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 +1,259 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 마더케이 리필형 롱핸들 스펀지 실리콘 젖병솔 젖꼭지솔 세트 3.리필형 스펀지 젖꼭지솔 세트_베이지 출산/육아 > 소독/살균용품 > 젖병솔
+- text: 비앤비 젖병세정제 거품형 용기 450ml B.BB젖병세제 거품형 용기450mlx1개 출산/육아 > 소독/살균용품 > 젖병세정제
+- text: 마더케이 리필형 롱핸들스펀지솔 세척용품세트 (젖병건조대+리필브러쉬+젖병집게) 네이비_1.리필형젖병솔세트_초코브라운 출산/육아 > 소독/살균용품
+    > 젖병건조대
+- text: 다용도 플라스틱 물병 텀블러 건조대 보틀 건조대  출산/육아 > 소독/살균용품 > 젖병건조대
+- text: 비앤비 유아 아기 젖병세정제 거품형 용기  출산/육아 > 소독/살균용품 > 젖병세정제
+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: 0.9978046103183315
+      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:** 7 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>'아기 젖병 식기 주방 세정제 유아 신생아 중성세제 거품형(리필400ml) 출산/육아 > 소독/살균용품 > 젖병세정제'</li><li>'마더케이 디아 아기 유아 이유식기 세정제 500ml (액상형/무향) 이유식기세정제500ml(무향)_이유식기세정제500ml(무향) 출산/육아 > 소독/살균용품 > 젖병세정제'</li><li>'비앤비 젖병세정제 거품형 리필 400ml x 4개  출산/육아 > 소독/살균용품 > 젖병세정제'</li></ul>             |
+| 3.0   | <ul><li>'NEW 폴레드 픽셀 UVC LED 자연건조 유아소독기 픽셀+맘마존트레이_오프화이트 바디_더스티핑크  패널 출산/육아 > 소독/살균용품 > 젖병소독기'</li><li>'해님 UV LED 젖병소독기 4세대 플러스 스마트 플렉스_화이트 출산/육아 > 소독/살균용품 > 젖병소독기'</li><li>'NEW 폴레드 픽셀 UVC LED 자연건조 유아소독기 픽셀+맘마존트레이_다크그레이 바디_오프화이트  패널 출산/육아 > 소독/살균용품 > 젖병소독기'</li></ul> |
+| 5.0   | <ul><li>'브러쉬 세트 스폰지 알프스  출산/육아 > 소독/살균용품 > 젖병솔'</li><li>'마더케이 리필형 롱핸들 스펀지 실리콘 젖병솔 젖꼭지솔 세트 7.리필형 스펀지 젖병솔+젖꼭지솔 풀세트_인디핑크 출산/육아 > 소독/살균용품 > 젖병솔'</li><li>'스펙트라 롱타입 젖꼭지솔 2개입세트 젖병 물병 텀블러 세척가능 1.스펙트라 롱타입 젖꼭지솔 2개입 [핑크] 출산/육아 > 소독/살균용품 > 젖병솔'</li></ul>                        |
+| 0.0   | <ul><li>'자외선 장난감 살균기SW-400열풍건조 소독기  출산/육아 > 소독/살균용품 > 장난감소독기'</li><li>'대신 DS-930 앞치마 살균 소독건조기  출산/육아 > 소독/살균용품 > 장난감소독기'</li><li>'소독 캐비닛 양문형 교재 살균기 자외선 UV 컵 소독기 700-GX1 이중문 및 이중서랍(나노대리석) 출산/육아 > 소독/살균용품 > 장난감소독기'</li></ul>                                            |
+| 1.0   | <ul><li>'젖병건조대 수납박스 물컵 젖병선반 보관함 브라운/2318 출산/육아 > 소독/살균용품 > 젖병건조대'</li><li>'뉴코코맘 프리미엄 젖병건조대 2단 뚜껑 이유식기건조대 1. 뉴코코맘 젖병건조대 프리미엄_1단 그레이 출산/육아 > 소독/살균용품 > 젖병건조대'</li><li>'마더케이 리필형 롱핸들 실리콘솔 세척용품세트(건조대+리필세척솔+집게) 네이비_젖병솔&젖꼭지솔_인디핑크 출산/육아 > 소독/살균용품 > 젖병건조대'</li></ul>          |
+| 6.0   | <ul><li>'실리콘 젖병소독집게 분유 열탕 가위 건조기 살균 수저받침_올리브그레이 출산/육아 > 소독/살균용품 > 젖병집게'</li><li>'더굿즈 다용도 젖병 소독 집게 핑크 핑크 출산/육아 > 소독/살균용품 > 젖병집게'</li><li>'젖병집게 젖병소독집게 아기 유아 다용도 페블식탁매트(조약돌)_골드크림 출산/육아 > 소독/살균용품 > 젖병집게'</li></ul>                                                         |
+| 4.0   | <ul><li>'오스람 실바니아 살균램프 4W 젖병소독기 자외선램프 UV램프  출산/육아 > 소독/살균용품 > 젖병소독기용품'</li><li>'스펙트라 젖병소독기호환 UV 램프 1개입 4W / 실바니아  출산/육아 > 소독/살균용품 > 젖병소독기용품'</li><li>'효성 자외선살균램프 20W UVC 살균등 젖병소독기 컵소독기 식기소독기 1개 G20T8  출산/육아 > 소독/살균용품 > 젖병소독기용품'</li></ul>                              |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9978   |
+
+## 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_bc7")
+# Run inference
+preds = model("비앤비 유아 아기 젖병세정제 거품형 용기  출산/육아 > 소독/살균용품 > 젖병세정제")
+```
+
+<!--
+### 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   | 13.7494 | 25  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 70                    |
+| 1.0   | 70                    |
+| 2.0   | 70                    |
+| 3.0   | 70                    |
+| 4.0   | 19                    |
+| 5.0   | 70                    |
+| 6.0   | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0116  | 1    | 0.4771        | -               |
+| 0.5814  | 50   | 0.4546        | -               |
