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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+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 고급형 검도 손목보호대 검도보호대 일본산 스포츠/레저>검도>검도보호용품
+- text: 검좌대 검도 목검 거치대 사무라이검 받침대 플루트 진열대 검 스탠드 죽도 선반 스포츠/레저>검도>기타검도용품
+- text: 검도단 탁상 사무실용 대나무 디스플레이 Tier 478490 1 스포츠/레저>검도>검도보호용품
+- text: 스탠드 검도 타격대 타이어 죽도 훈련 연습 수련 도장 스포츠/레저>검도>타격대
+- text: 검거치대 좌대 거치대 진열대 검받침대 사극 무술 랙 소품 목도 스포츠/레저>검도>기타검도용품
+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                                                                                                                                                                                                               |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0.0   | <ul><li>'검도 장갑 호완 보호대 호구 손 보호 장비 검도용품 스포츠/레저>검도>검도보호용품'</li><li>'검도호구 장비 보호대 호면 세트 갑옷 방어구 호완 머리 부품 입문용 손목 갑상 턱 초보자용 스포츠/레저>검도>검도보호용품'</li><li>'나인 더 가든 구사쿠라 체리 완제품 여아용 클로즈 토 가죽 미디엄 건축 t3l 스포츠/레저>검도>검도보호용품'</li></ul> |
+| 1.0   | <ul><li>'호구가방 검도 캐리어 방어구 장비 대용량 배낭 백팩 스포츠/레저>검도>기타검도용품'</li><li>'목검 거치대 받침대 검도 검 진열대 죽도 보관함 홀더 스포츠/레저>검도>기타검도용품'</li><li>'검좌대 원목 랙 도검좌대 사극 무술 검도 목도 소품 장식용 스탠드 목검 선반 스포츠/레저>검도>기타검도용품'</li></ul>                       |
+| 2.0   | <ul><li>'검도 백색 검도복 검도 도복 스포츠/레저 > 검도 > 도복'</li><li>'검도 도복 프리미엄 검도복 선 (SUN) 스포츠/레저 > 검도 > 도복'</li><li>'뉴페이스 도복 상하세트 C4B-2 스포츠/레저>검도>도복'</li></ul>                                                                       |
+| 3.0   | <ul><li>'성심 고도형 죽도 39호 스포츠/레저>검도>죽도'</li><li>'단심 시합용 죽도 39호 스포츠/레저>검도>죽도'</li><li>'충신 시합용 죽도 39호 스포츠/레저>검도>죽도'</li></ul>                                                                                               |
+| 4.0   | <ul><li>'봉집 무술용품 목검가방 죽도가방 스포츠/레저 > 검도 > 죽도집/부속품'</li><li>'검가방/천검집(가검용) G-12D 스포츠/레저 > 검도 > 죽도집/부속품'</li><li>'검도 죽도 가방 목검 주머니 패브릭 목검집 스트랩 휴대용 스포츠/레저 > 검도 > 죽도집/부속품'</li></ul>                                         |
+| 5.0   | <ul><li>'목인장 원목 샌드백 영춘권 무술 복싱 목각 타격대 1 7m-옵션사진참조 스포츠/레저>검도>타격대'</li><li>'검도 연습 타격대 스탠드 죽도 훈련 수련 샌드백 더미 연습용 수련대 펜싱 타겟 기술 스포츠/레저>검도>타격대'</li><li>'검도 타격대 샌드백 찌르기 연습용 죽도 수련대 무술 스포츠/레저>검도>타격대'</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_sl0")
+# Run inference
+preds = model("고급형 검도 손목보호대 검도보호대 일본산 스포츠/레저>검도>검도보호용품")
+```
+
+<!--
+### 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   | 9.5927 | 19  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 70                    |
+| 1.0   | 70                    |
+| 2.0   | 12                    |
+| 3.0   | 15                    |
+| 4.0   | 11                    |
+| 5.0   | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0204  | 1    | 0.4824        | -               |
+| 1.0204  | 50   | 0.4133        | -               |
+| 2.0408  | 100  | 0.0315        | -               |
+| 3.0612  | 150  | 0.0021        | -               |
+| 4.0816  | 200  | 0.0001        | -               |
+| 5.1020  | 250  | 0.0           | -               |
+| 6.1224  | 300  | 0.0           | -               |
+| 7.1429  | 350  | 0.0           | -               |
+| 8.1633  | 400  | 0.0           | -               |
+| 9.1837  | 450  | 0.0           | -               |
+| 10.2041 | 500  | 0.0           | -               |
+| 11.2245 | 550  | 0.0           | -               |
+| 12.2449 | 600  | 0.0           | -               |
+| 13.2653 | 650  | 0.0           | -               |
+| 14.2857 | 700  | 0.0           | -               |
+| 15.3061 | 750  | 0.0           | -               |
+| 16.3265 | 800  | 0.0           | -               |
+| 17.3469 | 850  | 0.0           | -               |
+| 18.3673 | 900  | 0.0           | -               |
+| 19.3878 | 950  | 0.0           | -               |
+| 20.4082 | 1000 | 0.0           | -               |
+| 21.4286 | 1050 | 0.0           | -               |
+| 22.4490 | 1100 | 0.0           | -               |
+| 23.4694 | 1150 | 0.0           | -               |
+| 24.4898 | 1200 | 0.0           | -               |
+| 25.5102 | 1250 | 0.0           | -               |
+| 26.5306 | 1300 | 0.0           | -               |
+| 27.5510 | 1350 | 0.0           | -               |
+| 28.5714 | 1400 | 0.0           | -               |
+| 29.5918 | 1450 | 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"
+  ],
+  "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": "4.44.2",
+  "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,
+  "labels": null
+}

model.safetensors

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model_head.pkl

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modules.json

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+    "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
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+}

tokenizer_config.json

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+}