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 +1,242 @@
+---
+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: '[헤지스ACC]HJBA3F885BK[13인치 노트북 수납가능][KEVIN]블랙 참장식 크로스 겸용 미니 토트백  에이케이에스앤디 (주)
+    AK인터넷쇼핑몰'
+- text: 마젤란 메신저백 크로스백 슬링백 힙색 힙쌕 학생 여성 남자 캐주얼 크로스 여행용 여권 핸드폰 보조 학원 가방 LKHS-304_B-연핑크(+키홀더)
+    더블유팝
+- text: 마젤란 메신저백 크로스백 슬링백 힙색 힙쌕 학생 여성 남자 캐주얼 크로스 여행용 여권 핸드폰 보조 학원 가방 ML-1928_연그레이
+    더블유팝
+- text: '[갤러리아] JUBA4E021G2 [MATEO] 그레이 로고프린트 숄더백 JUBA4E021G2 [MATEO] 그레이 로고프린트 숄더백
+    NS홈쇼핑_NS몰'
+- text: '[디스커버리](신세계강남점)[23N] 디스커버리 미니 슬링백 (DXSG0043N) IVD 다크 아이보리_F 주식회사 에스에스지닷컴'
+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: 0.8488667448221962
+      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:** 9 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>'[질스튜어트](광주신세계)블랙 클래식 클러치백 [JUWA2F392BK]  주식회사 에스에스지닷컴'</li><li>'심플 클러치백 EOCFHX257BK/에스콰이아 블랙 롯데쇼핑(주)'</li><li>'[듀퐁] 소프트그레인 파우치 베이지 CG180263CL 베이지 (주)씨제이이엔엠'</li></ul>                                                                                                                   |
+| 3.0   | <ul><li>'엔지니어드가먼츠 블랙 나일론 토트백 23F1H034BLACK  주식회사 어도어럭스'</li><li>'[가이거] 퀼팅 레더 체인 숄더백 (+플랩지갑) 캐러멜 브라운 (주)우리홈쇼핑'</li><li>'토트 브리프 크로스백 FT8570 블랙 글로리홈'</li></ul>                                                                                                                                      |
+| 4.0   | <ul><li>'여자캔버스 가방 코디 크로스백 남자에코백 신발 BLUE 고앤런'</li><li>'여학생 에코백 아이보리 가방 남녀공용 캐주얼 쇼퍼백  엘케이엠'</li><li>'패션 에코백 데일리 가방 캐주얼 숄더백 브라운 심정'</li></ul>                                                                                                                                                        |
+| 7.0   | <ul><li>'[갤러리아] 644040 2BKPI 1000 ONE SIZE 한화갤러리아(주)'</li><li>'[갤러리아] 헤지스핸드백 그린 워싱가죽 크로스 겸용 토트백 HJBA3E301E2(타임월드)  한화갤러리아(주)'</li><li>'[메종키츠네] 로고 프린트 코튼 토트백 블루 LW05102WW0008 BLUE_FREE 신세계몰'</li></ul>                                                                                           |
+| 2.0   | <ul><li>'바버 가죽 코팅 서류 가방 브리프 케이스 UBA0004 NAVY 뉴욕트레이딩'</li><li>'[롯데백화점]에스콰이아 23FW 신상 경량 나일론 노트북 수납 남여 데일리 토트 크로스백 EOCFHX258BK  롯데백화점_'</li><li>'22FW 신상 뉴 포멀 슬림 스퀘어 심플 비즈니스 캐주얼 서류가방 ECBFHX227GY  롯데백화점1관'</li></ul>                                                                                |
+| 1.0   | <ul><li>'NATIONALGEOGRAPHIC N225USD340 다이브 플러스 V3 BLACK 240 맥스투'</li><li>'레스포삭 보이저 백팩 경량 나일론 보부상 복조리 가방 7839 플라워 행운샵'</li><li>'레스포삭 보이저 백팩 경량 Voyager Backpack 7839 블랙 하하대행'</li></ul>                                                                                                            |
+| 0.0   | <ul><li>'[갤러리아] 헤지스핸드백HJBA2F770BK_ 블랙 로고 장식 솔리드 메신져백(타임월드)  한화갤러리아(주)'</li><li>'로아드로아 허쉬 메쉬 포켓 크로스 메신저백 (아이보리) 크로스백 FREE 가방팝'</li><li>'[본사공식] 타프 메신저백 사첼 S EOCBS04 008  롯데아이몰'</li></ul>                                                                                                          |
+| 5.0   | <ul><li>'팩세이프 가방 GO 크로스바디 백 2.5L / PACSAFE URBAN 도난방지 유럽 해외 여행 등산 슬링백 크로스백 RFID차단 1. 제트 블랙 (JET BLACK) 시계1위팝워치'</li><li>'샨타코[Chantaco] 레더 크로스백 BB NH3271C53N 000/라코스테  롯데쇼핑(주)'</li><li>'팩세이프 가방 GO 크로스바디 백 2.5L / PACSAFE URBAN 도난방지 유럽 해외 여행 등산 슬링백 크로스백 RFID차단 2. 로즈 (ROSE) 시계1위팝워치'</li></ul> |
