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 +1,258 @@
+---
+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: MLB [MLB] 루키 언스트럭쳐 볼캡 24종 택1 203993 선택 20) 3ACP7701N-07ORL_F 위드홀리투
+- text: 남여공용 기본군모 4컬러 EVE 카키 에브리씽굿
+- text: 골덴와이어버킷햇(T)7252 브라운 모티브코리아
+- text: 패션울벙거지97 베이지 디플코리아 (Digital Plus Korea)
+- text: '[닥스](강남점)DBHE4EL01W2 브라운 체크 면 헌팅캡  신세계백화점'
+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: 0.8489339496048904
+      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:** 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                                                                                                                                                                                                                                                  |
+|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 10.0  | <ul><li>'밀로 [Exclusive] Holiday Signature Ball Cap (20Colors) MINT GRAY 포챌린지'</li><li>'(골라) 남녀공용 (GL)CONTRAST STITCHED CAP (3 COLOR) WW9G3SAAC101 연핑크_FRE '</li><li>'밀로 [Exclusive] Holiday Signature Ball Cap (20Colors) STONE BLACK 포챌린지'</li></ul>     |
+| 4.0   | <ul><li>'꽈배기 비니 모자 두꺼운 골무 털 뜨개 여성 겨울 캡 알파카 남자 커플 니트 주황색_S(아이 32-52 cm) 앤디일레븐'</li><li>'패션모자 방한 남자 니트 후드 겨울 장갑 가을 워머 도톰한 3종세트 기모 블랙 마이클로드'</li><li>'털모자 따뜻한 낚시 모자 아빠 중년남성 노인 겨울 옵션06 에스앤지샵'</li></ul>                                                      |
+| 7.0   | <ul><li>'[하프클럽/구김스]구김스 모자(스포츠/등산/여행/방수) BEST 7종 균일가 763_블랙_D type 하프클럽'</li><li>'캉골 아웃도어 액티비티 버켓 4480 에크루 M AK플라자1관'</li><li>'[벤시몽](신세계센텀점)[23FW] WINTER BUCKET HAT - 2color NAVY_FREE 주식회사 에스에스지닷컴'</li></ul>                                            |
+| 3.0   | <ul><li>'고탄성 부드러운 메쉬 원단 운동야외활동 스카프 두건 연그레이 드림픽쳐스'</li><li>'[로스코]반다나 스카프 헤어밴드 페이즐리 손수건 OLIVE DRAB_4051/Freesize 패션플러스'</li><li>'페이즐리 반다나 헤어 머리두건 비 손수건 스카프 그린 보물삼'</li></ul>                                                                               |
+| 1.0   | <ul><li>'방한모자2종 귀달이 털모자 군밤 스키 용품 트래퍼햇 마스크 캡방한모자 01.불구덩이군방모자 제이케이 아트 갤러리'</li><li>'[MLB] 패딩 트루퍼 귀달이 햇(3AWMPH136-50BKS) 블랙-50BKS/59H 에이케이에스앤디(주) AK플라자 평택점'</li><li>'겨울 곰돌이 후드 귀달이 모자 목돌이 동물 털모자 05.브���운 석진케이 주식회사'</li></ul>                                |
+| 9.0   | <ul><li>'스냅백 패션모자 snapback (투톤)그레이오렌지 루나마켓'</li><li>'스냅백 패션모자 snapback 레드 루나마켓'</li><li>'공용 메탈 원포인트 스냅백 뉴욕양키스 (32CP57111-50L)  '</li></ul>                                                                                                                |
+| 0.0   | <ul><li>'기본 군모 버킷햇 밀리터리 여자 빈티지군모 모자 남자 버캣햇 블랙 카키 / FREE 체인지비'</li><li>'빈티지 워싱 느낌 영문 레터링 장식 포인트 엣지 군모 그레이 (주)오너클랜'</li><li>'질좋은 군모 모자(차콜/국내생산) 네이비 프리마켓'</li></ul>                                                                                         |
+| 2.0   | <ul><li>'여자 겨울템 따뜻 극세사 양털곰돌이머리띠 귀마개 A24973_베이지_FREE 세븐제이스(7JS)'</li><li>'양털 곰돌이귀마개 귀도리 뽀글이 귀마개 방한귀마개 목도리 화이트 현성마켓'</li><li>'스타일 더하기-36-꽈배기방한귀마개 핑크 이미연'</li></ul>                                                                                         |
+| 6.0   | <ul><li>'국내발송 MARITHE FRANCOIS GIRBAUD 마리떼 CABLE KNIT BEANIE blue 1MG23SHG112 ONE SIZE 씨이랩'</li><li>'[매장발송] 마리떼 CLASSIC LOGO BEANIE black OS 와이에스마켓'</li><li>'MARITHE FRANCOIS GIRBAUD CABLE KNIT BEANIE gray 1MG23SHG112 227185 ONE SIZE 원플렉스'</li></ul> |
+| 8.0   | <ul><li>'비앙카 BIANCA (여성용) 누가/내추럴로고_OS '</li><li>'[롯데백화점]화이트샌즈 공용 UV 프로텍션 바이저 소니아 2.아이보리 롯데백화점_'</li><li>'화이트샌즈 소니아 UV 프로텍션 썬바이저 1종 [00003] 아이보리 현대홈쇼핑'</li></ul>                                                                                          |
+| 12.0  | <ul><li>'캉골 헌팅캡 울 플렉스핏 504 K0873 심리스 울 507 K0875 3107 남녀공용 베레모 3. K3107ST (Black)_SMALL 어썸우즈'</li><li>'다용도 활용 직원 종업원 단체 패션 모자 헌팅캡 화이트 가온'</li><li>'앨리 카페 바리스타 모자 베이커 캡 마도로스햇[루즈루나주얼리] 블랙 주식회사 웹이즈'</li></ul>                                              |
+| 11.0  | <ul><li>'1631뉴욕 볼캡 6color / 남녀공용모자 캡모자 그린 레이어드컴퍼니'</li><li>'패션벙거지0009 벙거지 가을 모자 여성 패션 밤색 골드코스트'</li><li>'꽈배기니트벙거지모자B28016 검정 프레임바이브'</li></ul>                                                                                                            |
