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

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+---
+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: 세이코 SBTR SBTR011 전용 힐링쉴드 시계보호필름 기스방지 유리보호필름 31평면 스타샵
+- text: 시계줄 교체공구 스프링툴바/메탈,가죽밴드 변경도구/시계줄질도구 스프링바툴 멀티형 올리브tree
+- text: 오메가호환 시계줄 스트랩 가죽 시계 체인 12 OMJ-브라운 화이트 라인 + 실버_20mm 더블드래곤(Double dragon)
+- text: Uhgbsd 가죽 스트랩 VC 바쉐론 콘스탄틴 시계 호환 남성 액세서리 19mm 20mm 22mm 1_10 Black Gold Fold
+    Bk 시구왕씨
+- text: 디젤 DZ4316 DZ7395 7305 4209 4215 용 스테인레스 스틸 시계 호환용 남성용 메탈 솔리드 밴드 24mm 30mm
+    04 B Black_05 30mm 아이스박스(ICEBOX)
+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: 0.5793723141033988
+      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:** 5 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                                                                                                                                                                                                                                                                  |
+|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0.0   | <ul><li>'카시오 DW5600 시계 호환 16mm 러버 워치 밴드 실리콘 스트랩 우레탄 시계줄 옐로우 블랙 A_16mm 로움'</li><li>'갤럭시핏2 스트랩 실리콘 밴드 민트 보미헤안랩소디'</li><li>'로이드 어썸픽 소형 메쉬밴드 (2종 택 1) LL2B19611X LL2B19611XMG 로즈골드 세컨드플랜'</li></ul>                                                                           |
+| 3.0   | <ul><li>'BOBO BIRD 네이비 블루 커플 손목 시계 연인 나무 쿼츠 맞춤형 각인 최고 럭셔리 브랜드 여성용 2.Paper Box 2 Woman 아더월드'</li><li>'캐주얼남녀손목시계 남자시계 폭발적인 벨트 테리어 시계 유럽 및 미국 시계선물 여자시계 Grey 리마113'</li><li>'남녀 커플 시계 SCRRJU 스테인레스 스틸 밴드 방수 연인 Se 패션 캐주얼 손목 선물 09 9 홀릭스'</li></ul>                           |
+| 4.0   | <ul><li>'[프레드릭콘스탄트](신세계본점) FC-330MC4P6 클래식 문페이즈  주식회사 에스에스지닷컴'</li><li>'[다양한선물]순토 코어 올블랙 레귤러블랙 코어블랙레드 순토5 WHR 모음 시리즈 선택01.SS014279010 순토코어올블랙 스타샵'</li><li>'헬스공부타이머 집중공부타이머 요리 낮잠 여가 시간관리 알람 큐브 SW9EF763 15-60분 화이트 현대몰'</li></ul>                                        |
+| 2.0   | <ul><li>'SUNOEL 3기압 5기압 방수 어린이 초등학생 전자 손목시계 모음  SUNOEL'</li><li>'손목시계쇼핑몰 아동용손목시계(16-5A) 손목시계대량  기프트한국'</li><li>'어린이 손목시계 초등학생 시계 키즈 전자시계 유아 스마트워치 남아 여아  제이에이취'</li></ul>                                                                                                 |
+| 1.0   | <ul><li>'제작 빈 핀 버튼 메이커 부품 기계 용품 세트 25mm 32mm 37mm 44mm 50mm 56mm 58mm 50 개 [1]50sets_@#@[7]58mm 캐롤스하우스'</li><li>'무소음 무브먼트 시계 부품 모터 바늘 공예 DIY 선택D시계판_거북이 제이릴'</li><li>'시계공구 기타 야마하 YZF R125 R 125 YZFR125 20082013 바이크 오토바이 핸드가드 실드 핸드 가드 보호대 앞유리 07 Green 유비즈엘'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.5794 |
+
+## 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_ac6")
+# Run inference
+preds = model("세이코 SBTR SBTR011 전용 힐링쉴드 시계보호필름 기스방지 유리보호필름 31평면 스타샵")
+```
+
+<!--
+### 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   | 10.9107 | 22  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 24                    |
+| 3.0   | 50                    |
+| 4.0   | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0286  | 1    | 0.3696        | -               |
+| 1.4286  | 50   | 0.1249        | -               |
+| 2.8571  | 100  | 0.0114        | -               |
+| 4.2857  | 150  | 0.0001        | -               |
+| 5.7143  | 200  | 0.0001        | -               |
+| 7.1429  | 250  | 0.0001        | -               |
+| 8.5714  | 300  | 0.0001        | -               |
+| 10.0    | 350  | 0.0001        | -               |
+| 11.4286 | 400  | 0.0           | -               |
+| 12.8571 | 450  | 0.0001        | -               |
+| 14.2857 | 500  | 0.0           | -               |
+| 15.7143 | 550  | 0.0           | -               |
+| 17.1429 | 600  | 0.0           | -               |
+| 18.5714 | 650  | 0.0           | -               |
+| 20.0    | 700  | 0.0           | -               |
+
+### 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

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+{
+  "_name_or_path": "mini1013/master_item_ac",
+  "architectures": [
+    "RobertaModel"
+  ],
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+  "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,
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+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
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+  "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": {},
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+  "similarity_fn_name": null
+}

config_setfit.json

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+  "normalize_embeddings": false
+}

model.safetensors

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model_head.pkl

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modules.json

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+    "type": "sentence_transformers.models.Transformer"
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+    "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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tokenizer_config.json

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+}