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 +1,252 @@
+---
+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: 동성 순후추 1KG  주식회사 청춘에프앤비
+- text: 오뚜기 2배사과식초 1.8L  (주) 식자재민족
+- text: 무화당 알룰로스 분말 250g  (주)닥터다이어리
+- text: 마이노멀 알룰로스 485g  메인루트
+- text: 오뚜기 순후추 캔 100g  주식회사 두위드(Do With)
+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: 0.9504337050805453
+      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:** 12 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                                                                                                                                                                                                                             |
+|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 10.0  | <ul><li>'설탕대신 스테비아 1.2kg  주식회사 더 골든트리'</li><li>'자연지애 에리스리톨 1:1 눈꽃 스테비아 1kg 설탕대체 당뇨설탕 당류 제로 2. 1kg x 2개 주식회사 닥터스랩'</li><li>'바이오믹스 설탕대신 자일리톨 180g  주식회사 와식자재마트(민락지점)'</li></ul>                                                        |
+| 8.0   | <ul><li>'커클랜드 발사믹 식초 1L 코스트코 ▶▶▶전국 초완벽 뽁뽁이 택배◀◀◀ 브라이튼'</li><li>'롯데 미림 18L 말통 (맛술, 요리용 요리주)  와사비푸드'</li><li>'롯데 미림 1.8L 맛술 요리용 요리주 1개  블레스(Bless)'</li></ul>                                                                          |
+| 5.0   | <ul><li>'정경아 생강 조청 550g 무설탕 생강청 차 즙 엿 속쓰림 수제조청 엿기름 쌀조청 답례품 2. 스틱형 생강조청 33개 (1만원 할인) 정드림'</li><li>'CJ 백설 요리당 2.45kg 조림 무침 구이  에스비푸드시스템'</li><li>'오뚜기 옛날 물엿 5kg  솔브이트코리아'</li></ul>                                                    |
+| 0.0   | <ul><li>'태산식품 일회용 맛미 겨자소스 3g 200개 미니간장200개입 다온'</li><li>'오뚜기 오쉐프 연겨자 480g 튜브  주식회사 두위드(Do With)'</li><li>'오뚜기 오쉐프 연겨자 480g  주식회사 데일즈'</li></ul>                                                                                      |
+| 6.0   | <ul><li>'［DA85］큐원 하얀설탕(실온 3Kg)  기화유통'</li><li>'CJ제일제당 백설 브라운 자일로스설탕 5kg  오늘의 컨셉'</li><li>'CJ 백설 하얀설탕 1kg 매실 대용량 청 제빵용  에스비푸드시스템'</li></ul>                                                                                           |
+| 11.0  | <ul><li>'흑후추가루(서원 200g)/강황가루/후추1kg/가루쌀빵/햇고추가루/후추그라인더/후추가루1KG/tnsgncn/todnrkfn  (주)큐원상사'</li><li>'오뚜기 직접갈아먹는 통후추(리필용) 소스 조미료 고기 삼겹살 목살 통후추 스테이크 35G 1세트  청주그릇주방설비'</li><li>'청정원 향신료 잡내제거 천연 순후추 100g 육류요리 생선요리 알싸한풍미  지니마켓'</li></ul> |
+| 2.0   | <ul><li>'경상북도 영양 명가 고추가루 매운맛 1kg (2023년산) -인증  시안무역'</li><li>'델라미코 크러쉬드 레드페퍼 크러쉬드 레드페퍼 370g 두두유통'</li><li>'청정식품 23년 국산 고운 햇 고춧가루 1kg CJA001-99_(청양)고운 고추가루 1kg 유한킴벌리 에스와이'</li></ul>                                                 |
+| 9.0   | <ul><li>'한라식품 프리미엄참치액500ml 11203420 프리미엄참치액 세론세론'</li><li>'CJ제일제당 백설 참치액 진 더 풍부한 맛 900g  둘레푸드'</li><li>'티파로스 피쉬소스 700ml (태국 멸치액젓 남쁠라 느억맘소스)  팝스이엔티'</li></ul>                                                                        |
+| 1.0   | <ul><li>'움트리 705 고추냉이 700g 청비 알맹이 생고추냉이 700g 주식회사 팜'</li><li>'청비 생고추냉이 700g 생와사비 와사비 청비 뿌리갈은 생고추냉이 700g 주식회사 팜'</li><li>'삼광999 생와사비 750g  제루통상'</li></ul>                                                                            |
+| 4.0   | <ul><li>'[나가타니엔] 오토나노 후리카케 미니 2종  컬리'</li><li>'일본 후리카케 밥 주먹밥 혼가쓰오 나가타니엔 일본 오차즈케가루  매크로온'</li><li>'마루미야 노리타마 후리카케 28g 오차즈케  1초재팬'</li></ul>                                                                                           |
+| 3.0   | <ul><li>'코스트코 맥코믹 몬트리얼 스테이크 시즈닝 822g 1개  주식회사베이비또'</li><li>'샘표 연두 요리에센스 순 860ml  달달구리'</li><li>'해통령 육수한알 진한맛 25입 100g 트레이더스  스마일유통'</li></ul>                                                                                        |
+| 7.0   | <ul><li>'CJ제일제당 백설 허브맛 솔트 매콤한맛 50g 허브솔트매콤한맛 화진유통'</li><li>'백설 허브맛솔트시즈닝 매콤한맛 50g  주식회사 팩앤폴스'</li><li>'[백설]오천년의 신비 명품 천일염 (가는 입자) 250g (영등포점)  주식회사 에스에스지닷컴'</li></ul>                                                                 |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9504 |
+
