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: '[저소음 미세입자] 오므론 네블라이저 NE-C803  꿈꾸는약국'
+- text: 일동제약 케어리브 밴드 M 중형 10매입 약국용 3_중형 M 50매 이웃사랑팜
+- text: 퀸사이즈 병원침대/환자용침대 매트리스/고탄성 병원용 접이식 마사지 지압 의료용 매트 두께 7cm_베이지색 평매트리스_1400mm X
+    2000mm(더블사이즈) 메디칼베드마트
+- text: 일동제약 케어리브 밴드 중형 M 50매입 하이맘(중외제약)_하이맘밴드 아쿠아 혼합형 12매 테크노 제일약국
+- text: '[하프클럽/제일케어]웰팜스 의료기기 - 의료용 가위 1개  하프클럽'
+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: 0.9570833333333333
+      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:** 5 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                                                                                                                                                                                                                       |
+|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 2.0   | <ul><li>'세운 네라톤카테타 #1116 라텍스 멸균 100개 팩 6번 12fr 4.0mm0  트리비즈니스'</li><li>'세운 바로박(Barovac) PS200C 단위:1개  (주)엠디오씨'</li><li>'의무실 성인용 고무밴드 네블라이저 마스크 호흡기 흡입마스크 기관지  인사이트쇼핑몰'</li></ul>                                               |
+| 1.0   | <ul><li>'JW중외제약 하이맘밴드 프리미엄 2매 이지덤(대웅제약)_이지덤씬 2매(+가위) 테크노 제일약국'</li><li>'메디폼 친수성 폼드레싱 10x10cm (5mm) (2mm) 10매입 1박스 5mm 주식회사 엠퍼러'</li><li>'메나리니 더마틱스 울트라 겔 15g 1개.  릴리뷰티'</li></ul>                                              |
+| 0.0   | <ul><li>'약국 에탄올스왑 일회용 알콜솜 에프에이 이올스왑 알콜스왑 소독솜 1박스  다팜메디'</li><li>'[유한양행] 해피홈 소독용 알콜스왑알콜솜 100매입 2개 [0001]기본상품 CJONSTYLE'</li><li>'일회용 알콜솜 알콜스왑 소독 약국 바른케어 개별포장100매 바른케어 플러스 알콜솜 100매 로그엠(LOGM)'</li></ul>                        |
+| 4.0   | <ul><li>'가주 비멸균 설압자 1통(100개) 혀누르개 목설압자 의료용 병원용 더블세이프 MinSellAmount 이원헬스케어'</li><li>'의료용 겸자 12.5cm /곡 모스키토 켈리 포셉  SJ헬스케어'</li><li>'개부밧드6절(뚜껑있는밧드)소독통/개무밧드/사각트레이/트레이밧드/거어즈캔  신동방메디칼'</li></ul>                                   |
+| 3.0   | <ul><li>'일회용 베드 위생시트 부직포시트 침대커버 1롤 50장 80x180cm 비방수(고급형) 80x180 50장/롤 심비오시스'</li><li>'부직포자루,육수보자기,다시백,거름망 45x50-300장 봉제  지우씨'</li><li>'병원침대/환자용침대 매트리스/고탄성 접이식 마사지 지압 의료용 매트 두께 9cm_밤색 평매트리스_900mm X 1900mm 메디칼베드마트'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9571 |
+
+## 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_lh19")
+# Run inference
+preds = model("[저소음 미세입자] 오므론 네블라이저 NE-C803  꿈꾸는약국")
+```
+
+<!--
+### 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   | 10.084 | 20  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 50                    |
+| 3.0   | 50                    |
+| 4.0   | 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 |
+|:-----:|:----:|:-------------:|:---------------:|
+| 0.025 | 1    | 0.4162        | -               |
+| 1.25  | 50   | 0.2435        | -               |
+| 2.5   | 100  | 0.0066        | -               |
+| 3.75  | 150  | 0.0054        | -               |
+| 5.0   | 200  | 0.0001        | -               |
+| 6.25  | 250  | 0.0           | -               |
+| 7.5   | 300  | 0.0           | -               |
+| 8.75  | 350  | 0.0           | -               |
+| 10.0  | 400  | 0.0           | -               |
+| 11.25 | 450  | 0.0           | -               |
+| 12.5  | 500  | 0.0           | -               |
+| 13.75 | 550  | 0.0           | -               |
+| 15.0  | 600  | 0.0           | -               |
+| 16.25 | 650  | 0.0           | -               |
+| 17.5  | 700  | 0.0           | -               |
+| 18.75 | 750  | 0.0           | -               |
+| 20.0  | 800  | 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_lh",
+  "architectures": [
+    "RobertaModel"
+  ],
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+  "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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+{
+  "normalize_embeddings": false,
+  "labels": null
+}

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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+}