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 +1,254 @@
+---
+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: 모어네이처 프리미엄 코엔자임Q10 500mg x 60캡슐  주식회사 템스윈(Tems Win)
+- text: 솔가 맥주효모 비타민B12 티아민 250정 맥주효모 250정(80일분) X 2개 꿈
+- text: 닥터스베스트 OptiMSM 함유 MSM 분말 250g(8.8oz)  메트로 나인
+- text: 정관장 홍삼원 6년근 홍삼농축액 50ml 30포 고형분60% 선물세트 쇼핑백포함  주식회사 무한종합상사
+- text: 락토핏 당케어 2g x 60포  (주)레놈 성수지점
+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: 0.7147122562003371
+      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:** 13 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>'콤비타 마누카꿀 UMF10 500g 2개 2.콤비타 마누카꿀 UMF10+ 500g 2개 재인뉴트리셔널'</li><li>'데저트 크릭 로 텍사스 꿀 340g 2개 Desert Creek Honey Raw Unfiltered Texas Honey  누크몰글로벌'</li><li>'한울벌꿀 국내산 아카시아꿀 사양 벌꿀 2.4kg 아카시아꿀 2.4kg 선흥물산'</li></ul> |
+| 6.0   | <ul><li>'블루미너스 왕의 맥문동 30포X4박스/국내산 볶은 맥문동 뿌리 가루 맥문동차 사포닌 추천  주식회사 대한종합상사'</li><li>'홍국 발효 구기자 분말 가루 청양구기자 홍국균 500g 홍국발효구기자 분말 500g (1팩) 푸드센스'</li><li>'GNM자연의품격 루테인 지아잔틴 아스타잔틴 500mg x 30캡슐  케이티씨 주식회사'</li></ul>           |
+| 0.0   | <ul><li>'[3+1 이벤트] 데이풀 호박즙 아르기닌 110ml 1박스 여자아르기닌 쌍수 성형 붓기 늙은호박즙  어반랩스 주식회사'</li><li>'홍삼생알칡즙  망구 에이'</li><li>'국산 아로니아원액 1L 2병 아로니아즙  농업회사법인 청정산들해(주)'</li></ul>                                                            |
+| 5.0   | <ul><li>'고농축 랩온랩 k2 히말라야 숙취해소제 허브 추출물 1박스 30정  단비'</li><li>'고농축 k2 히말라야 숙취해소제 허브 추출물 1박스 30정 히말라야숙취해소제 (30정) 랩온랩(LAB ON LAB)'</li><li>'히말라야 숙취해소 파티스마트 소프트 츄 10개입 04_파티스마트 2개입 4박스 (주)히말라야코리아'</li></ul>                  |
+| 10.0  | <ul><li>'황성주박사의 마시는 영양식 140ml 24팩 고소한 곡물맛  하얀'</li><li>'뉴케어 고칼슘 영양갱 40g 30입  아르미유'</li><li>'대상웰라이프 마이밀 마이키즈 밀크맛 150ml 24팩 아이간식 +Npay 2000원 대상웰라이프(주)'</li></ul>                                                           |
+| 3.0   | <ul><li>'Green Foods 그린푸드 마그마 플러스 드링크 믹스 10.6oz  캐주얼'</li><li>'캐나다 영양제 퀄리티랩 프리미엄 퀄리티 로얄제리 1000mg \ufeff200캡슐  바이오 파크(Bio park)'</li><li>'(100매)푸드어홀릭 네이처스킨 오이 마스크팩 (100매)푸드어홀릭 네이처스킨 오이 마스크팩 차일드'</li></ul>               |
+| 9.0   | <ul><li>'동원 천지인 홍삼절편 수 (15g 8갑 8일분)  (주)동방유래'</li><li>'정관장 홍삼차 100포 인삼차 건강차 홍삼차(포장O) 주식회사 앨리스월드'</li><li>'[정기구독]함소아 홍키통키 프리미어 그린 1박스  함소아제약'</li></ul>                                                                    |
+| 8.0   | <ul><li>'우슬 300g 우슬분말 300g 농업회사법인 주식회사 두손애약초'</li><li>'차가버섯 선물세트 1kg 러시아/고급바구니 포장  주식회사 생생드림'</li><li>'산사 열매 국내산 300g 산사자 아가위 나무 산사 300g x5개(10%할인) 농업회사법인 주식회사 두손애약초'</li></ul>                                          |
+| 11.0  | <ul><li>'유한m 액티브 셀렌효모 플러스 480정 / 맥주효모 셀레늄 옥타코사놀  헬스피아'</li><li>'효소락 30포  용한약국'</li><li>'여에스더 맥주효모 비오틴 울트라 케어 5200 맥스 국내 최대함량 맥주효모&비오틴 소형환 [30%] 1박스 (14포) 에스더포뮬러 주식회사'</li></ul>                                          |
+| 1.0   | <ul><li>'몽글환 차전자피환 4g 30포  우리닥터'</li><li>'국산 여주환 500g 여주환 500g 주식회사 건강중심'</li><li>'마카 아르기닌 야관문 서리태환 남자 활력 모발관리 필수 콩의두감 3병 선물세트 (2+1)야관문 플러스 세트 3개(9병) 주식회사 루토닉스'</li></ul>                                                |
+| 4.0   | <ul><li>'암웨이 더블엑스 종합비타민 무기질 리필 칼맥디 프렌즈'</li><li>'종근당 이뮨 듀오 멀티비타맥스 140ml 7병 1박스  늘품서치'</li><li>'네추럴라이즈 멀티비타민 꾸미 2.5g x 60개입  현민예 스토어'</li></ul>                                                                            |
+| 12.0  | <ul><li>'활강원 백만 임산부 발효 자연 곡물 효소 400만 역가수치 효소제 100포 백만효소 x 2박스 (200스틱) 디와이코어'</li><li>'이영애의건강미식 카무트 브랜드 밀 효소 골드 3g x 30포  주식회사 템스윈(Tems Win)'</li><li>'이영애의 건강미식 골드 카무트 효소 1개월 분 카무트 효소 1개 주식회사 네이처라우드'</li></ul>          |
+| 7.0   | <ul><li>'인삼 도매일번지 난발삼 금산 세척 난발삼_03난발삼 소500g_세척안함 금산인삼 도매 일번지'</li><li>'동우당제약 궁중대보 250g 국내산 인삼 복령 지황  에이치앤지바이오'</li><li>'동우당제약 궁중대보 250g 국내산 인삼 복령 지황  인투'</li></ul>                                                       |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.7147 |
