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: 피클볼라켓 가족용 나무 패들 초보자 라켓 메쉬 캐리 백 스포츠/레저>수련용품>기타수련용품
+- text: 미즈노 복싱화 레슬링화 권투화 피니셔 미드 FINISHER MID 스포츠/레저>수련용품>수련화
+- text: 프랭클린 스포츠 사이즈 콘홀 백 - 8 프리미엄 6 헤비 듀티 더블 스티치 캔버스 스포츠/레저>수련용품>기타수련용품
+- text: 미즈노 복싱화 권투화 이지 스펙트라 37 플래시 그린 X 05 테두리 BM518 스포츠/레저>수련용품>수련화
+- text: 주짓수 경량 도복 상하세트 훈련 남성 여성 통기성 스포츠/레저>수련용품>무도복
+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>'레슬링화 신발 남성 권투화 전문 훈련 복싱용품 복싱 남녀공용 트레이닝 스포츠/레저>수련용품>수련화'</li><li>'아디다스 복싱 스피덱스18 복싱화 FZ5308 스포츠/레저>수련용품>수련화'</li><li>'여성 복싱화 킥복싱 신발 권투화 운동화-514 스포츠/레저>수련용품>수련화'</li></ul>                        |
+| 0.0   | <ul><li>'HK 조립식송판 태권도 격파판 격투기 용품 스포츠/레저 > 수련용품 > 격파용품'</li><li>'격파 용품 나무 격파판 나무송판 행사용 태권도 격파용 9mm 송판 50장묶음 스포츠/레저 > 수련용품 > 격파용품'</li><li>'무토 중급자용 플라스틱 송판 62kg 스포츠/레저>수련용품>격파용품'</li></ul>                |
+| 3.0   | <ul><li>'케이네트워크 컨텐더 시합용 주짓수도복 펄위브 도복 CJW-554WR 스포츠/레저>수련용품>무도복'</li><li>'주짓수 도복 기모노 훈련복 어린이 성인 여성 스포츠/레저>수련용품>무도복'</li><li>'무에타이 트렁크 쇼츠 바지 격투기 UFC 권투 팬츠 파이트 MMA 킥복싱 반바지 스포츠/레저>수련용품>무도복'</li></ul>       |
+| 1.0   | <ul><li>'전동 포일보드 방수 고출력 이포일 하이드로 윈드 스포츠/레저>수련용품>기타수련용품'</li><li>'남성과 여성을위한 전문 승마 초박형 속건 바지 흰색 경쟁 훈련 장비 실리콘 스포츠/레저>수련용품>기타수련용품'</li><li>'Weaver 가죽 벨트 블랭크 스냅 구멍 스포츠/레저>수련용품>기타수련용품'</li></ul>              |
+| 2.0   | <ul><li>'다오코리아 유도 태권도 주짓수 검정띠 자수포함 품띠 검은띠 유단자띠 스포츠/레저 > 수련용품 > 띠/벨트'</li><li>'아디다스 벨트 태권도 유급자 색 띠 스포츠/레저 > 수련용품 > 띠/벨트'</li><li>'아디다스 유도벨트 띠 선수용띠 국가대표 실업팀 대회띠 유도선수용 블랙밸트 스포츠/레저 > 수련용품 > 띠/벨트'</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_sl15")
+# Run inference
+preds = model("주짓수 경량 도복 상하세트 훈련 남성 여성 통기성 스포츠/레저>수련용품>무도복")
+```
+
+<!--
+### 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   | 9.7851 | 20  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 9                     |
+| 1.0   | 70                    |
+| 2.0   | 9                     |
+| 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0222  | 1    | 0.4899        | -               |
+| 1.1111  | 50   | 0.4031        | -               |
+| 2.2222  | 100  | 0.0374        | -               |
+| 3.3333  | 150  | 0.0           | -               |
+| 4.4444  | 200  | 0.0           | -               |
+| 5.5556  | 250  | 0.0           | -               |
+| 6.6667  | 300  | 0.0           | -               |
+| 7.7778  | 350  | 0.0           | -               |
+| 8.8889  | 400  | 0.0           | -               |
+| 10.0    | 450  | 0.0           | -               |
+| 11.1111 | 500  | 0.0           | -               |
+| 12.2222 | 550  | 0.0           | -               |
+| 13.3333 | 600  | 0.0           | -               |
+| 14.4444 | 650  | 0.0           | -               |
+| 15.5556 | 700  | 0.0           | -               |
+| 16.6667 | 750  | 0.0           | -               |
+| 17.7778 | 800  | 0.0           | -               |
+| 18.8889 | 850  | 0.0           | -               |
+| 20.0    | 900  | 0.0           | -               |
+| 21.1111 | 950  | 0.0           | -               |
+| 22.2222 | 1000 | 0.0           | -               |
+| 23.3333 | 1050 | 0.0           | -               |
+| 24.4444 | 1100 | 0.0           | -               |
+| 25.5556 | 1150 | 0.0           | -               |
+| 26.6667 | 1200 | 0.0           | -               |
+| 27.7778 | 1250 | 0.0           | -               |
+| 28.8889 | 1300 | 0.0           | -               |
+| 30.0    | 1350 | 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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+    "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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+}