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 +1,258 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 손가락 빠는 아기 치발기 손빠는 아이 못빨게 교정기 빨기 방지 신생아 엄지 백일 100일 손가락 빨기 방지 엄지 장갑 (L) 출산/육아
+    > 구강청결용품 > 손가락빨기방지용품
+- text: 치아모형 간단한 헤드 모델 치과 시뮬레이터 팬텀 헤드, 의사 교육용, 트레이닝 기구  출산/육아 > 구강청결용품 > 기타구강청결용품
+- text: 이천사 OT704 SSSS 7형0.4mm 치실치간칫솔 교정치간칫솔 유아 어린이 65- CT22- 3단계 이중라운드-12개 출산/육아
+    > 구강청결용품 > 기타구강청결용품
+- text: 제니튼 닥터제니 어린이치약 4개 미니4개 초등학생치약 유아 베이비 [1450 고불소]_1450 고불소(라즈베리)4개+미니치약4개 출산/육아
+    > 구강청결용품 > 유아치약
+- text: 이천사 OT110 S 1형1.0mm 치실치간칫솔 교정치간칫솔 유아 어린이 71- PT31- 청소년이중슬림모-12개 출산/육아 > 구강청결용품
+    > 기타구강청결용품
+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                                                                                                                                                                                                                                                                                            |
+|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 2.0   | <ul><li>'가그린 어린이 가글 딸기 3개 구강청결제  출산/육아 > 구강청결용품 > 유아구강세정제'</li><li>'메가텐 키즈 소닉 럭스 360 전동칫솔 리필모 4P 미디엄(만4세-만12세) 출산/육아 > 구강청결용품 > 유아구강세정제'</li><li>'2080 kids 어린이가글250ml/ 저불소/ 바나나맛/딸기맛 구강청결제 작은사이즈 키즈 상품선택_바나나맛250ml 출산/육아 > 구강청결용품 > 유아구강세정제'</li></ul>                                             |
+| 0.0   | <ul><li>'코클리어 시원한호흡 코밴드 코패치 10일사용(10매1박스) 출산/육아 > 구강청결용품 > 기타구강청결용품'</li><li>'플랙커스치실 일회용치실 어린이 유아 아기 치실 4팩 1. 플랙커스 키즈치실 30p 4팩 출산/육아 > 구강청결용품 > 기타구강청결용품'</li><li>'닥터코링  출산/육아 > 구강청결용품 > 기타구강청결용품'</li></ul>                                                                                       |
+| 5.0   | <ul><li>'켄트 초등학생 키즈 칫솔 어린이칫솔 4개입 켄트 오리지널 6개 출산/육아 > 구강청결용품 > 유아칫솔'</li><li>'메가텐 럭스360 어린이 유아 칫솔 6개입 기능성 (1 2 3 단계) 독일도스 유기농 치약(무불소)_메가텐7P_1단계 출산/육아 > 구강청결용품 > 유아칫솔'</li><li>'키즈텐 5형제 어린이 칫솔 3단계 5개입  출산/육아 > 구강청결용품 > 유아칫솔'</li></ul>                                                              |
+| 4.0   | <ul><li>'키즈세이프 충치집중케어 치약 60g x 10개/ 고불소 치약, 충치 6.키즈세이프 유아칫솔 2단계 x 16개 출산/육아 > 구강청결용품 > 유아치약'</li><li>'벨레다 어린이 치약 50ml 2p+손가락칫솔 1p 충치 예방 안전 치약 벨레다 치약 2개 + 유아칫솔 1개 출산/육아 > 구강청결용품 > 유아치약'</li><li>'페리오키즈 스텝2 핑크퐁 치약 청포도향 75g 무불소 80G 출산/육아 > 구강청결용품 > 유아치약'</li></ul>                                |
+| 3.0   | <ul><li>'[1+1] 121도씨 비앤비 리꼬 구강티슈 (오가닉 순면 무첨가물) [1+1] 121도씨 구강티슈 50매 x2 출산/육아 > 구강청결용품 > 유아구강청결티슈'</li><li>'메디안 골드 와이드프로 칫솔 4개입 잇몸케어 미세모 치석케어 이중미세모 치솔 메디안 골드 와이드프로 칫솔4개입(미세모) 출산/육아 > 구강청결용품 > 유아구강청결티슈'</li><li>'비앤비 구강청결티슈 30매 X 3개/4개/5개/2개 비앤비 구강청결티슈30매X3 출산/육아 > 구강청결용품 > 유아구강청결티슈'</li></ul> |
+| 1.0   | <ul><li>'썸프렌즈 (엄지용) A set 동물 친구들_Large 출산/육아 > 구강청결용품 > 손가락빨기방지용품'</li><li>'아가프라/닥터핑거 1+1 (엄지,검지,중지)l/손가락빨기교정 양손겸용 닥터핑거(중지/검지용)-화이트_닥터핑거(중지/검지용)-퍼플 출산/육아 > 구강청결용품 > 손가락빨기방지용품'</li><li>'스몰 투명색상 닥터썸  출산/육아 > 구강청결용품 > 손가락빨기방지용품'</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_bc1")
+# Run inference
+preds = model("치아모형 간단한 헤드 모델 치과 시뮬레이터 팬텀 헤드, 의사 교육용, 트레이닝 기구  출산/육아 > 구강청결용품 > 기타구강청결용품")
+```
+
+<!--
+### 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   | 7   | 14.6262 | 32  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 70                    |
+| 1.0   | 70                    |
+| 2.0   | 70                    |
+| 3.0   | 70                    |
+| 4.0   | 70                    |
+| 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0120  | 1    | 0.4929        | -               |
+| 0.6024  | 50   | 0.4852        | -               |
