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 +1,246 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: '[즉시15%+중복20%] 예꼬맘 어린이 실크 칫솔 0.07mm 3개 예꼬맘실크칫솔화이트 1단계_예꼬맘실크칫솔핑크 2단계_예꼬맘실크칫솔옐로우
+    1단계 출산/육아 > 유아세제 > 유아세탁비누'
+- text: 2023 에브리케어 블랙프라이데이 12. 주방세제 500g 출산/육아 > 유아세제 > 유아세탁세제
+- text: 마이비 피부에순한 유아섬유유연제 (리필 1600ml)  출산/육아 > 유아세제 > 유아세탁비누
+- text: 베르블랑 중성 아기세제 1L X 3개 (프리미엄 가루세제 구연산 1kg) 머스크향[VB-LM3]_프리미엄 가루세제 구연산 1kg[VB-CA1]
+    출산/육아 > 유아세제 > 유아세탁세제
+- text: 비브라이트 어린이 LED 타이머 유아칫솔 양치컵홀더3P세트 핑크냐옹 출산/육아 > 유아세제 > 유아세탁비누
+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>'비앤비 섬유유연제 베르가못 캡리필 1800ml X 6개  출산/육아 > 유아세제 > 혼합세트'</li><li>'베비언스 아기세제 핑크퐁 베이비 아기섬유유연제 유아세탁세제 유아세제 섬유유연제  출산/육아 > 유아세제 > 혼합세트'</li><li>'레드루트 유아 아기 세탁세제1L+유연제1L 향 선택 머스크플로랄_바닐라코튼 출산/육아 > 유아세제 > 혼합세트'</li></ul>                                                                                          |
+| 2.0   | <ul><li>'생활용품세탁세재욕실청소 베르블랑 유아 섬유 유연제 그린플로럴향 1000ml, 1개 샹활용품욕실청소세탁세재 생활용품욕실청소세탁세재 생활용품 1000ml × 3개 출산/육아 > 유아세제 > 유아유연제'</li><li>'레드루트 건조기시트 섬유유연제 50매 향선택 건조기시트50매_스위트 출산/육아 > 유아세제 > 유아유연제'</li><li>'비앤비 유아 아기 신생아 섬유유연제 1800ml 3팩 리필 베이비 유연제 액상형 섬유린스 세제/유연제_09.유연제베르가못1800ml리필×4 출산/육아 > 유아세제 > 유아유연제'</li></ul> |
+| 3.0   | <ul><li>'마더케이 디아 산소계 표백제 1kg (무향)  출산/육아 > 유아세제 > 유아표백제/얼룩제거제'</li><li>'비앤비 얼룩제거제 500ml 옷얼룩제거 유아옷 얼룩제거 천연��분 함유 젖병세정제_거품 450ml 용기 출산/육아 > 유아세제 > 유아표백제/얼룩제거제'</li><li>'마이비 얼룩제거제 330ml + 리필 300ml x 3개  출산/육아 > 유아세제 > 유아표백제/얼룩제거제'</li></ul>                                                                        |
+| 0.0   | <ul><li>'아이앤어스 독일더마 프리미엄 캡슐형 세탁세제 30개입 x4팩 아이앤어스 독일더마 프리미엄 캡슐형 세탁세제 출산/육아 > 유아세제 > 유아세탁비누'</li><li>'러블리앙즈 유아마스크 30매 어린이 3D 새부리형 초소형 소형 4 8세_M9 왕관곰 4 8세 출산/육아 > 유아세제 > 유아세탁비누'</li><li>'네이쳐러브메레 유연제, 리필, 체리블러썸향, 1300ml, 4개 체리블러썸 유연제 4개 오리지널 세제 4개 출산/육아 > 유아세제 > 유아세탁비누'</li></ul>                                |
+| 1.0   | <ul><li>'레드루트 유아 섬유유연제 세탁세제 1L 3개세트 바닐라코튼 세탁세제 _ 머스크_세탁세제 _ 스위트_유연제 _ 바닐라 출산/육아 > 유아세제 > 유아세탁세제'</li><li>'베비언스 핑크퐁 베이비 세탁세제 리필 2.2L  출산/육아 > 유아세제 > 유아세탁세제'</li><li>'[3개] 아이너바움 대용량 세탁세제 3개세트 무향 / 유아 아기 신생아 비건인증 세제 3.비건인증 안심 세제 3종(세탁2+섬유1)_세탁세제(네이처플라워 2개)_섬유유연제(스윗선데이 1개) 출산/육아 > 유아세제 > 유아세탁세제'</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_bc23")
+# Run inference
+preds = model("마이비 피부에순한 유아섬유유연제 (리필 1600ml)  출산/육아 > 유아세제 > 유아세탁비누")
+```
+
+<!--
+### 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   | 15.3086 | 31  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 70                    |
+| 1.0   | 70                    |
+| 2.0   | 70                    |
+| 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0145  | 1    | 0.4811        | -               |
+| 0.7246  | 50   | 0.4993        | -               |
+| 1.4493  | 100  | 0.4843        | -               |
+| 2.1739  | 150  | 0.276         | -               |
+| 2.8986  | 200  | 0.0128        | -               |
+| 3.6232  | 250  | 0.0           | -               |
+| 4.3478  | 300  | 0.0           | -               |
+| 5.0725  | 350  | 0.0           | -               |
+| 5.7971  | 400  | 0.0           | -               |
+| 6.5217  | 450  | 0.0           | -               |
+| 7.2464  | 500  | 0.0           | -               |
+| 7.9710  | 550  | 0.0           | -               |
+| 8.6957  | 600  | 0.0           | -               |
+| 9.4203  | 650  | 0.0           | -               |
+| 10.1449 | 700  | 0.0           | -               |
+| 10.8696 | 750  | 0.0           | -               |
+| 11.5942 | 800  | 0.0           | -               |
+| 12.3188 | 850  | 0.0           | -               |
+| 13.0435 | 900  | 0.0           | -               |
+| 13.7681 | 950  | 0.0           | -               |
+| 14.4928 | 1000 | 0.0           | -               |
+| 15.2174 | 1050 | 0.0           | -               |
+| 15.9420 | 1100 | 0.0           | -               |
+| 16.6667 | 1150 | 0.0           | -               |
+| 17.3913 | 1200 | 0.0           | -               |
+| 18.1159 | 1250 | 0.0           | -               |
+| 18.8406 | 1300 | 0.0           | -               |
+| 19.5652 | 1350 | 0.0           | -               |
+| 20.2899 | 1400 | 0.0           | -               |
+| 21.0145 | 1450 | 0.0           | -               |
+| 21.7391 | 1500 | 0.0           | -               |
+| 22.4638 | 1550 | 0.0           | -               |
+| 23.1884 | 1600 | 0.0           | -               |
+| 23.9130 | 1650 | 0.0           | -               |
+| 24.6377 | 1700 | 0.0           | -               |
+| 25.3623 | 1750 | 0.0           | -               |
+| 26.0870 | 1800 | 0.0           | -               |
+| 26.8116 | 1850 | 0.0           | -               |
+| 27.5362 | 1900 | 0.0           | -               |
+| 28.2609 | 1950 | 0.0           | -               |
+| 28.9855 | 2000 | 0.0           | -               |
+| 29.7101 | 2050 | 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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+    "name": "0",
+    "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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+}

tokenizer_config.json

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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}