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: 템퍼 컴포트 에어 베개 소프트 NEW 가구/인테리어>베개>메모리폼베개
+- text: 메이슬립 메모리폼 경추 베개 거북목 일자목 목이편한 숙면 편하베개 가구/인테리어>베개>계절베개
+- text: 다니카 다니카 프리미엄 3D 경추 메모리폼 베개 캠핑베개 휴대용 배개 크림 언니 베게 가구/인테리어>베개>메모리폼베개
+- text: 스트라이프 자카드 술 베개 커버 장식 코튼 리넨 원사 염색 쿠션 북유럽 홈 케이스 가구/인테리어>베개>베개커버세트
+- text: 아망떼 뉴데이즈 베개커버 40x60 2장 가구/인테리어>베개>베개커버
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: mini1013/master_domain
+---
+
+# 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                                                                                                                                                                                                                |
+|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 3.0   | <ul><li>'에이트룸 뱀부 프릴 시어서커 베개커버 50X70 가구/인테리어>베개>베개커버'</li><li>'파르페by알레르망 휴비스 듀라론 냉감 베개커버 방석 바디필로우 쇼파패드 냉감패드 쿨매트 4인 75x240 가구/인테리어>베개>베개커버'</li><li>'마틸라 NEW컬러 미드센추리 빈티지맨션 60수 고밀도순면 베개커버-11컬러 가구/인테리어>베개>베개커버'</li></ul> |
+| 4.0   | <ul><li>'마스터유닛2 - 소프트 가구/인테리어>베개>베개커버세트'</li><li>'제이앤우 기절베개 홈랩 오리지널 기절베개 속통 세트구성 파인애플 베개 릴렉스 필로우 베개커버 통세탁 진드기차단 가구/인테리어>베개>베개커버세트'</li><li>'제이앤우 기절베개 오리지널 기절베개 세트구성 호텔식 베개 통세탁가능 가구/인테리어>베개>베개커버세트'</li></ul>          |
+| 0.0   | <ul><li>'즐잠 메밀베개 편백나무 경추베개 목디스크 거북목 일자목 목침 가구/인테리어>베개>계절베개'</li><li>'조은잠 특허받은 허리베개 요추베개 다용도 교정베개 가구/인테리어>베개>계절베개'</li><li>'메이슬립 메모리폼 경추 베개 거북목 일자목 목이편한 숙면 편하베개 가구/인테리어>베개>계절베개'</li></ul>                              |
+| 2.0   | <ul><li>'스패로우 스프링 필로우 메모리폼 베개 가구/인테리어>베개>메모리폼베개'</li><li>'템퍼 템퍼베개 오리지날 베개 S 가구/인테리어>베개>메모리폼베개'</li><li>'템퍼 밀레니엄 베개 SmartCool 가구/인테리어>베개>메모리폼베개'</li></ul>                                                               |
+| 1.0   | <ul><li>'3D와플 경추 견인 베개 메모리폼 라텍스 베게 배게 가구/인테리어>베개>라텍스베개'</li><li>'일자목 안락 낮은 베개 거북목 목 보호 라텍스 숙면 -6cm 진드기 방지 베갯잇 가구/인테리어>베개>라텍스베개'</li><li>'성형 베개 뒤척임 방지 수술 후 코 양악 리프팅 가슴 눈 주름 윤곽 관리 베개 13종 가구/인테리어>베개>라텍스베개'</li></ul>    |
+
+## 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_fi2")
+# Run inference
+preds = model("템퍼 컴포트 에어 베개 소프트 NEW 가구/인테리어>베개>메모리폼베개")
+```
+
+<!--
+### 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   | 8.7293 | 17  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 37                    |
+| 1.0   | 22                    |
+| 2.0   | 23                    |
+| 3.0   | 34                    |
+| 4.0   | 17                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0385  | 1    | 0.4798        | -               |
+| 1.9231  | 50   | 0.2701        | -               |
+| 3.8462  | 100  | 0.0001        | -               |
+| 5.7692  | 150  | 0.0001        | -               |
+| 7.6923  | 200  | 0.0           | -               |
+| 9.6154  | 250  | 0.0           | -               |
+| 11.5385 | 300  | 0.0           | -               |
+| 13.4615 | 350  | 0.0           | -               |
+| 15.3846 | 400  | 0.0           | -               |
+| 17.3077 | 450  | 0.0           | -               |
+| 19.2308 | 500  | 0.0           | -               |
+| 21.1538 | 550  | 0.0           | -               |
+| 23.0769 | 600  | 0.0           | -               |
+| 25.0    | 650  | 0.0           | -               |
+| 26.9231 | 700  | 0.0           | -               |
+| 28.8462 | 750  | 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_fi",
+  "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",
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+    "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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+}