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 +1,239 @@
+---
+base_model: mini1013/setfit_robeta_base_s_domain
+library_name: setfit
+metrics:
+- metric
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: '[7매입/14매입] 마이크로바이옴 비건 모델링팩 모공 수축 수분 진정 마스크 팩 1set 1. 모델링팩 1set (7매입) 주식회사
+    에이치티오인터내셔널'
+- text: 더후 공진향 인양 넥앤페이스 탄력 리페어75ml 옵션없음 씨플랩
+- text: 빌리프 슈퍼 나이츠-리제너레이팅 나이트 마스크 75ml 옵션없음 라임쇼핑
+- text: 수이스킨 편안한 진정초 시트 마스크 5개입 × 1개 민물유통
+- text: 참존 지안 극결 콘트롤 크림 225ml (리뉴얼제품) 옵션없음 슈슈
+inference: true
+model-index:
+- name: SetFit with mini1013/setfit_robeta_base_s_domain
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: metric
+      value: 0.7714285714285715
+      name: Metric
+---
+
+# SetFit with mini1013/setfit_robeta_base_s_domain
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/setfit_robeta_base_s_domain](https://huggingface.co/mini1013/setfit_robeta_base_s_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/setfit_robeta_base_s_domain](https://huggingface.co/mini1013/setfit_robeta_base_s_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:** 8 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                                                                                                                                                                                                                                        |
+|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 4     | <ul><li>'메노킨 30초 퀵 버블 마스크 3종세트 (선물포장+세안밴드 ) 옵션없음 주식회사 포레스트에비뉴'</li><li>'라네즈 립 슬리핑 마스크EX 20g 각질케어 수면팩 자몽 20g (주)아모레퍼시픽'</li><li>'빌리프 슈퍼 나이츠 멀티 비타민 마스크 75ml 옵션없음 라임쇼핑'</li></ul>                                                                 |
+| 5     | <ul><li>'[러쉬]국내제조 컵 오 커피 325g - 페이스 마스크 / 커피팩 옵션없음 주식회사 러쉬코리아'</li><li>'올리고더미 딥클렌징 마스크 110ml+25ml+캐릭터파우치+스파츌라 리바이탈라이징 25ml(5mlx5매) [영양] 주식회사더마셀'</li><li>'스킨푸드 라벤더 푸드마스크 120g 02 딸기슈가_1개 미루코스메틱'</li></ul>                                      |
+| 3     | <ul><li>'[에스테프로] 708 티트리 알게러버 2000ml 고무마스크 모델링팩 피부미용사 실기준비물 에스테맥스 옵션없음 무제'</li><li>'린제이 프리미엄 쿨 티트리 모델링 마스크 고무팩 820g 11203606 옵션없음 세론세론'</li><li>'메디플라워 대용량 네이처 허브 모델링팩 쿨티트리 500g+히알루론산 멀티 부스터 500ml 쿨 티트리 500g_히알루론산 멀티 부스터 (주)메디플라워'</li></ul> |
+| 2     | <ul><li>'바이오던스 바이오 콜라겐 리얼 딥 마스크 1매 옵션없음 에스제이유통'</li><li>'아리얼 세븐데이즈 마스크 카렌듈라 P 1매 도매가능 옵션없음 앱스'</li><li>'바노바기 밀크 씨슬 리페어 시카 퀵 마스크 플러스(30매) 제품 구매 시 +시카 폼 미니어처 1EA 증정 주식회사 바노바기'</li></ul>                                                         |
+| 6     | <ul><li>'포잇 카카두 허니씨 톤업패드 60매 3개 옵션없음 스타일리시케이'</li><li>'미팩토리 3단 돼지 코팩, 3개입, 6개 3개입 × 6개 제이케이컴퍼니'</li><li>'올가드 아웃도어 패치 2패치 4매 3개 옵션없음 스타일리시케이'</li></ul>                                                                                          |
+| 0     | <ul><li>'오릭스마사지크림(450ml) 옵션없음 고호성'</li><li>'모든순간 살구씨 찌든때 비타민 피지 살구스크럽 500g 옵션없음 데이포유'</li><li>'필로티카 시트랑스 겔 마스크 270ml 필로티카 시트랑스 겔 마스크 270ml 오드엘르'</li></ul>                                                                                      |
+| 1     | <ul><li>'코 숨쉬기 입벌림 방지 호흡 유도 수면 보조 밴드 코골이 기구 용품 개선 제품 완화 기기 옵션없음 구씨네유통'</li><li>'희재감성 라인정리 이중턱 밴드 관리 리프팅 안면윤곽 단품(1701) 희재감성몰'</li><li>'50 개/몫 화이트 판지 카드 상자 포장 빈 마스크 더블 오픈 01 WHITE_06 9.2x8x5cm-50pcs 글로젠'</li></ul>                             |
+| 7     | <ul><li>'ECLADO 에끌라두 로얄 밀크 프로틴 페이셜 마스크 70g 단백질 필오프팩 1개 옵션없음 주식회사 아워스'</li><li>'비건이펙트 슬로우 앤 에이징 저분자 콜라겐 물광 랩마스크 80ml 옵션없음 (주)부스트랩'</li><li>'메디필 레드락토 콜라겐 랩핑마스크 70ml 물광 리프팅 팩 옵션없음 웬디스룸'</li></ul>                                                |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.7714 |
