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 +1,254 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 스프링 구조 고급형 농구링 농구골망 간편한운동 스포츠/레저>농구>농구대
+- text: 판 점수 배구 농구 전자 스포츠/레저>농구>기타농구용품
+- text: 낫소 농구공 믹스 매치 BMM 장기간 공기 보존 스포츠/레저>농구>농구공
+- text: 스타스포츠 농구 트레이닝 포지션 마커 세트 OFKNN1O2 스포츠/레저>농구>농구대
+- text: 소닉블라스트폭스40 휘슬와치 손목스톱워치세트폭스40 6906-0700 스포츠/레저>농구>기타농구용품
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: mini1013/master_domain
+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: accuracy
+      value: 1.0
+      name: Accuracy
+---
+
+# 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                                                                                                                                                                                                            |
+|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 4.0   | <ul><li>'NYS 365 긴팔 티셔츠 빅로고 농구유니폼 농구의류 슈팅셔츠 롱슬리브 상의 스포츠/레저>농구>농구의류'</li><li>'나이키 남성 맥스90 농구 티셔츠 FV8395-345 스포츠/레저>농구>농구의류'</li><li>'농구져지 나시 농구 반티 메쉬 시카고불스 농구복 유니폼 민소매 헬스 짐웨어 트레이닝 티셔츠 스포츠/레저>농구>농구의류'</li></ul>    |
+| 0.0   | <ul><li>'타요 이지훅 농구대 세트 스포츠/레저>농구>기타농구용품'</li><li>'먼지제거 더스터 슈 체육관신발 몰텐 농구장 보드판 AW5EA0E1 스포츠/레저>농구>기타농구용품'</li><li>'Kuangmi 카우아미 농구 6호 7호 스트리트볼 KMbb18 흰색 6호 스포츠/레저>농구>기타농구용품'</li></ul>                              |
+| 3.0   | <ul><li>'접이식 농구 게임 슈팅 골대 슛팅 연습 게임기 스포츠 스포츠/레저>농구>농구대'</li><li>'농구대 벽걸이 야외 연습 백보드 농구골대 체육관 스포츠/레저>농구>농구대'</li><li>'농구네트 이동식 거치대 트레이닝 패스 연습 기구 스포츠/레저>농구>농구대'</li></ul>                                               |
+| 2.0   | <ul><li>'농구 축구 풋살 공3개입 3볼백 스타 볼가방 중등부 스포츠/레저>농구>농구공가방'</li><li>'엄브로 백팩 이지 18L 에어팟 파우치 구성 풋살 블루 UP123CBP11 114856 스포츠/레저>농구>농구공가방'</li><li>'미카사 공가방 3개입 AC-BG230W 스포츠/레저>농구>농구공가방'</li></ul>                         |
+| 1.0   | <ul><li>'NBA NCAA 윌슨 농구공 한정판 DRV ENDURE PU 7호 스포츠/레저>농구>농구공'</li><li>'클래식 점보 농구공 스포츠/레저>농구>농구공'</li><li>'몰텐 농구공 7호 KBL 공인구 BG4000 스포츠/레저>농구>농구공'</li></ul>                                                          |
+| 5.0   | <ul><li>'조던 레거시 312 로우 파이어 Jordan Legacy Low Fire 547656 스포츠/레저>농구>농구화'</li><li>'JORDAN 조던 11 레트로 로우 시멘트 조단 11 Retro Low Cement 스포츠/레저>농구>농구화'</li><li>'아식스 젤 후프 V15 스탠다드 농구화 1063A063 100 스포츠/레저>농구>농구화'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 1.0      |
+
+## 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_sl5")
+# Run inference
+preds = model("판 점수 배구 농구 전자 스포츠/레저>농구>기타농구용품")
+```
+
+<!--
+### 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   | 9.1981 | 23  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 70                    |
+| 1.0   | 70                    |
+| 2.0   | 70                    |
+| 3.0   | 70                    |
+| 4.0   | 70                    |
+| 5.0   | 69                    |
+
+### Training Hyperparameters
+- batch_size: (256, 256)
+- num_epochs: (30, 30)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 50
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch   | Step | Training Loss | Validation Loss |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0122  | 1    | 0.5273        | -               |
+| 0.6098  | 50   | 0.4932        | -               |
+| 1.2195  | 100  | 0.2677        | -               |
+| 1.8293  | 150  | 0.0673        | -               |
+| 2.4390  | 200  | 0.0159        | -               |
+| 3.0488  | 250  | 0.0002        | -               |
+| 3.6585  | 300  | 0.0001        | -               |
+| 4.2683  | 350  | 0.0001        | -               |
+| 4.8780  | 400  | 0.0           | -               |
+| 5.4878  | 450  | 0.0           | -               |
+| 6.0976  | 500  | 0.0           | -               |
+| 6.7073  | 550  | 0.0           | -               |
+| 7.3171  | 600  | 0.0           | -               |
+| 7.9268  | 650  | 0.0           | -               |
+| 8.5366  | 700  | 0.0           | -               |
+| 9.1463  | 750  | 0.0           | -               |
+| 9.7561  | 800  | 0.0           | -               |
+| 10.3659 | 850  | 0.0           | -               |
+| 10.9756 | 900  | 0.0001        | -               |
+| 11.5854 | 950  | 0.0           | -               |
+| 12.1951 | 1000 | 0.0           | -               |
+| 12.8049 | 1050 | 0.0           | -               |
+| 13.4146 | 1100 | 0.0           | -               |
+| 14.0244 | 1150 | 0.0           | -               |
+| 14.6341 | 1200 | 0.0           | -               |
+| 15.2439 | 1250 | 0.0           | -               |
+| 15.8537 | 1300 | 0.0001        | -               |
+| 16.4634 | 1350 | 0.0           | -               |
+| 17.0732 | 1400 | 0.0           | -               |
+| 17.6829 | 1450 | 0.0           | -               |
+| 18.2927 | 1500 | 0.0           | -               |
+| 18.9024 | 1550 | 0.0           | -               |
+| 19.5122 | 1600 | 0.0           | -               |
+| 20.1220 | 1650 | 0.0           | -               |
+| 20.7317 | 1700 | 0.0           | -               |
+| 21.3415 | 1750 | 0.0           | -               |
+| 21.9512 | 1800 | 0.0           | -               |
+| 22.5610 | 1850 | 0.0           | -               |
+| 23.1707 | 1900 | 0.0           | -               |
+| 23.7805 | 1950 | 0.0           | -               |
+| 24.3902 | 2000 | 0.0           | -               |
+| 25.0    | 2050 | 0.0           | -               |
+| 25.6098 | 2100 | 0.0           | -               |
+| 26.2195 | 2150 | 0.0           | -               |
+| 26.8293 | 2200 | 0.0           | -               |
+| 27.4390 | 2250 | 0.0           | -               |
+| 28.0488 | 2300 | 0.0           | -               |
+| 28.6585 | 2350 | 0.0           | -               |
+| 29.2683 | 2400 | 0.0           | -               |
+| 29.8780 | 2450 | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0
+- Sentence Transformers: 3.3.1
+- Transformers: 4.44.2
+- PyTorch: 2.2.0a0+81ea7a4
+- Datasets: 3.2.0
+- Tokenizers: 0.19.1
+
+## 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_sl_org_gtcate",
+  "architectures": [
+    "RobertaModel"
+  ],
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+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
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+  "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": "4.44.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.2.0a0+81ea7a4"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+  "labels": null
+}

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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+}

tokenizer_config.json

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