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: 얌뚱이 칼라고무밴드 머리끈 헤어밴드 고무줄 유아 아동 여아 어린이집 검정 색 대용량 대핑크30g 얌뚱이
+- text: 파티 벨벳 심플 왕리본핀 반묶음핀 30칼라 와인_납작핀대 릴리트리
+- text: 넓은 여자 머리띠 윤아 와이드 귀안아픈 면 니트 터번 T-도톰쫀득_핑크 모스블랑
+- text: 얼굴소멸 히메컷 가발 앞머리 사이드뱅 옆머리 부분 가발 애교머리 풀뱅 규리 민니 옆2p-라이트브라운 굿모닝리테일
+- text: 13cm 빅사이즈 대왕 숱많은 긴 머리 꼬임 올림머리 집게핀 3/ 그라데이션 매트_브라운 블렌디드
+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: 0.9541466176054345
+      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:** 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                                                                                                                                                                                                                         |
+|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 2.0   | <ul><li>'(신세계김해점)에트로 프로푸미 헤어밴드 01046 05 1099 ONE SIZE 신세계백화점'</li><li>'Baby scrunchie 3set (White/Beige/Black) 빌라드실크 곱창밴드 미니 실크 스크런치 세트  주식회사 실크랩'</li><li>'간단 헤어밴드 미키마우스 머리띠 왕 리본 남자 캐릭터 플라스틱 반짝이 1-4. 글리터 / 블랙 아이드림'</li></ul> |
+| 1.0   | <ul><li>'위즈템 헤어밴드 진주 크리스탈 머리끈 연핑크 파파닐'</li><li>'둥근고무줄 (대용량) 칼라 금 은 천고무줄 벌크 탄성끈 가는줄 /굵은줄 02. 대용량 굵은줄(2.5mmx60M)_금색 마이1004(MY1004)'</li><li>'천연 컬러 고무 끈 고무줄 생활용품 3M 하늘색 제이앤제이웍스'</li></ul>                                         |
+| 0.0   | <ul><li>'인모 남자가발 정수리 커버 자연스러운 O형 커버가발 마오_인모14X14 하이윤'</li><li>'얼굴소멸 히메컷 가발 앞머리 사이드뱅 옆머리 부분 히메컷 사이드뱅 옆2p-내츄럴브라운 와우마켓'</li><li>'얼굴소멸 히메컷 가발 앞머리 사이드뱅 옆머리 부분 옆2p-라이트브라운 이지구'</li></ul>                                              |
+| 4.0   | <ul><li>'무지 12컬러 심플 리본 바나나핀 핫핑크 하얀당나귀'</li><li>'네임핀/이름핀/네임브로치/어린이집선물/유치원선물 5글자(영어6자~8자)_별_브로치 쭈스타'</li><li>'메탈 셀룰로오스 꼬임 올림머리 집게핀 사각4170_아이스옐로우 엑스엔서'</li></ul>                                                                   |
+| 3.0   | <ul><li>'웨딩 드레스 유니크 베일 셀프 촬영 소품 대형 리본 잡지 모델 패션쇼 장식 액세서리 머리 04.파란 (핸드메이드) 더비공이(TheB02)'</li><li>'슈퍼 요정 흰색 보석 웨딩 헤어 타워 공연 여행 T15-a_선택하세요 아토버디'</li><li>'뿌리볼륨집게3p  건강드림'</li></ul>                                                  |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9541 |
+
+## 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_ac16")
+# Run inference
+preds = model("파티 벨벳 심플 왕리본핀 반묶음핀 30칼라 와인_납작핀대 릴리트리")
+```
+
+<!--
+### 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   | 9.956  | 24  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 50                    |
+| 3.0   | 50                    |
+| 4.0   | 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 |
+|:-----:|:----:|:-------------:|:---------------:|
+| 0.025 | 1    | 0.4499        | -               |
+| 1.25  | 50   | 0.2065        | -               |
+| 2.5   | 100  | 0.0446        | -               |
+| 3.75  | 150  | 0.0001        | -               |
+| 5.0   | 200  | 0.0           | -               |
+| 6.25  | 250  | 0.0001        | -               |
+| 7.5   | 300  | 0.0           | -               |
+| 8.75  | 350  | 0.0           | -               |
+| 10.0  | 400  | 0.0           | -               |
+| 11.25 | 450  | 0.0           | -               |
+| 12.5  | 500  | 0.0           | -               |
+| 13.75 | 550  | 0.0           | -               |
+| 15.0  | 600  | 0.0           | -               |
+| 16.25 | 650  | 0.0           | -               |
+| 17.5  | 700  | 0.0           | -               |
+| 18.75 | 750  | 0.0           | -               |
+| 20.0  | 800  | 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_ac",
+  "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": "4.46.1",
+  "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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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
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+  {
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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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+}