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: 이소닉 MR-120 8GB 동의 화이트선셋
+- text: 이소닉 PCM-007 2G 무손실 PCM녹음 간단한사용법 볼펜녹음기 고성능 인터뷰 회의녹음/강의녹음/비밀녹음/녹취기/전화녹음 증거보존
+    초소형녹음기+USB메모리+MP3 원거리녹음 2GB 진경전자
+- text: ICD-PX470 4GB 속기사녹음기 비밀녹음기 장시간  동백
+- text: 베스타 전자사전 BK-100  핫앤쿨 (HNC)
+- text: TASCAM 4트랙 디지털 오디오 레코더 DR-40X  고운소리사
+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: 0.9616204690831557
+      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>'레벤트 백색소음기 무드등 RM20  (주)포트리스'</li><li>'레벤트 RM20 LED램프 무드등 백색소음기 수면등  (주)엠글로벌스'</li><li>'레벤트 신생아 백색소음기 화이트노이즈 수유 무드등 RM20  주식회사매니악'</li></ul>                                                                                                       |
+| 1     | <ul><li>'아이플라이텍 스캔톡 영어 번역기 동시 통역기 AI 인공지능 AI-DSA-001  펫 허브'</li><li>'아이플라이텍 스캔톡 영어 번역기 통역기 AI 인공지능 어학기 (3종 액세사리 무료 ) 그린_액정보호필름/젤리케이스/하드케이스 주식회사 엑스오비'</li><li>'슈피겐 인공지능 음성 번역기 동시 통역기 포켓토크 영어 일본어 중국어 베트남어 해외여행 화이트(eSIM) /ESE00001 주식회사 슈피겐코리아'</li></ul> |
+| 0     | <ul><li>'브리츠 BZ-VR1000 보이스 레코드 회의 강의 녹음  주식회사 투데이플러스'</li><li>'머레이 손목 시계형 보이스 레코더 RV-1000  알앤컴퍼니 (R&Company)'</li><li>'이소닉 MR-1000 8GB 장시간 특수녹음기 초소형 보이스레코더 증거 대화 강의  (주)포고텍'</li></ul>                                                                    |
+| 2     | <ul><li>'베스타 한자 일본어 국어 중국어 어학 영영 한영 옥편 영어 사전 전자사전 BK-100  (주)삼신이앤비'</li><li>'베스타 BK-200J 전자사전 일본어특화 전자사전 필기인식 메모리+액정필름+아답타  주식회사 마루엔'</li><li>'베스타 BK-200 8GB 전자사전 번역 회화  주식회사 모핏코리아'</li></ul>                                                            |
+| 3     | <ul><li>'교보문고 sam 10 Plus  셜크'</li><li>'교보 이북리더기 샘10플러스 sam 10 Plus  프린지'</li><li>'리디페이퍼 4 RIDIPAPER 4 리디 전용 전자책 이북리더기 전자책 리더기 (7인치, wifi, 블루투스, 방수) 블랙 리디 주식회사'</li></ul>                                                                                 |
+| 4     | <ul><li>'스코코 리디북스 리디페이퍼 4세대 무광 전신 외부보호필름 3종  (주)스코코'</li><li>'세이펜전용충전기 / 5핀 C타입 분리형충전기 세이펜5핀충전기 (주)세티'</li><li>'스캔톡 EPU 액정 보호 필름 2매입 2매입(벌크용) 주식회사 포유컴퍼니'</li></ul>                                                                                        |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9616 |
+
+## 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_el23")
+# Run inference
+preds = model("이소닉 MR-120 8GB 동의 화이트선셋")
+```
+
+<!--
+### 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   | 10.4144 | 23  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 50                    |
+| 1     | 42                    |
+| 2     | 50                    |
+| 3     | 11                    |
+| 4     | 12                    |
+| 5     | 16                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0345  | 1    | 0.4957        | -               |
+| 1.7241  | 50   | 0.0279        | -               |
+| 3.4483  | 100  | 0.0001        | -               |
+| 5.1724  | 150  | 0.0001        | -               |
+| 6.8966  | 200  | 0.0001        | -               |
+| 8.6207  | 250  | 0.0001        | -               |
+| 10.3448 | 300  | 0.0           | -               |
+| 12.0690 | 350  | 0.0           | -               |
+| 13.7931 | 400  | 0.0           | -               |
+| 15.5172 | 450  | 0.0           | -               |
+| 17.2414 | 500  | 0.0           | -               |
+| 18.9655 | 550  | 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"
+  ],
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+  "bos_token_id": 0,
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+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "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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+  "normalize_embeddings": false
+}

model.safetensors

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model_head.pkl

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modules.json

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+    "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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+}