+## 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_fd18")
+# Run inference
+preds = model("마이노멀 알룰로스 485g  메인루트")
+```
+
+<!--
+### 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   | 9.1646 | 29  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 50                    |
+| 3.0   | 50                    |
+| 4.0   | 21                    |
+| 5.0   | 50                    |
+| 6.0   | 50                    |
+| 7.0   | 50                    |
+| 8.0   | 50                    |
+| 9.0   | 50                    |
+| 10.0  | 50                    |
+| 11.0  | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0111  | 1    | 0.4135        | -               |
+| 0.5556  | 50   | 0.3821        | -               |
+| 1.1111  | 100  | 0.0967        | -               |
+| 1.6667  | 150  | 0.0493        | -               |
+| 2.2222  | 200  | 0.0399        | -               |
+| 2.7778  | 250  | 0.0149        | -               |
+| 3.3333  | 300  | 0.0107        | -               |
+| 3.8889  | 350  | 0.01          | -               |
+| 4.4444  | 400  | 0.0116        | -               |
+| 5.0     | 450  | 0.0078        | -               |
+| 5.5556  | 500  | 0.0001        | -               |
+| 6.1111  | 550  | 0.0001        | -               |
+| 6.6667  | 600  | 0.0001        | -               |
+| 7.2222  | 650  | 0.0001        | -               |
+| 7.7778  | 700  | 0.0001        | -               |
+| 8.3333  | 750  | 0.0001        | -               |
+| 8.8889  | 800  | 0.0001        | -               |
+| 9.4444  | 850  | 0.0001        | -               |
+| 10.0    | 900  | 0.0001        | -               |
+| 10.5556 | 950  | 0.0           | -               |
+| 11.1111 | 1000 | 0.0           | -               |
+| 11.6667 | 1050 | 0.0           | -               |
+| 12.2222 | 1100 | 0.0           | -               |
+| 12.7778 | 1150 | 0.0           | -               |
+| 13.3333 | 1200 | 0.0           | -               |
+| 13.8889 | 1250 | 0.0           | -               |
+| 14.4444 | 1300 | 0.0           | -               |
+| 15.0    | 1350 | 0.0           | -               |
+| 15.5556 | 1400 | 0.0           | -               |
+| 16.1111 | 1450 | 0.0           | -               |
+| 16.6667 | 1500 | 0.0           | -               |
+| 17.2222 | 1550 | 0.0           | -               |
+| 17.7778 | 1600 | 0.0           | -               |
+| 18.3333 | 1650 | 0.0           | -               |
+| 18.8889 | 1700 | 0.0           | -               |
+| 19.4444 | 1750 | 0.0           | -               |
+| 20.0    | 1800 | 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

@@ -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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+oid sha256:904f39480368207f44381eb6424fc030fb3f5e8602754df076136e8258c0ff2a
+size 442494816

model_head.pkl

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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,
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+    "single_word": false
+  },
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+    "lstrip": false,
+    "normalized": false,
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+    "single_word": false
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+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+  "added_tokens_decoder": {
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+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
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+  "cls_token": "[CLS]",
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+  "do_lower_case": false,
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+  "padding_side": "right",
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}