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 +1,233 @@
+---
+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: 칸토스 남여 기능성 다이어트 지압슬리퍼 5. 신여성자갈_240 온누리산업
+- text: 국산 헤라칸 케나프 케냐프 발 발바닥 지압 건강 슬리퍼 실내화 연핑크(M) 예일마켓
+- text: 풀리오 종아리마사지기 V3  디와이shop
+- text: 풋 브러쉬 발각질 제거 마사지  큐오랩
+- text: 마사지 실내 발지압매트 돌지압판 50x200CM보보보생꽃롱 도치글로벌
+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: 0.9710123383380407
+      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:** 8 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>'자갈 매트 지압판 조약돌 발판 지압길 지압 발매트 40X60CM 컬러풀 에이알'</li><li>'뽀송뽀송 메모리폼 발닦개매트 욕실 주방 발매트 러그 대형 면 화장실 라지 50X80_레드 시그나몰'</li><li>'굳은 살 걱정없는 특허기술 헬스핀 발 지압매트 지압판 부모님 선물 효도 헬스핀 운동 기획상품_핑크 붐코리아'</li></ul>                                                  |
+| 1.0   | <ul><li>'김수자 엔젤 종아리 발마사지기 다리안마기 GKM-1004  닥터PLUS'</li><li>'듀플렉스 쎄라웨어 온열찜질 발마사지기 DP-FM700  신인선'</li><li>'7만원대 추가할인 여름휴가필수 [ ]업그레이드 3세대 스마트센서 종아리부터 허벅지까지 붓기 싹!! 무선다리마사지기 SR-S1+슬리밍삭스(옐로우) 수련닷컴'</li></ul>                                                |
+| 5.0   | <ul><li>'발각질양말 실리콘 패드 발보습 양말 발각질 케어 화이트 석진케이 주식회사'</li><li>'[BZJKWP4I_49]irbrush 뒤꿈치 패드 풋케어 발각질 3.블랙(5mm)FREE 롯데아이몰'</li><li>'일상공감 보드랍족 발보호대 1+1 뒤꿈치 발각질 보습풋패드 양말 발보호대 1+1_스킨 L 1쌍+화이트 M 1쌍 주식회사 이공구오'</li></ul>                                        |
+| 4.0   | <ul><li>'쾌발Q 60매 발냄새제거제/유해세균억제/무좀/발관리  해피MART'</li><li>'편백나무 슈즈프레쉬 신발장 옷장 탈취 발냄새제거 제습 방향효과  크레비스'</li><li>'[공식수입] 발냄새제거제 그랜즈레미디 페퍼민트향 cscosmetics'</li></ul>                                                                                            |
+| 0.0   | <ul><li>'평발 아치 슬리퍼 발바닥 통증 완화 아치슬리퍼 사파이어 블루_290 탱큐'</li><li>'통굽 지압슬리퍼 실내화 층간소음방지 미끄럼방��� 욕실화 도톨 지압 옐로우 39-40 9025 파인메탈릭'</li><li>'[낫소]낫소 지압2 슬리퍼 아이보리/230 패션플러스'</li></ul>                                                                                 |
+| 3.0   | <ul><li>'바렌 매직 스텐 양면 프로 발각질제거기  포유어뷰티'</li><li>'바렌 패디퍼펙트 전동 발각질 제거기 퍼플에디션 발 뒤꿈치 발바닥 굳은살 제거 패디플래닝 1세트(사은품 증정)_리뷰약속 x (주)마르스랩스'</li><li>'오제끄 실크풋 버퍼 단품  인앤인마켓'</li></ul>                                                                                  |
+| 7.0   | <ul><li>'[리퍼브]굿프렌드 밸런스휴 건식좌훈족욕기 GOOD-F5 리퍼브 건식좌훈족욕기 GOOD-F5 주식회사 굿테크'</li><li>'GSN-1610 편백나무 원적외선 건식 좌훈기+족욕기 겸용 MinSellAmount 온유어핏'</li><li>'B 굿프렌드 캐나다산 소나무 원목 스마트 건식족욕기 GOOD-F4 휴게실 가정용  마켓뷰'</li></ul>                                               |
+| 2.0   | <ul><li>'염색도구세트 셀프 키트 볼 브러쉬 가정용 헤어 브러시 빗 머리 모발 간편 07.Aeib 염색빗세트3P_본상품선택 주식회사유마켓'</li><li>'OC1242 손가락 발가락 관절보호 보습 실리콘 골무18종 통기화이트S(12425) 테익디스(TAKE THIS)'</li><li>'OC1242 손가락 발가락 보호 보습 실리콘 골무18종 실리콘골무 구멍뚫린골무 발가락 보호 골무스킨톤L(11033) 제이한 주식회사'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9710 |
+
+## 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_lh11")
+# Run inference
+preds = model("풋 브러쉬 발각질 제거 마사지  큐오랩")
+```
+
+<!--
+### 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   | 9.9325 | 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                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0159  | 1    | 0.4383        | -               |
+| 0.7937  | 50   | 0.2003        | -               |
+| 1.5873  | 100  | 0.0636        | -               |
+| 2.3810  | 150  | 0.0158        | -               |
+| 3.1746  | 200  | 0.0239        | -               |
+| 3.9683  | 250  | 0.0153        | -               |
+| 4.7619  | 300  | 0.0004        | -               |
+| 5.5556  | 350  | 0.0023        | -               |
+| 6.3492  | 400  | 0.0005        | -               |
+| 7.1429  | 450  | 0.0002        | -               |
+| 7.9365  | 500  | 0.0001        | -               |
+| 8.7302  | 550  | 0.0001        | -               |
+| 9.5238  | 600  | 0.0001        | -               |
+| 10.3175 | 650  | 0.0001        | -               |
+| 11.1111 | 700  | 0.0001        | -               |
+| 11.9048 | 750  | 0.0001        | -               |
+| 12.6984 | 800  | 0.0           | -               |
+| 13.4921 | 850  | 0.0001        | -               |
+| 14.2857 | 900  | 0.0001        | -               |
+| 15.0794 | 950  | 0.0001        | -               |
+| 15.8730 | 1000 | 0.0           | -               |
+| 16.6667 | 1050 | 0.0001        | -               |
+| 17.4603 | 1100 | 0.0           | -               |
+| 18.2540 | 1150 | 0.0001        | -               |
+| 19.0476 | 1200 | 0.0001        | -               |
+| 19.8413 | 1250 | 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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+    "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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+}

tokenizer_config.json

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+  "truncation_strategy": "longest_first",
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+}