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: 폭스밸리 프리미엄 자세교정밴드 말린 어깨 목 굽은등 라운드숄더 일자 바른 체형 교정기 M+L 폭스밸리
+- text: 올그린 무릎 보조기 MCL 니케이지 인대 연골 보호대 수술후 의료용 니케이지_블루_XL 올그린
+- text: 통풍형 목보호대 쿨링 경추 목디스크 목쿠션 거북목 여성용 hilala115
+- text: THEPURE 목보호대 거북목 자세교정기 보조기 지지대 봄여름가을겨울 02. UIS-03_S 48CM 선셋
+- text: 필라델피아 목보호대 SM-001 사이즈선택 경추보호대 릴렉스 목해먹 목스트레칭 목견인기 일자목 디아
+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: 0.8887880986937591
+      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:** 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                                                                                                                                                                                                                                           |
+|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 6.0   | <ul><li>'반깁스 반기브스 다리보조기 골절 다리 수술 고정 오른발 ㅡ 기본 모델 올바른해외직구샵'</li><li>'아오스 의료용 무릎보호대 124 MCL [0002]S 왼쪽용 CJONSTYLE'</li><li>'원코어 발보조기 끌림방지 보호대 재활 장비 발목보조기 발지지대 왼발_L 키위프'</li></ul>                                                                    |
+| 2.0   | <ul><li>'이화 밸포밴드 팔걸이 견지대 성인용 21061458 M 비앤비(best & BEST)'</li><li>'허리보조기 허리 척추 보호 의료용 AOS-460 여_XXL 링쿠'</li><li>'울트라슬링 어깨보호대 팔걸이 어깨수술 울트라실링 팔깁스 K 타입 디엘아이'</li></ul>                                                                               |
+| 5.0   | <ul><li>'전동기립기 하반신 경사 스탠딩 편마비 침대 보조기 수동 높이 조절 + 사륜 + 식탁 쇼핑의품격001'</li><li>'물리치료기계 재활기 가정용 근육 도수 허리 승모근 단일 모델 에오인'</li><li>'환자 전동 침대 의료용 가정용 병원 전동기립기 보조 화이트 97cmx45cmx202cm 연림스토어'</li></ul>                                                       |
+| 0.0   | <ul><li>'미제 재활 고무찰흙 퓨티 (살색/노랑/빨강/초록/파랑) 초록 텔레그라프'</li><li>'손가락 재활 장갑 편마비 손재활 운동 로봇 기구 주황색 미러링된 왼손 M 구구상회'</li><li>'건강누리 말렛핑거스프린트 리필(Mallet Finger Splint Refill) 오픈형7호 단위:팩(5개)  (주)엠디오씨'</li></ul>                                                |
+| 4.0   | <ul><li>'통증바이 남녀공용 바른 자세밴드 3XL (허리둘레 ... 1개 XL (허리둘레 30~32인치) × 1개 이위에'</li><li>'굽은어깨 굽은등 어깨 허리 바른 자세 밴드 라운드숄더 펴주는 XXXL 아이엠어굿맨'</li><li>'(발음교정기 돌돌이) 스카이블루 학생용 영어 국어 발음연습 발음교정 하드(스카이블루) (주)애니덴'</li></ul>                                         |
+| 1.0   | <ul><li>'[의료기기](반값딜) 넥가디언 거북목 디스크 교정기 쿠션형 견인기 단독 (밤색) 852헤르츠'</li><li>'바른 목 미라클 고급형 보호대 밴드  젬마줌마'</li><li>'[OFLP1Q84]허리E UP 통기성 에어메디칼 견인요 S 27인치이하／FREE sellerhub'</li></ul>                                                                       |
+| 3.0   | <ul><li>'도고 렉스타 허벅지형 205 압박용밴드 의료용 압박스타킹 혈액순환 다리붓기 개선 의료기기 중압 (4)265발트임_살색_XL (주)도고메디칼'</li><li>'[GIN383R]종아리 압박밴드 스타킹 다리 간호사 수면 관리 블랙／FREE sellerhub'</li><li>'Duomed Advantage, 15-20 mmHg, 종아리 높이, 오픈 토 Small_Almond 수 스토리(SU STORY)'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.8888 |
+
+## 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_lh21")
+# Run inference
+preds = model("통풍형 목보호대 쿨링 경추 목디스크 목쿠션 거북목 여성용 hilala115")
+```
+
+<!--
+### 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   | 9.96   | 21  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 50                    |
+| 2.0   | 50                    |
+| 3.0   | 50                    |
+| 4.0   | 50                    |
+| 5.0   | 50                    |
+| 6.0   | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0182  | 1    | 0.4265        | -               |
+| 0.9091  | 50   | 0.3097        | -               |
+| 1.8182  | 100  | 0.0765        | -               |
+| 2.7273  | 150  | 0.0638        | -               |
+| 3.6364  | 200  | 0.0434        | -               |
+| 4.5455  | 250  | 0.0035        | -               |
+| 5.4545  | 300  | 0.0002        | -               |
+| 6.3636  | 350  | 0.0001        | -               |
+| 7.2727  | 400  | 0.0001        | -               |
+| 8.1818  | 450  | 0.0001        | -               |
+| 9.0909  | 500  | 0.0001        | -               |
+| 10.0    | 550  | 0.0001        | -               |
+| 10.9091 | 600  | 0.0001        | -               |
+| 11.8182 | 650  | 0.0001        | -               |
+| 12.7273 | 700  | 0.0001        | -               |
+| 13.6364 | 750  | 0.0001        | -               |
+| 14.5455 | 800  | 0.0001        | -               |
+| 15.4545 | 850  | 0.0001        | -               |
+| 16.3636 | 900  | 0.0           | -               |
+| 17.2727 | 950  | 0.0           | -               |
+| 18.1818 | 1000 | 0.0           | -               |
+| 19.0909 | 1050 | 0.0001        | -               |
+| 20.0    | 1100 | 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_lh",
+  "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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+{
+  "normalize_embeddings": false,
+  "labels": null
+}

model.safetensors

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model_head.pkl

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modules.json

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+    "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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+}