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: 벤시몽 RAIN BOOTS MID - 7color DOLPHIN GREY_40 260 오리상점
+- text: 플레이볼 오리진 뮬 (PLAYBALL ORIGIN MULE) NY (Off White) 화이트_230 주식회사 에프앤에프
+- text: XDMNBTX0037 빅 사이즈 봄여름 블로퍼 고양이 액체설 블랙_265 푸른바다
+- text: 다이어트 슬리퍼 다리 부종 스트레칭 균형 실내화 핑크 33-37_33 글로벌다이렉트
+- text: 케즈 챔피온 스트랩 캔버스5 M01778F001 Black/Black/Black_230 블루빌리
+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: 0.6511206701381028
+      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>'로저비비에 로저 비비어 i 러브 비비어 슬링백 펌프스 RVW53834670PE5 여성 37 주식회사 페칭'</li><li>'크롬베즈 스티치 장식 통굽펌프스 KP55797MA 카멜／245 sellerhub'</li><li>'HOBOKEN PS1511 PH2208 (3컬러) 브라운 230 NC_백화점'</li></ul>                                                       |
+| 2.0   | <ul><li>'어그클래식울트라미니 ugg 어그부츠 여성 방한화 여자 발편한 겨울 신발 1116109 Sage Blossom_US 6(230) 울바이울'</li><li>'해외문스타 810s ET027 마르케 모디 운동화 장화 레인부츠 일본 직구 300_코요테_모디ET027 뉴저지홀세일'</li><li>'무릎 위에 앉다 장화 롱부츠 굽이 거칠다 평평한 바닥 고통 라이더 부츠 블랙_225 ZHANG YOUHUA'</li></ul> |
+| 0.0   | <ul><li>'단화 한복신발 여성 새 혼례 소프트 한복구두 전통 꽃신 자수 39_빅화이트백봉이는한사이즈크게찍으셨으면좋겠습 대복컴퍼니'</li><li>'한복구두 꽃신 양단 생활한복 키높이 단화 굽 빅사이즈 담그어 여름 터지는 구슬 화이트-3.5cm_41 대한민국 일등 상점'</li><li>'여자 키높이 신발 여성 꽃신 한복 구두 전통 계량한복 37_화이트12(지연) 유럽걸스'</li></ul>                    |
+| 4.0   | <ul><li>'남여공용 청키 클로그 바운서 샌들 (3ASDCBC33) 블랙(50BKS)_240 '</li><li>'[포멜카멜레]쥬얼장식트위드샌들 3cm FJS1F1SS024 아이보리/255 에이케이에스앤디(주) AK플라자 평택점'</li><li>'[하프클럽/] 에끌라 투웨이 주얼 샌들 33.카멜/245mm 롯데아이몰'</li></ul>                                                    |
+| 8.0   | <ul><li>'에스콰이아 여성 발편한 경량 세미 캐주얼 앵클 워커 부츠 3cm J278C 브라운_230 (주) 패션플러스'</li><li>'[제옥스](신세계강남점) 스페리카 EC7 여성 워커부츠-블�� W1B6VDJ3W11 블랙_245(38) 주식회사 에스에스지닷컴'</li><li>'(신세계강남점)금강 랜드로바 경량 컴포트 여성 워커 부츠 LANBOC4107WK1 240 신세계백화점'</li></ul>               |
+| 6.0   | <ul><li>'10mm 2중바닥 실내 슬리퍼 병원 거실 호텔 실내화 슬리퍼-타올천_고급-C_검정 주식회사 하루이'</li><li>'소프달링 남녀공용 뽀글이 스마일 털슬리퍼 여성 겨울 털실내화 VJ/왕스마일/옐로우_255 소프달링'</li><li>'소프달링 남녀공용 뽀글이 스마일 털슬리퍼 여성 겨울 털실내화 VJ/왕스마일/옐로우_245 소프달링'</li></ul>                                     |
+| 3.0   | <ul><li>'지안비토로씨 여성 마고 미드 부티 GIA36T75BLU18A1A00 EU 38.5 봉쥬르유럽'</li><li>'모다아울렛 121507 여성 7cm 깔끔 스틸레토 부티 구두 블랙k040_250 ◈217326053◈ MODA아울렛'</li><li>'미들부츠 미들힐 봄신상 워커 롱부츠 봄 가을신상 힐 블랙 245 바이포비'</li></ul>                                            |
+| 5.0   | <ul><li>'[공식판매] 버켄스탁 지제 에바 EVA 블랙 화이트 07 비트루트퍼플 키즈_220 (34) 좁은발볼 (Narrow) '</li><li>'eva 털슬리퍼 방한 방수 따듯한 털신 통굽 실내 화 기모 크로스오버 블랙M 소보로샵'</li><li>'크록스호환내피 털 탈부착 퍼 겨울 슬리퍼 안감 크림화이트(주니어)_C10-165(155~165) 인터코리아'</li></ul>                            |
+| 7.0   | <ul><li>'[밸롭] 구름 브리즈 베이지 구름 브리즈 베이지245 (주)지티에스글로벌'</li><li>'[스텝100] 무지외반증 허리디스크 평발 신발 무릎 관절 중년 여성 운동화 화이트핑크플라워_235 스텝100'</li><li>'물컹슈즈 2.0 기능성 운동화 발편한 쿠션 운동화 무지외반증신발 족저근막염 물컹 업그레이드2.0_네이비_46(280mm) 주식회사 나인투식스'</li></ul>                     |
+| 1.0   | <ul><li>'베라왕 스타일온에어 23SS 청 플랫폼 로퍼 80111682 G 667381 틸블루_230 DM ENG'</li><li>'[MUJI] 발수 발이 편한 스니커 머스터드 235mm 4550182676303 무인양품(주)'</li><li>'[반스(슈즈)]반스 어센틱 체커보드 스니커즈 (VN000W4NDI0) 4.240 롯데아이몰'</li></ul>                                        |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.6511 |
+
+## 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_ac10")
+# Run inference
+preds = model("XDMNBTX0037 빅 사이즈 봄여름 블로퍼 고양이 액체설 블랙_265 푸른바다")
+```
+
+<!--
+### 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   | 10.504 | 21  |
+
+| 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    | 0.4172        | -               |
+| 0.6329  | 50   | 0.3266        | -               |
+| 1.2658  | 100  | 0.1718        | -               |
+| 1.8987  | 150  | 0.095         | -               |
+| 2.5316  | 200  | 0.0257        | -               |
+| 3.1646  | 250  | 0.0142        | -               |
+| 3.7975  | 300  | 0.0026        | -               |
+| 4.4304  | 350  | 0.0164        | -               |
+| 5.0633  | 400  | 0.01          | -               |
+| 5.6962  | 450  | 0.0004        | -               |
+| 6.3291  | 500  | 0.0003        | -               |
+| 6.9620  | 550  | 0.0002        | -               |
+| 7.5949  | 600  | 0.0002        | -               |
+| 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 | 0.0001        | -               |
+| 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 | 0.0001        | -               |
+| 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_ac",
+  "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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+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

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+oid sha256:6fb6f2ef6f8b76f97e0e74e0768da340ac048ace7c3469ee88625df1b6ccc95d
+size 442494816

model_head.pkl

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+oid sha256:639b452ad2d4959b91a5c5f242bdaf3bf90c02dc776a65bcc79bb516fac34f0d
+size 62407

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,
+    "single_word": false
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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
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+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
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+  "max_length": 512,
+  "model_max_length": 512,
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+  "pad_token": "[PAD]",
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+  "padding_side": "right",
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+  "stride": 0,
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}