+| 1.1628  | 100  | 0.1706        | -               |
+| 1.7442  | 150  | 0.0404        | -               |
+| 2.3256  | 200  | 0.0101        | -               |
+| 2.9070  | 250  | 0.0093        | -               |
+| 3.4884  | 300  | 0.0082        | -               |
+| 4.0698  | 350  | 0.0062        | -               |
+| 4.6512  | 400  | 0.0001        | -               |
+| 5.2326  | 450  | 0.0           | -               |
+| 5.8140  | 500  | 0.0           | -               |
+| 6.3953  | 550  | 0.0           | -               |
+| 6.9767  | 600  | 0.0           | -               |
+| 7.5581  | 650  | 0.0           | -               |
+| 8.1395  | 700  | 0.0           | -               |
+| 8.7209  | 750  | 0.0           | -               |
+| 9.3023  | 800  | 0.0           | -               |
+| 9.8837  | 850  | 0.0           | -               |
+| 10.4651 | 900  | 0.0           | -               |
+| 11.0465 | 950  | 0.0           | -               |
+| 11.6279 | 1000 | 0.0           | -               |
+| 12.2093 | 1050 | 0.0           | -               |
+| 12.7907 | 1100 | 0.0           | -               |
+| 13.3721 | 1150 | 0.0           | -               |
+| 13.9535 | 1200 | 0.0           | -               |
+| 14.5349 | 1250 | 0.0           | -               |
+| 15.1163 | 1300 | 0.0           | -               |
+| 15.6977 | 1350 | 0.0           | -               |
+| 16.2791 | 1400 | 0.0           | -               |
+| 16.8605 | 1450 | 0.0           | -               |
+| 17.4419 | 1500 | 0.0           | -               |
+| 18.0233 | 1550 | 0.0           | -               |
+| 18.6047 | 1600 | 0.0           | -               |
+| 19.1860 | 1650 | 0.0           | -               |
+| 19.7674 | 1700 | 0.0           | -               |
+| 20.3488 | 1750 | 0.0           | -               |
+| 20.9302 | 1800 | 0.0           | -               |
+| 21.5116 | 1850 | 0.0           | -               |
+| 22.0930 | 1900 | 0.0           | -               |
+| 22.6744 | 1950 | 0.0           | -               |
+| 23.2558 | 2000 | 0.0           | -               |
+| 23.8372 | 2050 | 0.0           | -               |
+| 24.4186 | 2100 | 0.0           | -               |
+| 25.0    | 2150 | 0.0           | -               |
+| 25.5814 | 2200 | 0.0           | -               |
+| 26.1628 | 2250 | 0.0           | -               |
+| 26.7442 | 2300 | 0.0           | -               |
+| 27.3256 | 2350 | 0.0           | -               |
+| 27.9070 | 2400 | 0.0           | -               |
+| 28.4884 | 2450 | 0.0           | -               |
+| 29.0698 | 2500 | 0.0           | -               |
+| 29.6512 | 2550 | 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

@@ -0,0 +1,29 @@
+{
+  "_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

@@ -0,0 +1,10 @@
+{
+  "__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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+oid sha256:00398005c3f16b06e75b5eee99f9faaabdce88e2f4fe45b5c8ade3ff3c49a383
+size 442494816

model_head.pkl

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+size 43935

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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+{
+  "bos_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "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]"
+}