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 +1,247 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 오픈페이스 스쿠터 오토바이 라이트 사계절 LED 남남녀공용 바이크 여성용 헬멧 스포츠/레저>오토바이/스쿠터>오토바이용품>헬멧
+- text: 코미네 오토바이 핀 잠금 장치 도난 방지 디스크락 열쇠 스포츠/레저>오토바이/스쿠터>오토바이부품>잠금장치
+- text: 방수 스즈끼 오토바이 방한복 배달 스즈키 우주복 겨울 오토바이 방한용품 스포츠/레저>오토바이/스쿠터>오토바이의류/잡화>상하세트
+- text: 오토바이 스쿠터 리어백 트렁크 소형 탑박스 두꺼운 범용 스포츠/레저>오토바이/스쿠터>오토바이용품>기타오토바이용품
+- text: 하템몰 오토바이 헬멧 블루투스 채터박스 BiT-3S 인터콤 스포츠/레저>오토바이/스쿠터>오토바이용품>기타오토바이용품
+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                                                                                                                                                                                                                                                |
+|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0.0   | <ul><li>'KR모터스 그란투스 125 스쿠터 스포츠/레저>오토바이/스쿠터>스쿠터>일반스쿠터'</li><li>'니키125 걸프 에디션 스쿠터 2024년 스포츠/레저>오토바이/스쿠터>스쿠터>일반스쿠터'</li><li>'AU테크 에코로 포니  전기스쿠터 12Ah 스포츠/레저>오토바이/스쿠터>스쿠터>전동스쿠터'</li></ul>                                                                 |
+| 3.0   | <ul><li>'오토바이 여름헬멧 오픈페이스 반모 경량 클래식 헬멧 빈티지 스틸 레트로 스포츠/레저>오토바이/스쿠터>오토바이용품>헬멧'</li><li>'레트로 바이크 헬멧 할리데이비슨 오토바이 감성 보 -레트로 미러 스포츠/레저>오토바이/스쿠터>오토바이용품>헬멧'</li><li>'남녀공용 편광 변색 선글라스 꽃가루 알레르기 방지 윈드 고글 UV400 보호 TR90 스포츠/레저>오토바이/스쿠터>오토바이용품>기타오토바이용품'</li></ul> |
+| 4.0   | <ul><li>'코미네 3핑거 여름 반장갑 메쉬 글러브 BLACK-RED GK-260 스포츠/레저>오토바이/스쿠터>오토바이의류/잡화>바이크장갑'</li><li>'맥슬러 케블라 라이딩청바지 오토바이바지 M-2073 스포츠/레저>오토바이/스쿠터>오토바이의류/잡화>하의'</li><li>'오토바이 방한복 배달 남녀공용 일체형 방수 바이크 라이딩 배달우주복 스포츠/레저>오토바이/스쿠터>오토바이의류/잡화>상하세트'</li></ul>           |
+| 2.0   | <ul><li>'피렐리 엔젤 스쿠터 더뉴 PCX 뒤 타이어 120 스포츠/레저>오토바이/스쿠터>오토바이부품>기타오토바이부품'</li><li>'투스크 메가비트 레이디얼 타이어 28x1015 혼다 탈론 폭스 라이브 밸브용 1000X 스포츠/레저>오토바이/스쿠터>오토바이부품>기타오토바이부품'</li><li>'혼다 PCX125 브레이크 캘리퍼 핀 45215-KPH-951 스포츠/레저>오토바이/스쿠터>오토바이부품>기타오토바이부품'</li></ul> |
+| 1.0   | <ul><li>'CFORCE 450 4륜 오토바이 스포츠/레저>오토바이/스쿠터>오토바이'</li><li>'ATV-A형 사륜오토바이 스포츠/레저>오토바이/스쿠터>오토바이'</li><li>'BMW F850GS 오토바이 스포츠/레저>오토바이/스쿠터>오토바이'</li></ul>                                                                                                 |
+| 5.0   | <ul><li>'자이로콥 G에스 전동투휠 4.4Ah 스포츠/레저>오토바이/스쿠터>전동휠'</li><li>'킹송 외발휠 16S 840Wh 스포츠/레저>오토바이/스쿠터>전동휠'</li><li>'나인봇 엘리트 플러스 전동휠 10Ah 스포츠/레저>오토바이/스쿠터>전동휠'</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_sl23")
+# Run inference
+preds = model("코미네 오토바이 핀 잠금 장치 도난 방지 디스크락 열쇠 스포츠/레저>오토바이/스쿠터>오토바이부품>잠금장치")
+```
+
+<!--
+### 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   | 8.3719 | 19  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 70                    |
+| 1.0   | 70                    |
+| 2.0   | 70                    |
+| 3.0   | 70                    |
+| 4.0   | 70                    |
+| 5.0   | 13                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0141  | 1    | 0.4943        | -               |
+| 0.7042  | 50   | 0.4622        | -               |
+| 1.4085  | 100  | 0.1572        | -               |
+| 2.1127  | 150  | 0.0427        | -               |
+| 2.8169  | 200  | 0.0006        | -               |
+| 3.5211  | 250  | 0.0           | -               |
+| 4.2254  | 300  | 0.0           | -               |
+| 4.9296  | 350  | 0.0           | -               |
+| 5.6338  | 400  | 0.0           | -               |
+| 6.3380  | 450  | 0.0           | -               |
+| 7.0423  | 500  | 0.0           | -               |
+| 7.7465  | 550  | 0.0           | -               |
+| 8.4507  | 600  | 0.0           | -               |
+| 9.1549  | 650  | 0.0           | -               |
+| 9.8592  | 700  | 0.0           | -               |
+| 10.5634 | 750  | 0.0           | -               |
+| 11.2676 | 800  | 0.0           | -               |
+| 11.9718 | 850  | 0.0           | -               |
+| 12.6761 | 900  | 0.0001        | -               |
+| 13.3803 | 950  | 0.0           | -               |
+| 14.0845 | 1000 | 0.0           | -               |
+| 14.7887 | 1050 | 0.0           | -               |
+| 15.4930 | 1100 | 0.0           | -               |
+| 16.1972 | 1150 | 0.0           | -               |
+| 16.9014 | 1200 | 0.0           | -               |
+| 17.6056 | 1250 | 0.0           | -               |
+| 18.3099 | 1300 | 0.0           | -               |
+| 19.0141 | 1350 | 0.0           | -               |
+| 19.7183 | 1400 | 0.0           | -               |
+| 20.4225 | 1450 | 0.0           | -               |
+| 21.1268 | 1500 | 0.0           | -               |
+| 21.8310 | 1550 | 0.0           | -               |
+| 22.5352 | 1600 | 0.0           | -               |
+| 23.2394 | 1650 | 0.0           | -               |
+| 23.9437 | 1700 | 0.0           | -               |
+| 24.6479 | 1750 | 0.0           | -               |
+| 25.3521 | 1800 | 0.0           | -               |
+| 26.0563 | 1850 | 0.0           | -               |
+| 26.7606 | 1900 | 0.0           | -               |
+| 27.4648 | 1950 | 0.0           | -               |
+| 28.1690 | 2000 | 0.0           | -               |
+| 28.8732 | 2050 | 0.0           | -               |
+| 29.5775 | 2100 | 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_sl_org_gtcate",
+  "architectures": [
+    "RobertaModel"
+  ],
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+  "bos_token_id": 0,
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+  "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
+}

model.safetensors

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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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+  "tokenizer_class": "BertTokenizer",
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+  "truncation_strategy": "longest_first",
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+}