</li></ul>             |
+| 5.0   | <ul><li>'금비 겉기저귀 프리미엄 와이드매직 실속형 대형 10p+10p(총 2팩) 팬티기저귀 대형 10p+10p 나루(NARU)리테일'</li><li>'디펜드 스타일 언더웨어 슬림 라이트핏 중형 여성용 10개입x8팩/요실금팬티 성인기저귀  송광물류'</li><li>'유한킴벌리 디펜드 안심플러스 중형 9매 -1개  주식회사 민영'</li></ul>                                                |
+| 7.0   | <ul><li>'말표 신발 탈취제 100ml 발냄새 신발냄새 제거 MinSellAmount 대코아'</li><li>'슈즈쿨 빨강색 신발건조탈취제 냄새 습기제거 MinSellAmount SMH만물상회'</li><li>'페브리즈 포맨 쿨아쿠아향 리필 320ml 포맨 쿨아쿠아향 리필 320ml 지기샵'</li></ul>                                                                     |
+| 1.0   | <ul><li>'좋은느낌 입는 오버나이트 중형 8매 x 1팩  주식회사 다올연구소'</li><li>'닉스컵 내몸을 생각하는 안전한 실리콘 생리컵 소형  luckytiger3'</li><li>'화이트 수퍼흡수 중형 (30+6)개입  (주) 삼성 에이치엔씨'</li></ul>                                                                                             |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.7297 |
+
+## 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_lh12")
+# Run inference
+preds = model("이글루캠 S3플러스 2K 300만화소 가정용 CCTV 홈 카메라 홈캠  (주) 트루엔")
+```
+
+<!--
+### 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   | 9.964  | 24  |
+
+| 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    | 0.3941        | -               |
+| 0.6329  | 50   | 0.3041        | -               |
+| 1.2658  | 100  | 0.1323        | -               |
+| 1.8987  | 150  | 0.0705        | -               |
+| 2.5316  | 200  | 0.0185        | -               |
+| 3.1646  | 250  | 0.021         | -               |
+| 3.7975  | 300  | 0.0292        | -               |
+| 4.4304  | 350  | 0.0158        | -               |
+| 5.0633  | 400  | 0.0176        | -               |
+| 5.6962  | 450  | 0.0001        | -               |
+| 6.3291  | 500  | 0.0079        | -               |
+| 6.9620  | 550  | 0.0004        | -               |
+| 7.5949  | 600  | 0.0001        | -               |
+| 8.2278  | 650  | 0.0001        | -               |
+| 8.8608  | 700  | 0.0001        | -               |
+| 9.4937  | 750  | 0.0001        | -               |
+| 10.1266 | 800  | 0.0001        | -               |
+| 10.7595 | 850  | 0.0001        | -               |
+| 11.3924 | 900  | 0.0001        | -               |
+| 12.0253 | 950  | 0.0001        | -               |
+| 12.6582 | 1000 | 0.0           | -               |
+| 13.2911 | 1050 | 0.0           | -               |
+| 13.9241 | 1100 | 0.0001        | -               |
+| 14.5570 | 1150 | 0.0           | -               |
+| 15.1899 | 1200 | 0.0           | -               |
+| 15.8228 | 1250 | 0.0           | -               |
+| 16.4557 | 1300 | 0.0001        | -               |
+| 17.0886 | 1350 | 0.0           | -               |
+| 17.7215 | 1400 | 0.0           | -               |
+| 18.3544 | 1450 | 0.0           | -               |
+| 18.9873 | 1500 | 0.0           | -               |
+| 19.6203 | 1550 | 0.0001        | -               |
+
+### 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

@@ -0,0 +1,10 @@
+{
+  "__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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+    "lstrip": false,
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+    "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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+  "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]"
+}