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: 트라택 마사지건 액티브건 팟 휴대용 초소형 김계란 근육 무선마사지 코발트블루&차콜 주식회사 나음케어
+- text: 접이식 도수 치료 추나 테이블 경락 안마 피부샵 베드 미용 침대 마사지 관리 보라색 70cm 침대+침공베개 레비하이
+- text: 케잔 괄사 마사지 승모근 어깨 림프순환 괄사 세라믹괄사 1. 옵션1 강한자극 케잔아일랜드
+- text: 접이식 마사지 침대 피부관리 한의원 안마 미용베드 11.70cm 와이드 2단 그레이 서진홀딩스
+- text: 등 허리 경추 다기능 전신 목 어깨 전기 마사지 쿠션 허리안마기 A_EU 파밀리아
+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: 0.8100799016594961
+      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:** 6 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                                                                                                                                                                                                                            |
+|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 2.0   | <ul><li>'멜킨 바른 그래핀 무릎 온열 찜질 마사지기 무선 안마기 찜질마사지기듀얼2개 아이니쥬♥'</li><li>'눈편한세상 눈 안마기 온열안대 공기압 기계 마사지기  오아월드'</li><li>'엘보타파 전완근 무선 온열 공기압 손 마사지기 MDM-1422S 실버 MDM-1422S 메디니스'</li></ul>                                                    |
+| 6.0   | <ul><li>'보이로 640.37 MG 260 지압 마사지기 시트 커버 블랙 사무실 의자 시니어 부모님  유로사이드라인'</li><li>'차량용안마기 화물차 기사용 바디 피로 자궁 쿠션 안정기 탑 USB/308E 디럭스 2 오션글림'</li><li>'차량용안마기 화물차 기사용 바디 피로 자궁 쿠션 안정기 탑 USB/308E 디럭스 1 오션글림'</li></ul>                        |
+| 0.0   | <ul><li>'접이식 도수 치료 추나 테이블 경락 안마 피부샵 베드 미용 침대 마사지 관리 조절형 커피색 60cm 침대+침공베개 레비하이'</li><li>'고급 비닐커버 특대형 침대비닐커버 침대 맞춤 병원 피부샵 경락샵 2000x1300_피부비닐커버(구멍X) skin2010'</li><li>'바이오힐보 프로바이오덤 리프팅 괄사 마사저 림프 마사지기 쿨링 마사저 쿨링 마사저 제이글로벌'</li></ul> |
+| 4.0   | <ul><li>'[ 추가 만원 할인쿠폰 ] 파나소닉 김강우 안마의자 EP-MA05 (2종택1)+/ 카페트 및 무상 AS 1년 화이트&클래식블루 (주)렙테크'</li><li>'[세라젬] 파우제 M4 안마의자 마사지 휴식가전 베이지 세라젬'</li><li>'웰모아 안마의자 공기압마사지기 다리길이 조절 등 허리 온열 HCW-6300  도레미마켓'</li></ul>                           |
+| 3.0   | <ul><li>'���프렌드 온열 전신안마매트 GT-S6  GSSHOP_'</li><li>'목욕탕 때밀이 침대 교체용 마사지 베드 매트 쿠션 판 블루 185x60 밀꾸밀꾸'</li><li>'혜성의료기 국내생산 두타매트 HS-770 온열과 안마 받침대옵션 마사지매트 받침대없음_14봉 멸치쇼핑'</li></ul>                                                         |
+| 1.0   | <ul><li>'멜킨 멜리즈 마사지건 무선 전동 휴대용 저소음 마사지기 어깨 승모근 전신 안마기 선물 빈티지 아이보리 (주)거성디지털'</li><li>'인썸 휴대용 미니 핸디 전동 마사지건 IMG-150  '</li><li>'멜킨 멜리즈 마사지건 무선 전동 휴대용 저소음 마사지기 어깨 승모근 전신 안마기 선물 제트 블랙 (주)거성디지털'</li></ul>                             |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.8101 |
+
+## 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_lh17")
+# Run inference
+preds = model("등 허리 경추 다기능 전신 목 어깨 전기 마사지 쿠션 허리안마기 A_EU 파밀리아")
+```
+
+<!--
+### 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   | 11.3370 | 23  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 20                    |
+| 2.0   | 50                    |
+| 3.0   | 50                    |
+| 4.0   | 50                    |
+| 6.0   | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0233  | 1    | 0.4557        | -               |
+| 1.1628  | 50   | 0.2241        | -               |
+| 2.3256  | 100  | 0.0604        | -               |
+| 3.4884  | 150  | 0.0172        | -               |
+| 4.6512  | 200  | 0.0031        | -               |
+| 5.8140  | 250  | 0.0009        | -               |
+| 6.9767  | 300  | 0.004         | -               |
+| 8.1395  | 350  | 0.0001        | -               |
+| 9.3023  | 400  | 0.0           | -               |
+| 10.4651 | 450  | 0.0           | -               |
+| 11.6279 | 500  | 0.0           | -               |
+| 12.7907 | 550  | 0.0           | -               |
+| 13.9535 | 600  | 0.0           | -               |
+| 15.1163 | 650  | 0.0           | -               |
+| 16.2791 | 700  | 0.0           | -               |
+| 17.4419 | 750  | 0.0           | -               |
+| 18.6047 | 800  | 0.0           | -               |
+| 19.7674 | 850  | 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_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

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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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model_head.pkl

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modules.json

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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "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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+    "single_word": false
+  }
+}

tokenizer_config.json

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+  "truncation_strategy": "longest_first",
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+}