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 +1,240 @@
+---
+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: '[갤러리아] [비비안][여]무봉제 햄팬티 3매입세트(BP0701)(타임월드) 3매세트_100 한화갤러리아(주)'
+- text: 하프클럽/크로커다일 이너웨어 심리스 퓨징 감탄브라 1+1 크림+베이지 1_사이즈 하프클럽
+- text: (신세계김해점)오르시떼 여성 C221 나시아 긴소매 원피스 L 신세계백화점
+- text: '[크로커다일 언더웨어][크로커다일] 라이크라 쉘론 몰드부착 V넥 스트랍 감탄브라 1종 택1 09.CDWBR4M09T 스트랍 라이트그린_XL '
+- text: 수정이네 데일리 베이직 나시탑 MFNC-030037 블랙/FREE 이궁이네
+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: 0.6911114499161457
+      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                                                                                                                                                                                                                                |
+|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 6.0   | <ul><li>'DBS7012 BYC 보디히트 발열 여자 반팔 티셔츠 내의 라이트스킨_85 에이치앤비 주식회사'</li><li>'바풀 융털기모 3부 속바지 드로즈 힙 워머 (90) MG 1911 3 wip 재색_90 _ F (주)에스비아이이너웨어'</li><li>'잔잔한 꽃프린트 반팔 3부 내의 LG7451 블루_100 '</li></ul>                                         |
+| 8.0   | <ul><li>'여성 D236 모드 민소매 원피스 OR24MFMBD236 1.S 롯데백화점'</li><li>'BYC 커플 잠옷 세트 가을 겨울 파자마 바지 체크 남성 여성 빅사이즈 수면 피치 기모 극세사 110 주니어 큰 1_MHS4615_L (95~100) 라브라'</li><li>'여 극세사 10부 파자마 팬츠 핑크 J203402010 215648 핑크_M 에스텍'</li></ul>                |
+| 2.0   | <ul><li>'인피티지 집업 올블랙 하이서포트 스포츠브라 70DD 네모난오렌지2'</li><li>'CALVIN KLEIN UNDERWEAR 여성 모던코튼 리프트 브라렛_QF5490100 화이트_L 에스제이글로벌'</li><li>'백온 로고밴드 삼각팬티NXWOU8941/세컨스킨 블랙_FREE 롯데쇼핑(주)'</li></ul>                                                  |
+| 1.0   | <ul><li>'[ ] 파워시리즈 하이웨스트 미드따이 중간보정 거들 XL(10398R) VERY BLACK_XL (주)씨제이이엔엠'</li><li>'디즈니 남아 여아 의류 가을 겨울 바지 2 피스/세트 Style 7 Style 10_100 크로노스직구'</li><li>'[스팽스](신세계강남점) TYT 2.0 보정 탱크 (10258R) CHAMPAGNE BEIGE_S 주식회사 에스에스지닷컴'</li></ul>   |
+| 4.0   | <ul><li>'여성 홈웨어 이너웨어 속바지 3부 쫄바지 짧은 레깅스 화이트_L 지에이치글로벌'</li><li>'여자 기모 밍크 속바지 겨울 교복 융속바지 블랙_FREE 제이스'</li><li>'[제임스딘] 국내산 여성 여자 텐셀 2부 속바지 JHWDT025 베이지_85 속옷세상'</li></ul>                                                                 |
+| 3.0   | <ul><li>'하프클럽/핏미인 핏미인 라이크라 풀커버맥스 노와이어 여성속옷세트 16종 MinSellAmount 하프클럽'</li><li>'[현대백화점][세컨스킨] NXWOU2011 2021년 노와이어 천연 뱀부 베이직 캐미브라 BLACK /55∼77 (주)현대백화점'</li><li>'[최초가 179 900원]비비안 스킨핏 FREE FIT V71 [0005]80 B CJONSTYLE_LIVE'</li></ul> |
+| 7.0   | <ul><li>'남성용 와이셔츠 잡아주는 가터벨트 2p세트  김상민'</li><li>'[ch4]삼각 브라패드 수영복 방수 수영복 비키니 볼륨업  도담도담몰'</li><li>'셔츠 가터벨트 와이셔츠 고정 빠짐방지 벨트(2P한세트) 셔츠 가터벨트(2P한세트) 홍스몰'</li></ul>                                                                           |
+| 0.0   | <ul><li>'[BYC본사]환타쟈 끈런닝16호 BYT3634 BK(검정색)_095 GSSHOP_'</li><li>'비너스자스민 여성 끈 나시 면스판 베이직 여자 런닝 JLG4506 살구(스킨)_90 아이보리shop'</li><li>'럭센스언더웨어 인견 쿨 노와이어 몰드 브라런닝 LU3007 BK_블랙_90A 주식회사 위드투윤'</li></ul>                                      |
+| 9.0   | <ul><li>'레이프릴 데일리 면스판 보정팬티 10종 90 쇼핑엔티'</li><li>'[트라이엄프](대전신세계)[Sioggi]슬로기 프리미엄 면스판 MIDI 데일리팬티 블랙 (TS76474/04) M/90 주식회사 에스에스지닷컴'</li><li>'[barbara](신세계강남점)1926 데일리 노라인 햄팬티 8종 세트(ABP5021SET) 100 주식회사 에스에스지닷컴'</li></ul>              |
