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 +1,228 @@
+---
+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: 코어슬리머 전용 리필패드 6P 2개  롯데아이몰
+- text: 발락 손목 마사지기 안마기 간편한 EMS 반영구적 통증 팔목 마사지 발락 손목 마사지기 세트 (주)엘가니
+- text: '[바이오프로테크]프로텐스 핀타입 대형 저주파패드 2조(RG01)  '
+- text: 성게 탱탱볼 노인복지센터 안마볼 촉각볼 선물 몸신 물리치료 어르신 탱볼_11.탱볼(농구) 워커스
+- text: '[약손드림] 저주파 EMS 어깨 마사지기 미세전류 휴대용 안마기 부모님선물 효도선물 어깨보호대 M(95~100호) 금양리테일 주식회사'
+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: 0.894511760513186
+      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>'예림전자 적외선조사기 전체화이트 필립스 250W 램프 적외선 치료기 아닌 국산 의료기기 01 전체화이트 e청춘'</li><li>'비타그램 필립스 적외선 램프 피부방사기 WGT-8888S  VitaGRAM'</li><li>'원적외선 온열 치료기 한의원 어깨 경추 램프 마사지 MinSellAmount 차류소'</li></ul>                                 |
+| 2.0   | <ul><li>'HWATO 고급형 부항기 14컵  라이프샵'</li><li>'손 사혈부항용 따주기 자 통사혈기 광명사 침 구비 측정 습식 손따주는 체했을때 혈당기 자동 간편  알리몽드투'</li><li>'한솔부항기 신형 소독가능 부항컵 10개 1박스 (사이즈선택1-5호) 한솔부항2호컵 수의료기'</li></ul>                                                |
+| 5.0   | <ul><li>'오므론 저주파 롱 라이프 패드 2p HV-LLPAD-G... 1개 HV-LLPAD-GY × 2개 스위에'</li><li>'코어슬리머 전용 리필패드 6P 3개 [0001]기본상품 CJONSTYLE'</li><li>'클럭 미니 마사지기 리필패드 큰패드 2박스 총6P /DY_MC  멸치쇼핑'</li></ul>                                            |
+| 0.0   | <ul><li>'닥터체크 슬림 X형 테이핑 무릎보호대(좌우겸용 1P) M-중형(630475)  트래이드 씨스템(TRADE SYSTEM)'</li><li>'닥터체크 슬림 X형 테이핑 종아리압박보호대(좌우겸용 1P) M-중형(630499)  태빛ID'</li><li>'국산 의료용 허리보호대 편안하고 부드러운 허리복대 선택01- 001s 허리보호대_XXXL(40~43인치) 대한건강'</li></ul> |
+| 4.0   | <ul><li>'스트라텍 의료용 전침기 4채널 STN-220 저주파자극기 침전기자극기 자석형 (주)오픈메디칼'</li><li>'디웰 저주파 마사지기 버튼형 LB-1803 미니마사지기 휴대용 무선 안마기 일반구매_06.버튼형2��스+대형패드 8매+흡착컵8개 주식회사 청훈'</li><li>'극동저주파 PRO1000 wave GOLD  헬스푸드메디칼'</li></ul>                    |
+| 1.0   | <ul><li>'조은팜 초음파젤 의료용젤 투명5L 1통 무료전달 조은초음파젤5L블루 세븐메디컬'</li><li>'이도팜 소노젤리 투명 블루 5L +250ml 공병 소노겔 초음파젤리 ECG [0001]블루 5L CJONSTYLE'</li><li>'세니피아 에코소닉 초음파젤 투명 250mL 12개x4통 1박스 소노젤리 피부과 산부인과용  세븐메디컬'</li></ul>                   |
+| 3.0   | <ul><li>'클럭 미니 마사지기SE  YGGlobal'</li><li>'온열/공기압/원적외선/저주파 4중케어 무릎마사지기[공기압 온열 원적외선 진동기능]안마기 05.클레버 마사지건 SR825 수련닷컴'</li><li>'휴테크 하체 근육 강화 EMS 마사지기 식스패드 풋핏2 HT-W03A  '</li></ul>                                                |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.8945 |
+
+## 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_lh9")
+# Run inference
+preds = model("코어슬리머 전용 리필패드 6P 2개  롯데아이몰")
+```
+
+<!--
+### 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   | 9.78   | 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    | 0.4065        | -               |
+| 0.9091  | 50   | 0.2829        | -               |
+| 1.8182  | 100  | 0.0954        | -               |
+| 2.7273  | 150  | 0.0196        | -               |
+| 3.6364  | 200  | 0.0057        | -               |
+| 4.5455  | 250  | 0.0069        | -               |
+| 5.4545  | 300  | 0.0024        | -               |
+| 6.3636  | 350  | 0.0003        | -               |
+| 7.2727  | 400  | 0.0002        | -               |
+| 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  | 0.0001        | -               |
+| 17.2727 | 950  | 0.0001        | -               |
+| 18.1818 | 1000 | 0.0001        | -               |
+| 19.0909 | 1050 | 0.0           | -               |
+| 20.0    | 1100 | 0.0001        | -               |
+
+### 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

@@ -0,0 +1,29 @@
+{
+  "_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

@@ -0,0 +1,10 @@
+{
+  "__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

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

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f8c9e182dfae4c27708993ad2b77e1489195326e043b42590f382ca5d4bb62af
+size 442494816

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9cfc4c26d7e2dc3c2af1dab784eab0ca8b4ad2950f31e99d2a07c926a8e570a0
+size 43935

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]"
+}