+
+## 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_fd1")
+# Run inference
+preds = model("락토핏 당케어 2g x 60포  (주)레놈 성수지점")
+```
+
+<!--
+### 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   | 4   | 9.8446 | 23  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 50                    |
+| 3.0   | 50                    |
+| 4.0   | 50                    |
+| 5.0   | 31                    |
+| 6.0   | 50                    |
+| 7.0   | 50                    |
+| 8.0   | 24                    |
+| 9.0   | 50                    |
+| 10.0  | 50                    |
+| 11.0  | 50                    |
+| 12.0  | 24                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0110  | 1    | 0.3794        | -               |
+| 0.5495  | 50   | 0.2808        | -               |
+| 1.0989  | 100  | 0.1721        | -               |
+| 1.6484  | 150  | 0.0976        | -               |
+| 2.1978  | 200  | 0.0646        | -               |
+| 2.7473  | 250  | 0.0528        | -               |
+| 3.2967  | 300  | 0.0428        | -               |
+| 3.8462  | 350  | 0.0128        | -               |
+| 4.3956  | 400  | 0.0079        | -               |
+| 4.9451  | 450  | 0.01          | -               |
+| 5.4945  | 500  | 0.0115        | -               |
+| 6.0440  | 550  | 0.0002        | -               |
+| 6.5934  | 600  | 0.0001        | -               |
+| 7.1429  | 650  | 0.0001        | -               |
+| 7.6923  | 700  | 0.0001        | -               |
+| 8.2418  | 750  | 0.0001        | -               |
+| 8.7912  | 800  | 0.0001        | -               |
+| 9.3407  | 850  | 0.0001        | -               |
+| 9.8901  | 900  | 0.0001        | -               |
+| 10.4396 | 950  | 0.0001        | -               |
+| 10.9890 | 1000 | 0.0001        | -               |
+| 11.5385 | 1050 | 0.0001        | -               |
+| 12.0879 | 1100 | 0.0001        | -               |
+| 12.6374 | 1150 | 0.0001        | -               |
+| 13.1868 | 1200 | 0.0001        | -               |
+| 13.7363 | 1250 | 0.0001        | -               |
+| 14.2857 | 1300 | 0.0001        | -               |
+| 14.8352 | 1350 | 0.0           | -               |
+| 15.3846 | 1400 | 0.0001        | -               |
+| 15.9341 | 1450 | 0.0           | -               |
+| 16.4835 | 1500 | 0.0001        | -               |
+| 17.0330 | 1550 | 0.0           | -               |
+| 17.5824 | 1600 | 0.0           | -               |
+| 18.1319 | 1650 | 0.0           | -               |
+| 18.6813 | 1700 | 0.0           | -               |
+| 19.2308 | 1750 | 0.0           | -               |
+| 19.7802 | 1800 | 0.0001        | -               |
+
+### 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_fd",
+  "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

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+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ec3a20e8556b899d3212ceaa374abeec16ceadb0bf8f259c2378e5f988c3bfa4
+size 442494816

model_head.pkl

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+oid sha256:ea1b41602f3003eb2443cad9967d590707d7e467972be26951da8201dbf57bf5
+size 80895

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

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+{
+  "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

@@ -0,0 +1,66 @@
+{
+  "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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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "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]"
+}