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: 지푸드박스 제이엔제이트레이드 코코엘 유기농 엑스트라버진 코코넛오일 필리핀산 415ml  헬시푸드몰
+- text: CJ 백설 2통 이상 구매시 할인 쿠폰 콩기름 식용유 대두유 18L 이츠웰 해표 오뚜기 롯데 식용유 말통 전국 최저가판매 식용유_오뚜기식용유
+    주식회사 황금알에프앤오
+- text: 올리타리아 엑스트라버진 올리브오일 1L  카비스
+- text: 커클랜드 시그니춰 카놀라유 오일 2.83L 커클랜드 카놀라유2.83L 베이비파크
+- text: 해표)고추맛기름 1.8L  에스엠(SM)식자재도매센터
+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: 0.9926900584795322
+      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>'사조해표 해표 고급유 2호 선물세트  풀문'</li><li>'CJ 백설 프리미엄 23호  형제종합물류'</li><li>'노브랜드 카놀라유 1L 노브랜드 카놀라유2L 주식회사 유일글로벌'</li></ul>                                                                             |
+| 3.0   | <ul><li>'오타비오 아보카도오일 2L 이탈리아 코스트코  포시즌'</li><li>'건강한오늘 아보카도오일 500ml 건강한오늘 아보카도오일 500ml 잇츠설렘'</li><li>'아보퍼시픽 아보카도오일 1L 코스트코 1021670  굿데이'</li></ul>                                                     |
+| 4.0   | <ul><li>'만능 올리브유 900ml 청정원 가을 식재료 추석 설날 제사 드레싱 샐러드 파스타  모두감동해'</li><li>'CJ제일제당 백설 압착 올리브유 900ml  준스토리'</li><li>'오로바일렌 엑스트라버진 올리브오일 아르베키나 500ml 500ml (주)운우'</li></ul>                                  |
+| 7.0   | <ul><li>'사조 해표 포도씨유 250ML  주식회사 킴벌리마스타'</li><li>'오뚜기 프레스코 포도씨유 900ml  주식회사 삼부'</li><li>'대상 청정원 포도씨유 900ml  주식회사 당장만나'</li></ul>                                                                        |
+| 2.0   | <ul><li>'국산 저온압착 들기름 300ml 국내산 아기들기름 저온압착 저온들기름 300ml 농부창고 영농조합법인'</li><li>'미식상회 생들기름 대용량 350ml  에프유니마켓'</li><li>'오뚜기 향긋한 들기름 160ml 1개  (주)하우'</li></ul>                                               |
+| 1.0   | <ul><li>'대용량 업소용 식용유 해표 콩 식용유 18L 선택04)오뚜기 콩 식용유 18L 소유앳홈(SO:YOU@Home)'</li><li>'CJ 백설 식용유 1.8L 해표 식용유 1.8L 주식��사 경일종합식품 케이마트몰'</li><li>'CJ 해피스푼 콩식용유 18L 업소용 대용량 저가 식용유 광주 말통  주식회사 케이제이플러스'</li></ul> |
+| 0.0   | <ul><li>'캘리포니아골드뉴트리션 슈퍼푸드 오가닉 엑스트라 버진 코코넛 오일 473ml 액상 코코넛기름  에스지샵(SGshop)'</li><li>'참미정 파기름 1.8L 대파 맛기름 참미정 마늘기름 1.8L 주식회사 팜'</li><li>'시아스 불맛기름 화유 500ml 시아스 불맛 고추기름 500ml (주) 식자재민족'</li></ul>        |
+| 5.0   | <ul><li>'50년전통 대현상회 저온압착 참기름 350ml 돌려따는 BIG 아빠의주스 양배추사과즙 180 네오카트'</li><li>'오뚜기 고소한 참기름 450ml 오뚜기 고소한 참기름 320ml(병) 삼영유통'</li><li>'국산 저온압착 참기름 180ml 선물세트 이삭방앗간 당일착유 국산 저온압착 참기름_250ml 이삭방앗간'</li></ul> |
+| 8.0   | <ul><li>'백설 해바라기씨유 500ml 당일 출발  (주) 바쿰'</li><li>'사조해표 해바라기유 500ml 1개  (주)해피상사'</li><li>'사조 해표 해바라기유 500ml (유통기한 24.01까지) ★유통기한임박특가(24년1월까지) 주식회사 킴벌리마스타'</li></ul>                                     |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9927 |
+
+## 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_fd12")
+# Run inference
+preds = model("올리타리아 엑스트라버진 올리브오일 1L  카비스")
+```
+
+<!--
+### 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   | 8.5356 | 22  |
+
+| 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                    |
+| 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0141  | 1    | 0.4844        | -               |
+| 0.7042  | 50   | 0.3408        | -               |
+| 1.4085  | 100  | 0.0769        | -               |
+| 2.1127  | 150  | 0.0298        | -               |
+| 2.8169  | 200  | 0.023         | -               |
+| 3.5211  | 250  | 0.0251        | -               |
+| 4.2254  | 300  | 0.0291        | -               |
+| 4.9296  | 350  | 0.0156        | -               |
+| 5.6338  | 400  | 0.0137        | -               |
+| 6.3380  | 450  | 0.0029        | -               |
+| 7.0423  | 500  | 0.0001        | -               |
+| 7.7465  | 550  | 0.0001        | -               |
+| 8.4507  | 600  | 0.0001        | -               |
+| 9.1549  | 650  | 0.0           | -               |
+| 9.8592  | 700  | 0.0           | -               |
+| 10.5634 | 750  | 0.0           | -               |
+| 11.2676 | 800  | 0.0           | -               |
+| 11.9718 | 850  | 0.0           | -               |
+| 12.6761 | 900  | 0.0           | -               |
+| 13.3803 | 950  | 0.0           | -               |
+| 14.0845 | 1000 | 0.0           | -               |
+| 14.7887 | 1050 | 0.0           | -               |
+| 15.4930 | 1100 | 0.0           | -               |
+| 16.1972 | 1150 | 0.0           | -               |
+| 16.9014 | 1200 | 0.0           | -               |
+| 17.6056 | 1250 | 0.0           | -               |
+| 18.3099 | 1300 | 0.0           | -               |
+| 19.0141 | 1350 | 0.0           | -               |
+| 19.7183 | 1400 | 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,
+  "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
+}

model.safetensors

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model_head.pkl

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modules.json

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+    "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,
+    "normalized": false,
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+    "single_word": false
+  }
+}

tokenizer_config.json

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+  "truncation_strategy": "longest_first",
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+}