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

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+---
+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: 동원 덴마크 구워먹는 치즈 후라이드 갈릭 125g x 3개  007스테이지스
+- text: 앵커크림치즈 1박스 (1kg x 12개) 앵커크림치즈 1박스 (1kg x 12개) (주)비오비
+- text: 홉라 무가당 휘핑크림 1L 2개세트  마켓이
+- text: 매일 상하치즈 리코타치즈 200g 2개 냉장배송  대명유통
+- text: 홉라 생크림 무가당 1L 휘핑크림 베이킹 쿠킹크림 1000ml 홉라 생크림 무가당 1L + 아이스박스 (주)비오비
+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: 0.9220726783310902
+      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:** 6 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                                                                                                                                                                                                                  |
+|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 5.0   | <ul><li>'[아이스박스무료] 선인 DB 휘핑크림 1L 무가당 혼합 생크림  재이F&B'</li><li>'이탈리아 홉라 식물성 생크림 500ml 6개 무가당 홉라 홈라 크림  알이즈웰'</li><li>'카파 포모나 휘핑 스프레이 500g 와플 크림 딸기향/휘핑 스프레이 500g 디씨즈(This is)'</li></ul>                                     |
+| 1.0   | <ul><li>'오뚜기 버터후레시 48개(아이스박스)/일회용버터 오뚜기 딸기잼 디스펜팩 40개+메이플시럽 40 박길용'</li><li>'버터린 롯데 450g 마늘향오일 갈릭버터오일  (주)인벨'</li><li>'[본사직송] 라꽁비에뜨 가염 무염 꽃소금 버터 450g (15g x 30개) 11/15(수)배송예정_라꽁비에뜨-가염 450g (30개입) 인에이블 코리아(주)'</li></ul> |
+| 3.0   | <ul><li>'인도네시아 인도 밀크 스위트드컨덴센 팩 545g 연유  동원무역(이마트24 감천네거리점)'</li><li>'매일연유 5kg 대용량 x 2개 연유  카페스토리(CAFE STORY)'</li><li>'누티 크리머 스위텐드 연유 시럽 385g x 8개  클루'</li></ul>                                                         |
+| 0.0   | <ul><li>'오뚜기 파운드 마아가린(벌크) 9kg  피치피치몰'</li><li>'오뚜기 쿠키 옥수수마가린 200Gx2 1세트 제과 제빵 토스트  방글방글마켓'</li><li>'Whirl Admiration Pro Fry 액체 쇼트닝 튀김용 3.6kg 8파운드  포커스라이프'</li></ul>                                                     |
+| 2.0   | <ul><li>'밀락골드 1L 제품수량선택 에스제이푸드(SJ FOOD)'</li><li>'[구매전 긴급공지 필독]1217. 뉴골드라벨 - 한박스(1030g x 12개)  베이킹도전'</li><li>'밀락골드 1L 아이스박스필수구매 에스제이푸드(SJ FOOD)'</li></ul>                                                               |
+| 4.0   | <ul><li>'상하 샐러드용 슈레드치즈 210g X 1개 종이박스포장 오하'</li><li>'끼리 크림���즈 스프레드 플레인 x 4개 베이글 발라 먹는 치즈 토스트 끼리 크림치즈 스프레드 플레인 4개 더팜'</li><li>'데르뜨 롤케이크 선물세트 소잘우유 초코크림 380g 1개 우유크림 360g 냉동  오씨홀딩스'</li></ul>                               |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9221 |
+
+## 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_fd13")
+# Run inference
+preds = model("홉라 무가당 휘핑크림 1L 2개세트  마켓이")
+```
+
+<!--
+### 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   | 9.5067 | 18  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 50                    |
+| 3.0   | 50                    |
+| 4.0   | 50                    |
+| 5.0   | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0213  | 1    | 0.4249        | -               |
+| 1.0638  | 50   | 0.2783        | -               |
+| 2.1277  | 100  | 0.0747        | -               |
+| 3.1915  | 150  | 0.0734        | -               |
+| 4.2553  | 200  | 0.0368        | -               |
+| 5.3191  | 250  | 0.0373        | -               |
+| 6.3830  | 300  | 0.0003        | -               |
+| 7.4468  | 350  | 0.0001        | -               |
+| 8.5106  | 400  | 0.0001        | -               |
+| 9.5745  | 450  | 0.0001        | -               |
+| 10.6383 | 500  | 0.0           | -               |
+| 11.7021 | 550  | 0.0           | -               |
+| 12.7660 | 600  | 0.0           | -               |
+| 13.8298 | 650  | 0.0           | -               |
+| 14.8936 | 700  | 0.0           | -               |
+| 15.9574 | 750  | 0.0           | -               |
+| 17.0213 | 800  | 0.0           | -               |
+| 18.0851 | 850  | 0.0           | -               |
+| 19.1489 | 900  | 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_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",
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+  "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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+    "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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tokenizer_config.json

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+}