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 +1,237 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 스타스포츠 스타 루카스 스포츠용품 운동신발 족구화 스포츠/레저>족구>족구화
+- text: 스텝 레더 축구 연습 훈련 사다리 순발력 족구 연습기 스포츠/레저>족구>기타족구용품
+- text: 낫소 족구공 큐스팩트A T패널 적용 EVA FORM 쿠션감 스포츠/레저>족구>족구공
+- text: 더블레이어 고탄력 터치감 족구공 족구동호회 족구볼 스포츠/레저>족구>족구공
+- text: 라인기 운동장 경기장 선긋기 트랙 축구장 족구장 주차장 스포츠/레저>족구>기타족구용품
+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:** 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                                                                                                                                                                                                       |
+|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 4.0   | <ul><li>'족구화 발볼넓은 JOMA 호머 축구 신발 남성 MG 짧은 손톱 조마 학생 성인 프리스비 스포츠/레저>족구>족구화'</li><li>'신신상사 스타스포츠 스타 에너제틱 족구화 선수용 JS6200-03 245 스포츠/레저>족구>족구화'</li><li>'스타스포츠 스타 레독스R 족구화 입문자 동호회용 JS5970 스포츠/레저>족구>족구화'</li></ul>  |
+| 1.0   | <ul><li>'신신상사 스타스포츠 스타스포츠 족구공 사인볼 하이브리드 8판넬방식 스포츠/레저>족구>족구공'</li><li>'신신상사 스타스포츠 족구공 풋살공 축구공 족구공 더 윙 태극 8판넬 JB435 스포츠/레저>족구>족구공'</li><li>'스타스포츠 스타스포츠 태극 족구공 동계용 방수코팅 족구 시합구 스포츠/레저>족구>족구공'</li></ul>          |
+| 0.0   | <ul><li>'족구타격기 발차기 훈련 스탠드 연습 운동 레슨 스포츠/레저>족구>기타족구용품'</li><li>'스타 족구 심판대 의자 심판 발판대 라인기 코트경계망 스코어보드 지주세트 네트 번호판 스포츠/레저>족구>기타족구용품'</li><li>'ZIPPO 라이터 Figurehead GD 신명글로빅스 ZPM3MA007R 스포츠/레저>족구>기타족구용품'</li></ul> |
+| 3.0   | <ul><li>'브럼비 족구유니폼 사이트 21시즌 디자인 피오드 - 2 스포츠/레저>족구>족구의류'</li><li>'브럼비 축구유니폼 사이트 24시즌 디자인 로마-2 스포츠/레저>족구>족구의류'</li><li>'족구 유니폼제작 전사팀복 221007 스포츠/레저>족구>족구의류'</li></ul>                                           |
+| 2.0   | <ul><li>'낫소 일반형 족구네트 NSJ-N105 스포츠/레저>족구>족구네트'</li><li>'오레인 족구 네트 OJG-N224 스포츠/레저>족구>족구네트'</li><li>'엔포유 N4U-B500 배드민턴 족구 다용도네트 스포츠/레저>족구>족구네트'</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_sl26")
+# Run inference
+preds = model("스타스포츠 스타 루카스 스포츠용품 운동신발 족구화 스포츠/레저>족구>족구화")
+```
+
+<!--
+### 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   | 2   | 8.0441 | 19  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 70                    |
+| 1.0   | 70                    |
+| 2.0   | 15                    |
+| 3.0   | 70                    |
+| 4.0   | 70                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0172  | 1    | 0.4882        | -               |
+| 0.8621  | 50   | 0.4668        | -               |
+| 1.7241  | 100  | 0.1034        | -               |
+| 2.5862  | 150  | 0.0002        | -               |
+| 3.4483  | 200  | 0.0           | -               |
+| 4.3103  | 250  | 0.0           | -               |
+| 5.1724  | 300  | 0.0           | -               |
+| 6.0345  | 350  | 0.0           | -               |
+| 6.8966  | 400  | 0.0           | -               |
+| 7.7586  | 450  | 0.0           | -               |
+| 8.6207  | 500  | 0.0           | -               |
+| 9.4828  | 550  | 0.0           | -               |
+| 10.3448 | 600  | 0.0           | -               |
+| 11.2069 | 650  | 0.0           | -               |
+| 12.0690 | 700  | 0.0           | -               |
+| 12.9310 | 750  | 0.0           | -               |
+| 13.7931 | 800  | 0.0           | -               |
+| 14.6552 | 850  | 0.0           | -               |
+| 15.5172 | 900  | 0.0           | -               |
+| 16.3793 | 950  | 0.0           | -               |
+| 17.2414 | 1000 | 0.0           | -               |
+| 18.1034 | 1050 | 0.0           | -               |
+| 18.9655 | 1100 | 0.0           | -               |
+| 19.8276 | 1150 | 0.0           | -               |
+| 20.6897 | 1200 | 0.0           | -               |
+| 21.5517 | 1250 | 0.0           | -               |
+| 22.4138 | 1300 | 0.0           | -               |
+| 23.2759 | 1350 | 0.0           | -               |
+| 24.1379 | 1400 | 0.0           | -               |
+| 25.0    | 1450 | 0.0           | -               |
+| 25.8621 | 1500 | 0.0           | -               |
+| 26.7241 | 1550 | 0.0           | -               |
+| 27.5862 | 1600 | 0.0           | -               |
+| 28.4483 | 1650 | 0.0           | -               |
+| 29.3103 | 1700 | 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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+  "bos_token_id": 0,
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+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "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": "4.44.2",
+  "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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+  "normalize_embeddings": false
+}

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

tokenizer_config.json

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