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 +1,253 @@
+---
+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: 네이쳐리빙 모던 트롤리 스윙 3단 빨래바구니 E) ★한정특가★_E05간편보관접이식대야(S)_블루 서전통상
+- text: 빨래방망이 다듬이방망이 2P세트 이불방 다듬잇방망이  신규A
+- text: '[홈앤하우스]라탄 패턴 사각 햄퍼 80L 내추럴/단품 패션플러스'
+- text: 빨래판 세면대 세라믹 매립형 가정용 발코니 세미빌트인 간이 개수대 4. A형 35x46 - 수전 별도 구매 고야글로벌
+- text: 전동빨래건조대 베란다 건조대 전동 자동 천정 천장형 천장 T-V917Yahei텔레스코픽로드 데일바이
+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: 0.9600729631130929
+      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:** 12 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>'신발세척 브러쉬 싱크대 가벼운 다용도 브러시 실내화  푸른은하수마트'</li><li>'방글방글운동화솔5P 다용도솔  따뜻한마켓'</li><li>'신발닦이솔 아이디어 청소솔 화이트 에이치비상사'</li></ul>                                                                 |
+| 8.0   | <ul><li>'삼정 스마트세탁망 내복용 31x50cm 4441  장가요몰'</li><li>'무형광 국산 세탁망 맘스필 브라망 사각 원형 특대형 드럼세탁기 빨래망 사각XL-미세망 rodzina'</li><li>'고급형 세탁기 먼지망/이물질/먼지/세탁기 거름망 블루 레이어드컴퍼니'</li></ul>                         |
+| 1.0   | <ul><li>'면마직류 고급 다리미풀 정전기방지 말표 말표다리미풀480ml 추가E'</li><li>'말표 다리미풀 다림질풀 뉴 스프레이 다리미용품 480ml 고급의류 다림풀 의류다림 MinSellAmount 종종걸음샵'</li><li>'키밍 핸드 스팀 다리미판 다리미 스폰지 장갑 상품선택_7403 삼각형 꼬부기 가게'</li></ul>  |
+| 6.0   | <ul><li>'다용도실 빨래판 세면대 속옷빨래 사각형 간이세면기 비 47x54 더드컨트리'</li><li>'[여름신상베스트]키높이빨래판  자스트데어(JUST THERE)'</li><li>'Per 빨래판 속 튼튼한 부드러운판 논슬립 손빨래 미끄럼방지 비누수납 일체형디자인 옷빨래 실용적인 대형_그레이 투베스트컴퍼니'</li></ul>     |
+| 5.0   | <ul><li>'집 소형 편리한 공간활용 미니행거-집게20p 접이식 폴딩 가정용 심플  스위트 가이 (sweet guy)'</li><li>'다용도 만능 신발정리 휴지집게 구두 물건 잡는 집게  미아앤미오 컴퍼니'</li><li>'접이식 미니행거-집게20p 폴딩 소형 공간활용  풍성한정원'</li></ul>                    |
+| 9.0   | <ul><li>'세탁볼 실내건조세제 드럼 이용가능 매직클린 통돌이  행복나라'</li><li>'쇼핑추천 포함 양모볼 인기제품 건조기 키니툴 6P 파우치  핑쇼24'</li><li>'[1300K] 고슴도치 세탁볼 3개 세트  엔에이치엔위투 주식회사'</li></ul>                                           |
+| 4.0   | <ul><li>'키친아트 빨래 삶는 통 냄비 인덕션 빨래솥 삶숙이 32cm 3.키친아트 34cm(일반) 척척홀릭'</li><li>'삶순이 행주삶기 행주 냄비 스테인리스 삶기 22cm 빠른대행'</li><li>'키친아트 스팀빨래솥30CM ZW1E3205  윤지상회'</li></ul>                                  |
+| 0.0   | <ul><li>'일상드로우 다리미판 스팀다리미 스탠드 접이식 스팀다리미판 스탠드 다다마스'</li><li>'휴대용 핸드 스팀 다리미판 패드 장갑 핸드다리미판장갑 주식회사 아이니쥬'</li><li>'가담다 접이식 스팀 좌식 플립 다리미판 (스톤그레이/프리미엄블랙)/높이조절7단 다리미판 모음전 우마형 화이트 가담다공식스토어'</li></ul> |
+| 7.0   | <ul><li>'캠핑용 빨래줄 여행용 빨랫줄 품 휴대용 용품 야외 건조  동그라미'</li><li>'스테인레스 빨래줄 이불 걸이대 스텐 빨랫줄 와이어 304 빨랫줄(10m) 버클세트 스마일_'</li><li>'빨래집게걸이 12P 빨레 집개 건조대 찝개 형 빨래 집게 연두 골드코스트'</li></ul>                       |
+| 11.0  | <ul><li>'[판매순위1등] 삼성에어드레서 5벌 DF-FL 호환필터  팡스토리'</li><li>'부드러운 카카오프렌즈 기획전 차박 무릎 학생 잇템 캠핑 춘식이 사무실 담요  청춘유통'</li><li>'암앤해머 베이킹 소다 6.12kg x 2 세척 탈취  백두마켓'</li></ul>                                |
+| 3.0   | <ul><li>'벽걸이 메쉬 빨래 바구니 벽걸이 메쉬빨래바구니(핑크) 위드주'</li><li>'친환경 플라스틱 라탄 대용량 특대형 빨래바구니 60L 2. 베이지 '</li><li>'빨래통 3단 트롤리 4단 이동식 스윙빨래바구니 세탁함 빨래바구니3단 사구있오'</li></ul>                                     |
+| 2.0   | <ul><li>'리빙나이스 천장건조대 스텐봉 200cm 소경환'</li><li>'삼덕기업 하드웰 PVC코팅봉 스텐봉 베란다 천장빨래건조대 스텐봉 표준형 까바짬'</li><li>'전동빨래건조대 발코니 베란다 천장형 조명 C 꾸대'</li></ul>                                                      |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9601 |
+
+## 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_lh13")
+# Run inference
+preds = model("빨래방망이 다듬이방망이 2P세트 이불방 다듬잇방망이  신규A")
+```
+
+<!--
+### 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   | 9.8017 | 20  |
