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 +1,236 @@
+---
+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: 한끼스토리 딸기드레싱 500g 10개  한울마켓
+- text: 맷돌표 뉴슈가 60g/ 20개  (주)디엔제이
+- text: 하회마을 쌈장 14kg 업소용 대용량  물레푸드
+- text: 화가장가평발효과학 국산콩청국장 120g16팩  화가장 주식회사
+- text: 춘장(삼화 300g) 4개 식자재 업소용 대용량  더착한컴퍼니
+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: 0.8727670433831571
+      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                                                                                                                                                                                                                          |
+|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 8.0   | <ul><li>'아이스티음료 복숭아음료 립톤 음료 베이스 가루 업소 대용량 907g  온달이'</li><li>'죽순캔(진양 400g)X4 진양 400g)X4 프렌들리 컴퍼니'</li><li>'커피믹스(맥심 2.04k) X6 모카골드  오구오구(5959)'</li></ul>                                                                          |
+| 0.0   | <ul><li>'청정원 장아찌 간장소스 1.7L  착한사람들'</li><li>'몽고 송표 골드 간장 1.5L  몽고식품(주)창원1공장'</li><li>'샘표 맛간장 조림볶음용  보니따엠'</li></ul>                                                                                                                |
+| 3.0   | <ul><li>'으뜸 낫또 제주콩 생나또 53 g 특허기술로 만든 생 청국장 실이많은 생낫또 24 팩 혼합구성_생낫또 18 개 + 하나또 18 개 (주)으뜸엘엔에스'</li><li>'국산콩100g 12개 일본장인전수 수제 가정생낫또 나또 사또 검정콩 쥐눈이 대용량 검정콩100g 10개_간장360ml 가정생청국장'</li><li>'청정 제주콩 생 낫또 36개  주식회사 네오넥스글로벌'</li></ul> |
+| 4.0   | <ul><li>'CJ 해찬들 그대로 된장찌개양념 450gx3 고깃집 된장찌개용 차돌 조개 코스트코 1021460 4 바지락꽃게 3개 까까아일랜드'</li><li>'[2+1] 해찬들 물로만 끓여도 차돌 된장찌개 양념 450G 물로만 끓여도 차돌 된장찌개 450Gx3 메가글로벌001'</li><li>'[샘표]샘표 토장 900g  티디티유통'</li></ul>                           |
+| 1.0   | <ul><li>'안동제비원 고추장 담그기 세트 (약7kg)[33628066]  (주)엔에스쇼핑'</li><li>'CJ 해찬들 태양초 알찬 고추장 6.5kg  리브웨이'</li><li>'청정원순창 현미 태양초 찰고추장, 2kg, 1개 2kg × 1개 2kg x 1개 카리스광클'</li></ul>                                                              |
+| 6.0   | <ul><li>'샘표 쌈토장 450g  대성상사'</li><li>'참고을 신선한 쌈장 14kg 맛있는 쌈장 대용량 업소용 쌈장 지함 순창궁 양념 쌈장 14kg 우성수산'</li><li>'청정원 순창 쌈장골드 4.8kg  주식회사 푸드공공칠'</li></ul>                                                                                  |
+| 5.0   | <ul><li>'콩마실 국산 메주 가루 (1kg 국산콩100%, 고추장용)  콩마실'</li><li>'고령 국산 메주 전통 국산콩메주 세트 5kg  장현식품'</li><li>'100%국산콩으로 만든 순창 전통메주 1덩이 1.2kg내외  열정농부'</li></ul>                                                                               |
+| 7.0   | <ul><li>'고추명가 비빔냉면 소스 2kg 냉면 양념장 비냉 비빔장 국수 양념 다대기 식당업소용 대용량  이도'</li><li>'CJ 손맛 다담 안동찜닭 양념 220g 분식 식당 식자재 감칠맛 풍미 맛다시  제이지무역'</li><li>'연안식당 부추꼬막장 150g 앙념 비빔장 꼬막비빔밥 밥도둑 꼬막장 넉넉한 2인분  주식회사 디딤'</li></ul>                            |
+| 2.0   | <ul><li>'오뚜기 가쓰오부시 장국 360ml 외 7종 01_가쓰오부시 장국 360ml 주식회사 삼부'</li><li>'면사랑 프리미엄 메밀장국 1.8L 모밀 소바 육수 장국 국수 찌개 만능 다시  문화벙커'</li><li>'뽕보감 조청 1000g  철원군농업기술센터'</li></ul>                                                                |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.8728 |
+
+## 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_fd15")
+# Run inference
+preds = model("맷돌표 뉴슈가 60g/ 20개  (주)디엔제이")
+```
+
+<!--
+### 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   | 9.8578 | 26  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 50                    |
+| 3.0   | 50                    |
+| 4.0   | 50                    |
+| 5.0   | 22                    |
+| 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0152  | 1    | 0.3728        | -               |
+| 0.7576  | 50   | 0.2769        | -               |
+| 1.5152  | 100  | 0.1245        | -               |
+| 2.2727  | 150  | 0.0532        | -               |
+| 3.0303  | 200  | 0.0532        | -               |
+| 3.7879  | 250  | 0.0385        | -               |
+| 4.5455  | 300  | 0.0052        | -               |
+| 5.3030  | 350  | 0.0025        | -               |
+| 6.0606  | 400  | 0.0004        | -               |
+| 6.8182  | 450  | 0.0004        | -               |
+| 7.5758  | 500  | 0.0005        | -               |
+| 8.3333  | 550  | 0.0007        | -               |
+| 9.0909  | 600  | 0.0002        | -               |
+| 9.8485  | 650  | 0.0002        | -               |
+| 10.6061 | 700  | 0.0001        | -               |
+| 11.3636 | 750  | 0.0001        | -               |
+| 12.1212 | 800  | 0.0001        | -               |
+| 12.8788 | 850  | 0.0001        | -               |
+| 13.6364 | 900  | 0.0001        | -               |
+| 14.3939 | 950  | 0.0001        | -               |
+| 15.1515 | 1000 | 0.0001        | -               |
+| 15.9091 | 1050 | 0.0001        | -               |
+| 16.6667 | 1100 | 0.0001        | -               |
+| 17.4242 | 1150 | 0.0001        | -               |
+| 18.1818 | 1200 | 0.0001        | -               |
+| 18.9394 | 1250 | 0.0           | -               |
+| 19.6970 | 1300 | 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

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+{
+  "_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

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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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+{
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+  "normalize_embeddings": false
+}

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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+    "normalized": false,
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+}

tokenizer_config.json

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+}