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 +1,236 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 남자 후리스 점퍼 자켓 패딩 가을 겨울 남학생 고등학생 중학생 빅사이즈 3XL E_XXL 출산/육아 > 유아발육용품 > 점퍼루
+- text: 부드러운 양말 스타 유아 워커 편안한 국민 미끄럼 신발 아기 부티 보행기 첫 따뜻한 방지 16=B315white_13-18달 출산/육아
+    > 유아발육용품 > 보행기
+- text: 이븐플로 엑서쏘서 트리플펀 아마존 점프앤런 9종 택1 출산선물 조카선물 이븐플로 엑서쏘서_트리플펀 아마존 출산/육아 > 유아발육용품
+    > 쏘서
+- text: 신생아 바운서 유아 용품 흔들 요람 침대 스마트 출산 카키 접이식 표준 엘리트 버전 출산/육아 > 유아발육용품 > 바운서/흔들침대
+- text: 아기보행기 O자형 전복방지 카트탑승가능 보행기 4.하늘색 유아교육경음악발매트 출산/육아 > 유아발육용품 > 보행기
+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:** 4 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                                                                                                                                                                                                                                                                                                                                      |
+|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1.0   | <ul><li>'동물 자동차 당김 로프, 조기 교육 선물, 드래그 차량 딸랑이 158447 B2QD2SS901998-6 158447 B2QD2SS901998-4 출산/육아 > 유아발육용품 > 보행기'</li><li>'[대여] [보행기] NEW 뉴 스텝360 다기능 아기보행기 대여 렌탈 ①스텝360_3개월대여 출산/육아 > 유아발육용품 > 보행기'</li><li>'휴대용 접이식 신생아 걸음마 보행기 Egobaby 360 베이비 캐리어 다기능 통기성 유아 백팩 어린이 캐리지 아기 슬링 랩 멜빵 옴니 28 breeze black print 출산/육아 > 유아발육용품 > 보행기'</li></ul> |
+| 2.0   | <ul><li>'이븐플로 엑서쏘서 트리플펀 아마존 점프앤런 9종 택1 출산선물 조카선물 이븐플로 엑서쏘서_사파리 친구들 점프&런 출산/육아 > 유아발육용품 > 쏘서'</li><li>'젤리캣 동물 인형 딸랑이 버니 퍼피 양 코끼리 유니콘 5종 유니콘 출산/육아 > 유아발육용품 > 쏘서'</li><li>'[대여][+10일연장] 바로가능 새상품입고 피아노 어라운드 위고 베이비아인슈타인/위고대여 중고판매상품_피아노어라운드위고 출산/육아 > 유아발육용품 > 쏘서'</li></ul>                                                                    |
+| 0.0   | <ul><li>'2021 new 스마트 흔들바운서 진동요람 (B타입)그린 출산/육아 > 유아발육용품 > 바운서/흔들침대'</li><li>'[개봉/미개봉] 베이비뵨 바운서 대여 렌탈 메쉬 저지 코튼 아기 신생아 컨디션A 05. 소프트코튼저지(개봉)_2개월+10일_랜덤발송 출산/육아 > 유아발육용품 > 바운서/흔들침대'</li><li>'[대여][+7일추가/왕복 ] 최신형 크래들스윙대여 피셔프라이스 [베이비노리터] 신형 버튼식 A급+이너시트(당일출고)_블루_한달대여+7일서비스 출산/육아 > 유아발육용품 > 바운서/흔들침대'</li></ul>                                |
+| 3.0   | <ul><li>'여자 어린이 겨울 패딩 후드 잠바 다운 재킷 6. 2중후드 패딩 퍼플화이트_140cm 출산/육아 > 유아발육용품 > 점퍼루'</li><li>'[대여][미개봉새상품대여] 졸리점퍼 오리지널스탠드 슈퍼스탠드 점퍼루 쏘서 슈퍼 스탠드[+7일 추가]_2022입고 한달대여[+7일 추가] 출산/육아 > 유아발육용품 > 점퍼루'</li><li>'경량 패딩점퍼 오버핏 중년 여성롱패딩 빅사이즈 구스다운 퀄팅 롱패딩 블랙_XL 출산/육아 > 유아발육용품 > 점퍼루'</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_bc22")
+# Run inference
+preds = model("아기보행기 O자형 전복방지 카트탑승가능 보행기 4.하늘색 유아교육경음악발매트 출산/육아 > 유아발육용품 > 보행기")
+```
+
+<!--
+### 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   | 8   | 15.8643 | 35  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 70                    |
+| 1.0   | 70                    |
+| 2.0   | 70                    |
+| 3.0   | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0182  | 1    | 0.489         | -               |
+| 0.9091  | 50   | 0.4696        | -               |
+| 1.8182  | 100  | 0.1613        | -               |
+| 2.7273  | 150  | 0.0002        | -               |
+| 3.6364  | 200  | 0.0           | -               |
+| 4.5455  | 250  | 0.0           | -               |
+| 5.4545  | 300  | 0.0           | -               |
+| 6.3636  | 350  | 0.0           | -               |
+| 7.2727  | 400  | 0.0           | -               |
+| 8.1818  | 450  | 0.0           | -               |
+| 9.0909  | 500  | 0.0           | -               |
+| 10.0    | 550  | 0.0           | -               |
+| 10.9091 | 600  | 0.0           | -               |
+| 11.8182 | 650  | 0.0           | -               |
+| 12.7273 | 700  | 0.0           | -               |
+| 13.6364 | 750  | 0.0           | -               |
+| 14.5455 | 800  | 0.0           | -               |
+| 15.4545 | 850  | 0.0           | -               |
+| 16.3636 | 900  | 0.0           | -               |
+| 17.2727 | 950  | 0.0           | -               |
+| 18.1818 | 1000 | 0.0           | -               |
+| 19.0909 | 1050 | 0.0           | -               |
+| 20.0    | 1100 | 0.0           | -               |
+| 20.9091 | 1150 | 0.0           | -               |
+| 21.8182 | 1200 | 0.0           | -               |
+| 22.7273 | 1250 | 0.0           | -               |
+| 23.6364 | 1300 | 0.0           | -               |
+| 24.5455 | 1350 | 0.0           | -               |
+| 25.4545 | 1400 | 0.0           | -               |
+| 26.3636 | 1450 | 0.0           | -               |
+| 27.2727 | 1500 | 0.0           | -               |
+| 28.1818 | 1550 | 0.0           | -               |
+| 29.0909 | 1600 | 0.0           | -               |
+| 30.0    | 1650 | 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_bc",
+  "architectures": [
+    "RobertaModel"
+  ],
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+  "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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+    "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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tokenizer_config.json

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+}