+| 8.0   | <ul><li>'[기회공작소] 데일리 슬링백 크로스 힙색 허리가방 스포츠 등산 힙색 허리색 슬링백 보조가방  글로리커머스'</li><li>'구찌 GG 캔버스 투웨이 밸트백 힙색 630915 KY9KN 9886  쏠나인'</li><li>'벨트형 핸드폰 허리가방 남자 벨트백 세로형 가죽 벨트파우치 지갑 허리벨트케이스 브라운 자주구매'</li></ul>                                                                                               |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.8489 |
+
+## 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_ac0")
+# Run inference
+preds = model("[디스커버리](신세계강남점)[23N] 디스커버리 미니 슬링백 (DXSG0043N) IVD 다크 아이보리_F 주식회사 에스에스지닷컴")
+```
+
+<!--
+### 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   | 4   | 9.2289 | 29  |
+
+| 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                    |
+| 7.0   | 50                    |
+| 8.0   | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0141  | 1    | 0.3958        | -               |
+| 0.7042  | 50   | 0.3012        | -               |
+| 1.4085  | 100  | 0.1811        | -               |
+| 2.1127  | 150  | 0.0599        | -               |
+| 2.8169  | 200  | 0.0333        | -               |
+| 3.5211  | 250  | 0.0169        | -               |
+| 4.2254  | 300  | 0.0005        | -               |
+| 4.9296  | 350  | 0.0003        | -               |
+| 5.6338  | 400  | 0.0002        | -               |
+| 6.3380  | 450  | 0.0003        | -               |
+| 7.0423  | 500  | 0.0001        | -               |
+| 7.7465  | 550  | 0.0001        | -               |
+| 8.4507  | 600  | 0.0001        | -               |
+| 9.1549  | 650  | 0.0001        | -               |
+| 9.8592  | 700  | 0.0001        | -               |
+| 10.5634 | 750  | 0.0           | -               |
+| 11.2676 | 800  | 0.0001        | -               |
+| 11.9718 | 850  | 0.0001        | -               |
+| 12.6761 | 900  | 0.0001        | -               |
+| 13.3803 | 950  | 0.0           | -               |
+| 14.0845 | 1000 | 0.0           | -               |
+| 14.7887 | 1050 | 0.0           | -               |
+| 15.4930 | 1100 | 0.0           | -               |
+| 16.1972 | 1150 | 0.0           | -               |
+| 16.9014 | 1200 | 0.0           | -               |
+| 17.6056 | 1250 | 0.0           | -               |
+| 18.3099 | 1300 | 0.0           | -               |
+| 19.0141 | 1350 | 0.0           | -               |
+| 19.7183 | 1400 | 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

@@ -0,0 +1,29 @@
+{
+  "_name_or_path": "mini1013/master_item_ac",
+  "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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+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

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+oid sha256:7e88ab44bc082b46c1974452b1eabbb4d53100dc0341a139ab61094a83659411
+size 442494816

model_head.pkl

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modules.json

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+    "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

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
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+    "lstrip": false,
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+}

tokenizer_config.json

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+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
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+  "pad_token": "[PAD]",
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
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+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}