+
+## 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_bt2")
+# Run inference
+preds = model("수이스킨 편안한 진정초 시트 마스크 5개입 × 1개 민물유통")
+```
+
+<!--
+### 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   | 9.8591 | 27  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 90                    |
+| 1     | 78                    |
+| 2     | 88                    |
+| 3     | 95                    |
+| 4     | 94                    |
+| 5     | 90                    |
+| 6     | 84                    |
+| 7     | 34                    |
+
+### Training Hyperparameters
+- batch_size: (512, 512)
+- num_epochs: (20, 20)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 30
+- 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0130  | 1    | 0.4922        | -               |
+| 0.6494  | 50   | 0.2317        | -               |
+| 1.2987  | 100  | 0.0726        | -               |
+| 1.9481  | 150  | 0.033         | -               |
+| 2.5974  | 200  | 0.0322        | -               |
+| 3.2468  | 250  | 0.0056        | -               |
+| 3.8961  | 300  | 0.001         | -               |
+| 4.5455  | 350  | 0.0003        | -               |
+| 5.1948  | 400  | 0.0001        | -               |
+| 5.8442  | 450  | 0.0001        | -               |
+| 6.4935  | 500  | 0.0001        | -               |
+| 7.1429  | 550  | 0.0002        | -               |
+| 7.7922  | 600  | 0.0001        | -               |
+| 8.4416  | 650  | 0.0001        | -               |
+| 9.0909  | 700  | 0.0001        | -               |
+| 9.7403  | 750  | 0.0001        | -               |
+| 10.3896 | 800  | 0.0006        | -               |
+| 11.0390 | 850  | 0.0001        | -               |
+| 11.6883 | 900  | 0.0001        | -               |
+| 12.3377 | 950  | 0.0001        | -               |
+| 12.9870 | 1000 | 0.0           | -               |
+| 13.6364 | 1050 | 0.0           | -               |
+| 14.2857 | 1100 | 0.0           | -               |
+| 14.9351 | 1150 | 0.0           | -               |
+| 15.5844 | 1200 | 0.0           | -               |
+| 16.2338 | 1250 | 0.0           | -               |
+| 16.8831 | 1300 | 0.0           | -               |
+| 17.5325 | 1350 | 0.0           | -               |
+| 18.1818 | 1400 | 0.0           | -               |
+| 18.8312 | 1450 | 0.0           | -               |
+| 19.4805 | 1500 | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0.dev0
+- Sentence Transformers: 3.1.1
+- Transformers: 4.45.1
+- 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

@@ -0,0 +1,29 @@
+{
+  "_name_or_path": "mini1013/setfit_robeta_base_bt_item",
+  "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": "4.45.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.45.1",
+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

@@ -0,0 +1,4 @@
+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b2df1d765584937f36ad5033062cbfdddbe8779d5cdd57ffe1871147b84fa399
+size 442494816

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:137dbc936cca9ebf3024e702ff59e072522026629abaced639f3c195362434ef
+size 50119

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,66 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}