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 +1,238 @@
+---
+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: 핸드자키 미니 전기 지게차 트럭 스태커 1 톤 15 1500kg 용량 4 륜  피치본
+- text: 영원/욕창방지 에어 매트리스/MARK-II GREEN  향누리
+- text: 지팡이의자 어르신 노인 휴대용 경량 접이식 스틱 지팡이 의자 미끄럼 방지 스틱 스툴 스카이 블루 업그레이드 145-18 매니몰
+- text: 오토바이 휠체어 경사로 계단 안전발판 상가 문턱 진입판 DO.높이 7.2cm 고무 에스제이몰
+- text: 고품질 장애인 화장실 손잡이 안전바 변기 안전손잡이 노인 화장실안전바 안전바(화이트 30cm) 우주방위사령부
+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: 0.880607050235328
+      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:** 9 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>'가하 영원메디칼 욕창 클리어뷰 에어매트 요양병원침대매트 KH-1 AD1200 메디트리'</li><li>'삼각 베개 욕창방지 자세변환용구 복지용구 실버용품 SW-J3 일반구매 (장기요양등급 대상자가 아닌 경우) 케이에이치라이프(KHLife)'</li><li>'[복지용구]자세변환용구 젤패드 LBP-01 본인부담율15% 우리의료기'</li></ul>                                                        |
+| 3.0   | <ul><li>'샤이닝 DS-EX 프로 3D 레이저 구강 스캐너, 휴대용 이어몰드 보청기 01 DS-EX pro 로얄트레이딩'</li><li>'골전도보청기 노인 귀걸이형 블루투스 보청기 증폭기 1_레드 손소독제'</li><li>'일본 리오네트 포켓형 보청기 HA-20DX  이화메디케어(보청기)'</li></ul>                                                                                   |
+| 4.0   | <ul><li>'청려장 노인 지팡이 목발 나무 걷기보조기구 선물 M 직구202'</li><li>'노인용지팡이 연수목 도사 나무 우드 목재 지팡이 A타입 88cm 한발(165-175사용)4발판 지금상점'</li><li>'세비앙 CV100 안전손잡이 봉형 기둥형 안전바 천정 부착식 봉 안전봉 노인복지용구 일반구매 (A+) 늘찬 복지용구'</li></ul>                                                             |
+| 7.0   | <ul><li>'납골당 봉안당 소품 미니어쳐 강아지 꾸미기 JXK치와와 2034C(그레이) 하니준한진'</li><li>'국산 보자기 유골 흰색 함보 흰보자기 유골함 흰색보자기 블루매거진'</li><li>'애견유골함 도자기 납골함 꽃 세라믹 진공 밀폐 수국 - 삼원병 영맨'</li></ul>                                                                                                |
+| 2.0   | <ul><li>'공사장 화장실 현장용 이동식 화징실 휴게소 캠핑장 샤워가 있는 1.1x1.1x2.3 직선 행 차이엘리'</li><li>'이동식 샤워실 간이화장실 건설현장 조립식 야외 임시 거름 상자 포함 1.1x1.1x2.3 마구팜'</li><li>'화장실 변기안전손잡이 MSH-103 좌변기 보조대 지지대 양변기 안전바 노인 복지용구 고급형 부식방지 MSH-502(스테인리스)_노인장기요양등급 구입(본인부담금 6%) (A+) 늘찬 복지용구'</li></ul> |
+| 1.0   | <ul><li>'헤어 워시 접이식 바스켓 CareFree H-1004 핑크 블루 미루카'</li><li>'손목보호대 커버 샤워 깁스 통깁스 붕대 보호대 방수포 상품선택_발목 제이프로젝트'</li><li>'CareFree H-1004 헤어 워시 접이식 바스켓 핑크 블루 (주)이마켓'</li></ul>                                                                                           |
+| 0.0   | <ul><li>'Coms 납땜 LED 확대경 스탠드 2LED OE792  마트천'</li><li>'독서 확대경 스탠드 LED 조명 대형 돋보기 확대기  제이티컴퍼니'</li><li>'오토스 OTOS 용접확대경(돋보기) 산업안전용품 안전용품 안면보호구 용접용품 2.50 스위치컴퍼니'</li></ul>                                                                                           |
+| 5.0   | <ul><li>'할머니유모차 노인 어르신 성인용 보행기 보행보조기 실버카 DS-379 DS-800 PLUS 800 (주)메디위'</li><li>'살졸 보행기 카본 롤레이터 노인 보행보조기 환자용 워커 할머니 워커기 어른유모차 복지용구 그린_미디움( 신장 : 170cm~200cm)_노인장기요양등급 구매(본인부담금 15%) (A+) 늘찬 복지용구'</li><li>'태양메디텍 해피웰리스 보행보조차 F-338  메디위'</li></ul>                |
+| 8.0   | <ul><li>'족욕통 정맥류 정맥 치료 적인 완화 혈관염 정맥염 거미줄 통증 05 Buy 10 get 13 free 율리시에'</li><li>'휠체어 바퀴 베어링 타이어 부품 22인치 교체 수동 22인치  (솔리드 타이어) - 그레이 하나누리'</li><li>'머리 받침 베개 노인 목받침대 환자 쿠션 악세사리 휠체어 헤드레스트 이동식 1. 블랙 패스트커머스'</li></ul>                                              |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.8806 |
+
+## 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_lh16")
+# Run inference
+preds = model("영원/욕창방지 에어 매트리스/MARK-II GREEN  향누리")
+```
+
+<!--
+### 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   | 10.4333 | 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                    |
+| 8.0   | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0141  | 1    | 0.4502        | -               |
+| 0.7042  | 50   | 0.3154        | -               |
+| 1.4085  | 100  | 0.0837        | -               |
+| 2.1127  | 150  | 0.0399        | -               |
+| 2.8169  | 200  | 0.0081        | -               |
+| 3.5211  | 250  | 0.0057        | -               |
+| 4.2254  | 300  | 0.0003        | -               |
+| 4.9296  | 350  | 0.0002        | -               |
+| 5.6338  | 400  | 0.0001        | -               |
+| 6.3380  | 450  | 0.0002        | -               |
+| 7.0423  | 500  | 0.0001        | -               |
+| 7.7465  | 550  | 0.0001        | -               |
+| 8.4507  | 600  | 0.0001        | -               |
+| 9.1549  | 650  | 0.0001        | -               |
+| 9.8592  | 700  | 0.0001        | -               |
+| 10.5634 | 750  | 0.0           | -               |
+| 11.2676 | 800  | 0.0001        | -               |
+| 11.9718 | 850  | 0.0           | -               |
+| 12.6761 | 900  | 0.0           | -               |
+| 13.3803 | 950  | 0.0           | -               |
+| 14.0845 | 1000 | 0.0001        | -               |
+| 14.7887 | 1050 | 0.0           | -               |
+| 15.4930 | 1100 | 0.0           | -               |
+| 16.1972 | 1150 | 0.0           | -               |
+| 16.9014 | 1200 | 0.0           | -               |
+| 17.6056 | 1250 | 0.0           | -               |
+| 18.3099 | 1300 | 0.0           | -               |
+| 19.0141 | 1350 | 0.0           | -               |
+| 19.7183 | 1400 | 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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+size 442494816

model_head.pkl

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modules.json

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+  {
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+    "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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+    "lstrip": false,
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+  "tokenizer_class": "BertTokenizer",
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+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}