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 +1,237 @@
+---
+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: '[본죽]5첩반상 5종(진미채+멸치+연근+콩자반+깻잎) 5팩+5팩 외 밑반찬 5종 5팩+5팩 메가글로벌001'
+- text: 싸고 맛있고 영양까지 풍부한 110가지 우리집반찬/우리홈메이드푸드 도토리묵/양념 홈메이드 푸드
+- text: 샘표 쓱쓱싹싹밥도둑 반찬 9봉 골라담기 / 장조림 오징어채볶음 멸치볶음 2. 고추장 멸치볶음 3봉_4. 쇠고기 장조림 3봉_6. 돼지고기
+    장조림 3봉 샘표식품 주식회사
+- text: 본죽 쇠고기 장조림 170g x 4  마이엘(Maiel)
+- text: 국산 고추장멸치볶음 500g 조림 반찬 국산 오복채 1kg 사계절반찬
+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: 0.9101876675603218
+      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>'본죽 미니장조림 2박스 70gx5개입x2  셜크'</li><li>'[본죽]쇠고기 장조림 300g (냉장 소고기 반찬 점심 저녁 도시락 어린이 아기반찬)  순수본 주식회사'</li><li>'본죽 쇠고기 장조림 170g x 4 5. 비비고 육개장 500g x 5개 감성주머니'</li></ul>                               |
+| 1.0   | <ul><li>'일가집 일미 쫄깃 치자 단무지 1kg 두부 날치알 피클 일가집 일미 고추지 1kg 고추절임 고추장아찌 머치바잉'</li><li>'일가집 일미 쫄깃 치자 단무지 1kg 두부 날치알 피클 일가집 일미 깐마늘 1kg 양파 다진마늘 청양 머치바잉'</li><li>'참 맛좋은 하진 반달 단무지 2.5kg  농업회사법인 봉농주식회사'</li></ul>  |
+| 5.0   | <ul><li>'진 명이나물(실속형) 10kg 대용량 업소용 식당 반찬 장아찌 05 유림 명이나물 10kg (유) 협동맛사랑식품'</li><li>'단풍콩잎 500g 양념 장아찌 국내제조 콩잎김치 삭힌 국산 갈치속젓 500g 사계절반찬'</li><li>'군산 울외장아찌 2kg 나라즈케 나라스케 술지게미 2.무 장아찌 2kg 주식회사 백년부엌'</li></ul> |
+| 2.0   | <ul><li>'마늘쫑무침 4kg 대용량 식당 업소용 반찬 무침 장아찌  (유) 협동맛사랑식품'</li><li>'[서울,성남 ] 푸릇푸릇 시금치무침 300g [암사 우리집반찬]  주식회사 프레시멘토'</li><li>'[주문폭주] 농가살리기 30년 전통 통영할매 원조 생굴무침 330g 생굴무침 330g 1통 주식회사 청년농부들'</li></ul>         |
+| 8.0   | <ul><li>'일본식 반찬대용 츠쿠다니 김조림 180g  서울타임즈'</li><li>'오뚜기 고등어갈치조림양념120g  제이디(JD)'</li><li>'청우식품 이음식 스지사태조림 200g  푸드뱅크(주)'</li></ul>                                                                            |
+| 4.0   | <ul><li>'[종가집]종가집 오징어채볶음 60g  에스케이스토아주식회사'</li><li>'[반찬가게 찬장]신선한재료 당일제조 배송 고사리볶음 가정식 반찬 집밥 나물/무침/볶음 배달 밑반찬_건파래무침 주식회사 찬장에프에스대전'</li><li>'청정원 종가집 견과류 멸치볶음 60G  조은마켓'</li></ul>                            |
+| 7.0   | <ul><li>'종가집 옛맛 무말랭이 1kg x 2개  더빈(THE BIN)'</li><li>'반찬단지 마늘쫑무침 1kg 아삭 마늘장아찌 반찬거리  와이엘플래닛'</li><li>'가을무를 말려 쫄깃하고 달큰한 국산 무말랭이 1kg 1. 국산 무말랭이 1kg 주식회사 태극인 농업회사법인'</li></ul>                                |
+| 0.0   | <ul><li>'씨제이 비비고 오징어채 볶음 55g 아이스박스 포장 (주)씨티케이이비전코리아'</li><li>'매운 고추부각 튀각 30g 6봉 티각태각 속초 명품 특산물 김부각30g 6봉 엠앤엠컴퍼니'</li><li>'대구 반고개 무침회 똘똘이식당 납작만두 오징어 회무침 캠핑 밀키트 무침회세트(중)_보통맛 대구 똘똘이 무침회'</li></ul>         |
+| 3.0   | <ul><li>'미자언니네 밑반찬 하얀콩강정 120g 1팩 미자언니네  하얀콩강정 에센셜키친'</li><li>'[메인반찬 국 찌개 김치 세트] 건강한 반찬 이기는면역찬 메인반찬_계란말이 이기는면역찬(서초점)'</li><li>'[본죽] 밑반찬 5종 세트(진미채볶음 멸치볶음 깻잎무침 무말랭이 궁채절임)  메가글로벌001'</li></ul>              |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9102 |
+
+## 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_fd9")
+# Run inference
+preds = model("본죽 쇠고기 장조림 170g x 4  마이엘(Maiel)")
+```
+
+<!--
+### 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   | 10.1981 | 21  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 42                    |
+| 2.0   | 22                    |
+| 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0154  | 1    | 0.4845        | -               |
+| 0.7692  | 50   | 0.2975        | -               |
+| 1.5385  | 100  | 0.0992        | -               |
+| 2.3077  | 150  | 0.0418        | -               |
+| 3.0769  | 200  | 0.0246        | -               |
+| 3.8462  | 250  | 0.0358        | -               |
+| 4.6154  | 300  | 0.0185        | -               |
+| 5.3846  | 350  | 0.0123        | -               |
+| 6.1538  | 400  | 0.0121        | -               |
+| 6.9231  | 450  | 0.0008        | -               |
+| 7.6923  | 500  | 0.0003        | -               |
+| 8.4615  | 550  | 0.0002        | -               |
+| 9.2308  | 600  | 0.0001        | -               |
+| 10.0    | 650  | 0.0001        | -               |
+| 10.7692 | 700  | 0.0001        | -               |
+| 11.5385 | 750  | 0.0002        | -               |
+| 12.3077 | 800  | 0.0001        | -               |
+| 13.0769 | 850  | 0.0001        | -               |
+| 13.8462 | 900  | 0.0001        | -               |
+| 14.6154 | 950  | 0.0001        | -               |
+| 15.3846 | 1000 | 0.0001        | -               |
+| 16.1538 | 1050 | 0.0001        | -               |
+| 16.9231 | 1100 | 0.0001        | -               |
+| 17.6923 | 1150 | 0.0001        | -               |
+| 18.4615 | 1200 | 0.0001        | -               |
+| 19.2308 | 1250 | 0.0001        | -               |
+| 20.0    | 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"
+  ],
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+  "bos_token_id": 0,
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+  "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
+}

model.safetensors

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model_head.pkl

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modules.json

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+    "name": "0",
+    "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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+    "lstrip": false,
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tokenizer_config.json

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+  "truncation_strategy": "longest_first",
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+}