+| 1.2048  | 100  | 0.3091        | -               |
+| 1.8072  | 150  | 0.0608        | -               |
+| 2.4096  | 200  | 0.0005        | -               |
+| 3.0120  | 250  | 0.0001        | -               |
+| 3.6145  | 300  | 0.0001        | -               |
+| 4.2169  | 350  | 0.0           | -               |
+| 4.8193  | 400  | 0.0           | -               |
+| 5.4217  | 450  | 0.0           | -               |
+| 6.0241  | 500  | 0.0           | -               |
+| 6.6265  | 550  | 0.0           | -               |
+| 7.2289  | 600  | 0.0           | -               |
+| 7.8313  | 650  | 0.0           | -               |
+| 8.4337  | 700  | 0.0           | -               |
+| 9.0361  | 750  | 0.0           | -               |
+| 9.6386  | 800  | 0.0           | -               |
+| 10.2410 | 850  | 0.0           | -               |
+| 10.8434 | 900  | 0.0           | -               |
+| 11.4458 | 950  | 0.0           | -               |
+| 12.0482 | 1000 | 0.0           | -               |
+| 12.6506 | 1050 | 0.0           | -               |
+| 13.2530 | 1100 | 0.0           | -               |
+| 13.8554 | 1150 | 0.0           | -               |
+| 14.4578 | 1200 | 0.0           | -               |
+| 15.0602 | 1250 | 0.0           | -               |
+| 15.6627 | 1300 | 0.0           | -               |
+| 16.2651 | 1350 | 0.0           | -               |
+| 16.8675 | 1400 | 0.0           | -               |
+| 17.4699 | 1450 | 0.0           | -               |
+| 18.0723 | 1500 | 0.0           | -               |
+| 18.6747 | 1550 | 0.0           | -               |
+| 19.2771 | 1600 | 0.0           | -               |
+| 19.8795 | 1650 | 0.0           | -               |
+| 20.4819 | 1700 | 0.0           | -               |
+| 21.0843 | 1750 | 0.0           | -               |
+| 21.6867 | 1800 | 0.0           | -               |
+| 22.2892 | 1850 | 0.0           | -               |
+| 22.8916 | 1900 | 0.0           | -               |
+| 23.4940 | 1950 | 0.0           | -               |
+| 24.0964 | 2000 | 0.0           | -               |
+| 24.6988 | 2050 | 0.0           | -               |
+| 25.3012 | 2100 | 0.0           | -               |
+| 25.9036 | 2150 | 0.0           | -               |
+| 26.5060 | 2200 | 0.0           | -               |
+| 27.1084 | 2250 | 0.0           | -               |
+| 27.7108 | 2300 | 0.0           | -               |
+| 28.3133 | 2350 | 0.0           | -               |
+| 28.9157 | 2400 | 0.0           | -               |
+| 29.5181 | 2450 | 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"
+  ],
+  "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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+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

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+size 442494816

model_head.pkl

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modules.json

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+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "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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+{
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+    "content": "[CLS]",
+    "lstrip": false,
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+  },
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}