+| 5.0   | <ul><li>'이벤트속옷 섹시 옆트임 슬립 란제리 야한 빅사이즈 원피스 잠옷 크리스마스속옷  메모리포인트'</li><li>'여성 빅사이즈 이벤트 속옷 섹시 슬립 망사 란제리  앤브리사'</li><li>'여성 미니 롱 슬립 인견 모달 이너 끈 원피스 속치마 여름 잠옷  라이크라라'</li></ul>                                                                 |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.6911 |
+
+## 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_ap2")
+# Run inference
+preds = model("(신세계김해점)오르시떼 여성 C221 나시아 긴소매 원피스 L 신세계백화점")
+```
+
+<!--
+### 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   | 9.9869 | 22  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 50                    |
+| 3.0   | 50                    |
+| 4.0   | 50                    |
+| 5.0   | 7                     |
+| 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0139  | 1    | 0.3999        | -               |
+| 0.6944  | 50   | 0.3239        | -               |
+| 1.3889  | 100  | 0.169         | -               |
+| 2.0833  | 150  | 0.033         | -               |
+| 2.7778  | 200  | 0.0122        | -               |
+| 3.4722  | 250  | 0.0022        | -               |
+| 4.1667  | 300  | 0.0008        | -               |
+| 4.8611  | 350  | 0.0006        | -               |
+| 5.5556  | 400  | 0.0004        | -               |
+| 6.25    | 450  | 0.0003        | -               |
+| 6.9444  | 500  | 0.0003        | -               |
+| 7.6389  | 550  | 0.0003        | -               |
+| 8.3333  | 600  | 0.0002        | -               |
+| 9.0278  | 650  | 0.0002        | -               |
+| 9.7222  | 700  | 0.0002        | -               |
+| 10.4167 | 750  | 0.0002        | -               |
+| 11.1111 | 800  | 0.0002        | -               |
+| 11.8056 | 850  | 0.0001        | -               |
+| 12.5    | 900  | 0.0001        | -               |
+| 13.1944 | 950  | 0.0001        | -               |
+| 13.8889 | 1000 | 0.0001        | -               |
+| 14.5833 | 1050 | 0.0001        | -               |
+| 15.2778 | 1100 | 0.0001        | -               |
+| 15.9722 | 1150 | 0.0001        | -               |
+| 16.6667 | 1200 | 0.0001        | -               |
+| 17.3611 | 1250 | 0.0001        | -               |
+| 18.0556 | 1300 | 0.0001        | -               |
+| 18.75   | 1350 | 0.0001        | -               |
+| 19.4444 | 1400 | 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_ap",
+  "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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+oid sha256:a29098868cdc992b182d1d56b6466018c0860bec0ae350abb8dd42c347177cd3
+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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+{
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+    "content": "[CLS]",
+    "lstrip": false,
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+    "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": {
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+      "content": "[CLS]",
+      "lstrip": false,
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+  "cls_token": "[CLS]",
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+  "do_lower_case": false,
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+  "padding_side": "right",
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+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}