+| 5.0   | <ul><li>'니트 베레모 S1450 진주방울 핑크 지에이치글로벌'</li><li>'[박민영, 라이즈 원빈 착용] 스터드 로고 울 베레모 블랙  '</li><li>'/ 베이직 레더 뉴스보이캡 빵모자 (2color) 아이보리_one size 롭스(robs)'</li></ul>                                                                                                |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.8489 |
+
+## 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_ac2")
+# Run inference
+preds = model("남여공용 기본군모 4컬러 EVE 카키 에브리씽굿")
+```
+
+<!--
+### 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   | 9.5523 | 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                    |
+| 10.0  | 50                    |
+| 11.0  | 50                    |
+| 12.0  | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0098  | 1    | 0.4348        | -               |
+| 0.4902  | 50   | 0.3427        | -               |
+| 0.9804  | 100  | 0.1921        | -               |
+| 1.4706  | 150  | 0.1061        | -               |
+| 1.9608  | 200  | 0.0544        | -               |
+| 2.4510  | 250  | 0.0384        | -               |
+| 2.9412  | 300  | 0.0155        | -               |
+| 3.4314  | 350  | 0.0128        | -               |
+| 3.9216  | 400  | 0.0177        | -               |
+| 4.4118  | 450  | 0.0082        | -               |
+| 4.9020  | 500  | 0.005         | -               |
+| 5.3922  | 550  | 0.0007        | -               |
+| 5.8824  | 600  | 0.0004        | -               |
+| 6.3725  | 650  | 0.0003        | -               |
+| 6.8627  | 700  | 0.0003        | -               |
+| 7.3529  | 750  | 0.0003        | -               |
+| 7.8431  | 800  | 0.0003        | -               |
+| 8.3333  | 850  | 0.0003        | -               |
+| 8.8235  | 900  | 0.0002        | -               |
+| 9.3137  | 950  | 0.0002        | -               |
+| 9.8039  | 1000 | 0.0001        | -               |
+| 10.2941 | 1050 | 0.0001        | -               |
+| 10.7843 | 1100 | 0.0001        | -               |
+| 11.2745 | 1150 | 0.0001        | -               |
+| 11.7647 | 1200 | 0.0001        | -               |
+| 12.2549 | 1250 | 0.0001        | -               |
+| 12.7451 | 1300 | 0.0001        | -               |
+| 13.2353 | 1350 | 0.0001        | -               |
+| 13.7255 | 1400 | 0.0001        | -               |
+| 14.2157 | 1450 | 0.0001        | -               |
+| 14.7059 | 1500 | 0.0001        | -               |
+| 15.1961 | 1550 | 0.0001        | -               |
+| 15.6863 | 1600 | 0.0001        | -               |
+| 16.1765 | 1650 | 0.0001        | -               |
+| 16.6667 | 1700 | 0.0001        | -               |
+| 17.1569 | 1750 | 0.0001        | -               |
+| 17.6471 | 1800 | 0.0001        | -               |
+| 18.1373 | 1850 | 0.0001        | -               |
+| 18.6275 | 1900 | 0.0001        | -               |
+| 19.1176 | 1950 | 0.0001        | -               |
+| 19.6078 | 2000 | 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 sha256:764f30753eecfc632a003f3438dd32d4cb4e73f0bb5c933a9b8fccddfbe02579
+size 442494816

model_head.pkl

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+oid sha256:75738c0048b61dad8ac19e1c17e86d9a55041063fd60f83f9d273de7ec14f5d7
+size 80895

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
+  },
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "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,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
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+      "special": true
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+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
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+    }
+  },
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "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]"
+}