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

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+---
+base_model: mini1013/master_domain
+library_name: setfit
+metrics:
+- metric
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 원목 듀얼 모니터받침대 미송 B타입 M  주식회사 제이테크(J-TECH)
+- text: 대형 게이밍모니터거치대 카멜마운트 PMA-2U USB지원 32인치 거치가능 모니터암 블랙 (주)순천물류
+- text: 카멜마운트 CMA2V 듀얼 벽면 밀착형 상하 거치대 모니터암 블랙 주식회사 카멜인터내셔널
+- text: 알파스캔 AOC AM400 시에라 블루 싱글 모니터암 컴퓨터 27인치 32인치 브라켓 AM400 로즈쿼츠 주식회사 멀티스캔텍
+- text: 카멜인터내셔널 카멜마운트 고든 DMA-DSS 벽면 밀착형 듀얼 모니터암  (주)아이티엔조이
+inference: true
+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: metric
+      value: 0.8586497890295358
+      name: Metric
+---
+
+# 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                                                                                                                                                                                                                     |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 5     | <ul><li>'(주)근호컴 [리버네트워크]USB 2.0 리피터 전용 전원 어댑터 (NX-USBEXPW)  (주)근호컴'</li><li>'NEXI 넥시 정품 NX-USBEXPW아답터 (NX0284)  (주)유니정보통신'</li><li>'국산 12V 5A 모니터 아답터 ML-125A  헤라유통'</li></ul>                                               |
+| 3     | <ul><li>'카멜마운트 GDA3 고든 디자인 모니터 거치대 모니터암 듀얼 블랙 주식회사 카멜인터내셔널'</li><li>'카멜 CA2 화이트 나뭉'</li><li>'마루느루 마운트뷰 MV-G1A  셜크'</li></ul>                                                                                                 |
+| 0     | <ul><li>'셋탑 박스 게임기 리모컨 수납 TV 모니터 TOP 공간 선반 공유기 거치대  아이디어윙'</li><li>'리모컨수납 TV 모니터 TOP 공간선반 Black 연상연하'</li><li>'애니포트 TV거치대 엘마운트 다용도 멀티 선반 S900  이스토어'</li></ul>                                                               |
+| 1     | <ul><li>'ELLOVEN 엘로벤 모니터스탠드+서랍 엘로벤 스탠드 앤트러 (804.851.02) 랩앤툴스'</li><li>'썬엔원 유보드 모니터받침대 U-BOARD Basic [화이트] 강화유리 / 유리색상: 투명 블랙 (주)세븐앤씨'</li><li>'앱코 MES100 사이드 폴딩 모니터 받침대 선반 받침 서랍 데스크 정리 블랙 앱코 MES100 블랙 (주)드림팩토리샵'</li></ul> |
+| 2     | <ul><li>'아이존아이앤디 EZ MSM-10  아이러브드라이브(I Love Drive)'</li><li>'아이존아이앤디 EZ MSM-10/EZ MSM-10/조절브라켓/모니터스탠드/높낮이조절/조절스탠드/모니터홀타입/홀타입스탠드 EZ MSM-10 기쁘다희샵'</li><li>'루나랩 베사확장브라켓 200x100 200x200  주식회사 루나'</li></ul>                    |
+| 4     | <ul><li>'지클릭커 휴 쉴드 PET 부착식 정보보호 모니터 보안필름 22인치  가이드컴퓨터'</li><li>'힐링쉴드 11890340 22인치 모니터 블루라이트차단 보호필름 거치식 조립형 양면필터  온라인정품인증점'</li><li>'지클릭커 휴 쉴드 PET 부착식 정보보호 모니터 보안필름 22인치  주식회사 리더샵'</li></ul>                               |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.8586 |
+
+## 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_el10")
+# Run inference
+preds = model("원목 듀얼 모니터받침대 미송 B타입 M  주식회사 제이테크(J-TECH)")
+```
+
+<!--
+### 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   | 4   | 9.9725 | 24  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 50                    |
+| 1     | 50                    |
+| 2     | 13                    |
+| 3     | 50                    |
+| 4     | 5                     |
+| 5     | 50                    |
+
+### Training Hyperparameters
+- batch_size: (512, 512)
+- num_epochs: (20, 20)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 40
+- body_learning_rate: (2e-05, 2e-05)
+- head_learning_rate: 2e-05
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch   | Step | Training Loss | Validation Loss |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0286  | 1    | 0.4958        | -               |
+| 1.4286  | 50   | 0.0386        | -               |
+| 2.8571  | 100  | 0.0016        | -               |
+| 4.2857  | 150  | 0.0001        | -               |
+| 5.7143  | 200  | 0.0           | -               |
+| 7.1429  | 250  | 0.0           | -               |
+| 8.5714  | 300  | 0.0           | -               |
+| 10.0    | 350  | 0.0           | -               |
+| 11.4286 | 400  | 0.0001        | -               |
+| 12.8571 | 450  | 0.0           | -               |
+| 14.2857 | 500  | 0.0001        | -               |
+| 15.7143 | 550  | 0.0           | -               |
+| 17.1429 | 600  | 0.0001        | -               |
+| 18.5714 | 650  | 0.0           | -               |
+| 20.0    | 700  | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0.dev0
+- Sentence Transformers: 3.1.1
+- Transformers: 4.46.1
+- PyTorch: 2.4.0+cu121
+- Datasets: 2.20.0
+- Tokenizers: 0.20.0
+
+## 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_el",
+  "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": "4.46.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.46.1",
+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+{
+  "labels": null,
+  "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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+}