+
+| 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                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0106  | 1    | 0.4631        | -               |
+| 0.5319  | 50   | 0.4236        | -               |
+| 1.0638  | 100  | 0.2102        | -               |
+| 1.5957  | 150  | 0.114         | -               |
+| 2.1277  | 200  | 0.0733        | -               |
+| 2.6596  | 250  | 0.0541        | -               |
+| 3.1915  | 300  | 0.0316        | -               |
+| 3.7234  | 350  | 0.0104        | -               |
+| 4.2553  | 400  | 0.0098        | -               |
+| 4.7872  | 450  | 0.0039        | -               |
+| 5.3191  | 500  | 0.0026        | -               |
+| 5.8511  | 550  | 0.0002        | -               |
+| 6.3830  | 600  | 0.0002        | -               |
+| 6.9149  | 650  | 0.0001        | -               |
+| 7.4468  | 700  | 0.0001        | -               |
+| 7.9787  | 750  | 0.0001        | -               |
+| 8.5106  | 800  | 0.0001        | -               |
+| 9.0426  | 850  | 0.0001        | -               |
+| 9.5745  | 900  | 0.0001        | -               |
+| 10.1064 | 950  | 0.0001        | -               |
+| 10.6383 | 1000 | 0.0001        | -               |
+| 11.1702 | 1050 | 0.0001        | -               |
+| 11.7021 | 1100 | 0.0001        | -               |
+| 12.2340 | 1150 | 0.0001        | -               |
+| 12.7660 | 1200 | 0.0           | -               |
+| 13.2979 | 1250 | 0.0001        | -               |
+| 13.8298 | 1300 | 0.0001        | -               |
+| 14.3617 | 1350 | 0.0           | -               |
+| 14.8936 | 1400 | 0.0001        | -               |
+| 15.4255 | 1450 | 0.0           | -               |
+| 15.9574 | 1500 | 0.0001        | -               |
+| 16.4894 | 1550 | 0.0           | -               |
+| 17.0213 | 1600 | 0.0           | -               |
+| 17.5532 | 1650 | 0.0           | -               |
+| 18.0851 | 1700 | 0.0           | -               |
+| 18.6170 | 1750 | 0.0           | -               |
+| 19.1489 | 1800 | 0.0           | -               |
+| 19.6809 | 1850 | 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

@@ -0,0 +1,29 @@
+{
+  "_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

@@ -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

@@ -0,0 +1,4 @@
+{
+  "normalize_embeddings": false,
+  "labels": null
+}

model.safetensors

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+oid sha256:6f4161708a9c42e493a78512fd7d41db61dc908844ad075aaebbd8ac477a63e2
+size 442494816

model_head.pkl

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+oid sha256:ed312449dd52252bcd237d9727e26aa3452c71a75534eb6a979a720767241f73
+size 74727

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

@@ -0,0 +1,4 @@
+{
+  "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
+    },
+    "1": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
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+      "lstrip": false,
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+      "special": true
+    },
+    "4": {
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+      "single_word": false,
+      "special": true
+    }
+  },
+  "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]"
+}