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 +1,235 @@
+---
+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: '[CJ](신세계 의정부점) 비비고 누룽지닭다리삼계탕 550g  주식회사 에스에스지닷컴'
+- text: 고객 후기로 만들어진 밀푀유 쇼유 나베 밀키트 (2인) 2월27일(화) 주식회사 아내의쉐프
+- text: 룸서비스 홈파티음식 케이터링 생일팩15종 서울출장뷔페 집들이 배달 돌잔치상 손님초대요리 3.룸서비스파티팩15종(고급박스용기)_6월_19일
+    주식회사 룸서비스딜리버리
+- text: 홈파티음식 케이터링 생일팩15종 인천출장뷔페 집들이 배달 돌잔치상 소규모 손님초대요리 01.룸서비스 생일팩 15종_1월_20일 (주)셀루체
+- text: 홈파티음식 케이터링 생일팩15종 인천출장뷔페 집들이 배달 돌잔치상 소규모 손님초대요리 3.룸서비스파티팩15종(고급박스용기)_4월_19일
+    (주)셀루체
+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: 0.9173203883495146
+      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:** 8 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>'듬뿍담은 안동식 순살 찜닭 밀키트 711g  주식회사 프레시지'</li><li>'우렁쌈장 (2인분) 밀키트 쿠킹박스 우렁살 2개 추가(100g) 농업회사법인 주식회사 아임셰프'</li><li>'홍수계 매콤 당면듬뿍 순살 찜닭 850g 2인분 냉동 밀키트  셀린'</li></ul>                                                       |
+| 1.0   | <ul><li>'[마이셰프] 찹스테이크(1인)(프리미엄박스)  주식회사 마이셰프'</li><li>'소문난 청정원 호밍스 마포식 돼지양념구이 210g 정원이샵 홈파티음식 캠핑요리 맥주안주 야식 간편식 홈캉스 풍미업 모에모에큥  에스더블유디자인'</li><li>'심쿡 슈페리어 연어 스테이크 455g 밀키트 쿠킹박스  인영이네'</li></ul>                             |
+| 5.0   | <ul><li>'[골든벨통상](신세계센텀점)골든벨 심영순쇠고기국간장250ml  주식회사 에스에스지닷컴'</li><li>'[CJ](신세계센텀점) 튀김가루 1kg 1개  주식회사 에스에스지닷컴'</li><li>'(치즈박스)쉐프가 만든 캠핑 와인안주세트(고기 포함 안됨 X) 캘리포니아 키친 실속형(-2500)_11/20 월요일 캘리포니아키친(california kitchen)'</li></ul> |
+| 4.0   | <ul><li>'소고기 버섯 잡채 (2인분)  주식회사 프레시지'</li><li>'야식메뉴 청정원 호밍스 춘천식 치즈닭갈비 220g 저녁반찬 자취요리  규비에스코퍼레이션'</li><li>'하림 궁중 국물 닭떡볶이 700g 밀키트  바이라이프'</li></ul>                                                                           |
+| 0.0   | <ul><li>'올바르고반듯한 떡볶이 원조시장 떡볶이 (냉동), 575g, 1개  하누코지'</li><li>'두끼 즉석떡볶이 560G 아이스박스 포장/선택 인터드림'</li><li>'두끼 매콤 고소 로제떡볶이 3팩 450g  주식회사 다른'</li></ul>                                                                            |
+| 3.0   | <ul><li>'[강원팜] 홈스랑 곤드레감자밥 쉽게만들기6인분  강원팜'</li><li>'마이셰프 즉석밥 일상정원 명란 솥밥 (냉동), 233g, 1개  하누코지'</li><li>'여름철 보양식 전복죽 200g 1팩  더블제이doubleJ'</li></ul>                                                                            |
+| 7.0   | <ul><li>'우정옥 여주 한우 특곰탕 1kg(2인분) 한우사골곰탕 도가니탕 1000g(약 2인분) 주식회사 우정옥'</li><li>'25년 전통 수복 얼큰 감자탕 [기본팩] 캠핑요리 밀키트 우거지 리얼감자탕 알뜰팩(라면사리X / 야채X) 수복얼큰감자탕'</li><li>'인천 정통 맛집 장금수 스페셜 부대전골 부대찌개 2-3인분 술안주 캠핑 집들이 밀키트  더렌'</li></ul>     |
+| 2.0   | <ul><li>'1분완성 개별포장 매콤 알싸 비빔 막국수 막국수 1팩 (주)데이지웰푸드'</li><li>'동원 면발의신 얼큰칼국수 268g 엄마손맛 육수 쉬운요리 감칠맛 자취 풍미 레시피 소스  인영'</li><li>'샐러드미인 쉐프엠 미트파스타 230g  주식회사 엠디에스코리아'</li></ul>                                                     |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9173 |
+
+## 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_fd8")
+# Run inference
+preds = model("[CJ](신세계 의정부점) 비비고 누룽지닭다리삼계탕 550g  주식회사 에스에스지닷컴")
+```
+
+<!--
+### 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   | 9.3575 | 20  |
+
+| 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                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0159  | 1    | 0.4347        | -               |
+| 0.7937  | 50   | 0.2865        | -               |
+| 1.5873  | 100  | 0.0903        | -               |
+| 2.3810  | 150  | 0.0636        | -               |
+| 3.1746  | 200  | 0.0401        | -               |
+| 3.9683  | 250  | 0.003         | -               |
+| 4.7619  | 300  | 0.0016        | -               |
+| 5.5556  | 350  | 0.0017        | -               |
+| 6.3492  | 400  | 0.0025        | -               |
+| 7.1429  | 450  | 0.0007        | -               |
+| 7.9365  | 500  | 0.0001        | -               |
+| 8.7302  | 550  | 0.0001        | -               |
+| 9.5238  | 600  | 0.0002        | -               |
+| 10.3175 | 650  | 0.0001        | -               |
+| 11.1111 | 700  | 0.0008        | -               |
+| 11.9048 | 750  | 0.0001        | -               |
+| 12.6984 | 800  | 0.0001        | -               |
+| 13.4921 | 850  | 0.0           | -               |
+| 14.2857 | 900  | 0.0001        | -               |
+| 15.0794 | 950  | 0.0           | -               |
+| 15.8730 | 1000 | 0.0           | -               |
+| 16.6667 | 1050 | 0.0           | -               |
+| 17.4603 | 1100 | 0.0           | -               |
+| 18.2540 | 1150 | 0.0           | -               |
+| 19.0476 | 1200 | 0.0           | -               |
+| 19.8413 | 1250 | 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

@@ -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

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

model.safetensors

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+size 442494816

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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+    "lstrip": false,
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+    "single_word": false
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+}

tokenizer_config.json

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