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: 백설 찰밀가루 3Kg  에프엠에스인터내셔널 주식회사
+- text: 퀘이커 마시는오트밀 그래인 50g 20개 오트&봄딸기50gx10개_오트&우리쌀 50gx10개 (주)태풍
+- text: CJ제일제당 백설 강력밀가루 2.5kg  둘레푸드
+- text: 이츠웰 맛있는 튀김가루 1kg / CJ프레시웨이  청신호
+- text: 피플스 퀵오트밀 500gx2 (1kg) 귀리 07.퀵오트500g+뮤즐리500g 피플스(Peoples)
+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: 0.9629787234042553
+      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:** 11 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                                                                                                                                                                                                                              |
+|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 7.0   | <ul><li>'[플라하반] 유기농 포리지 500g 외 2종 롤드오트 압착귀리 유기농 포리지 280g 주식회사 수성인터내셔널'</li><li>'포스트 화이버 오트밀 오리지날 350g  다복상사'</li><li>'오트밀(식사용) 1kg/이든타운/오트밀/오트밀죽/oatmeal/압착귀리/곡류/곡물/시리얼/씨리얼/후레이크/생식/선식/건강식/두유/우유/제과/제빵/쿠키/재료/식사대용/요거트  드랍쉽'</li></ul> |
+| 0.0   | <ul><li>'볶은 검은깨 분말 가루 국내산 300g 검정깨 블랙푸드 검은콩청국장환 200g 농업회사법인 주식회사 두손애약초'</li><li>'볶은 검은깨 분말 가루 국내산 300g 검정깨 블랙푸드 검은콩검은깨환 210g 농업회사법인 주식회사 두손애약초'</li><li>'국산 냉풍건조 아로니아분말 500g [분말]아로니아분말 500g x 2팩 농업회사법인 청정산들해(주)'</li></ul>          |
+| 1.0   | <ul><li>'뚜레반 17곡 미숫가루 1kg B_청정원 홍초 자몽900ml 무한상사'</li><li>'뚜레반 17곡 미숫가루 1kg C_뚜레반 콩국수용 콩가루850g 무한상사'</li><li>'뚜레반 17곡 미숫가루A+1kg  주식회사 삼부'</li></ul>                                                                                    |
+| 3.0   | <ul><li>'[대한제분]곰표부침가루1kg / 곰표튀김가루1kg 감사 곰표부침가루1kg 동아식품'</li><li>'오뚜기 나눔7호 직원 거래처 명절준비 선물세트  제이엔팩토리'</li><li>'큐원 쫄깃한 참 부침 가루 1kg 가정 업소 호박 파 전  전가네TMG'</li></ul>                                                                       |
+| 6.0   | <ul><li>'프리미엄 아몬드가루 1kg 95% 아몬드분말 아몬드파우더 프리미엄 아몬드분말(95%) 1kg 대륙유통'</li><li>'너츠빌 캘리포니아 아몬드 분말 가루 파우더 1kg 아몬드 슬라이스 1kg (주)엠디에프앤'</li><li>'너츠빌 캘리포니아 아몬드 분말 가루 파우더 1kg 아몬드 분말 100% 1kg (주)엠디에프앤'</li></ul>                               |
+| 8.0   | <ul><li>'사조해표 찹쌀가루 350g  건우푸드'</li><li>'사조 해표 찹쌀가루 350g 감자전분 350g 주식회사 더 골든트리'</li><li>'해표 찹쌀가루 350g-1개  에이치엠몰(HM mall)'</li></ul>                                                                                                    |
+| 10.0  | <ul><li>'해표 튀김가루 1kg/부침요리/전 해표 튀김가루 1kg 단비마켓'</li><li>'CJ제일제당 백설 치킨 튀김가루 1kg  바름푸드'</li><li>'CJ제일제당 백설 튀김가루 1kg 1)튀김가루 태성유통'</li></ul>                                                                                                |
+| 4.0   | <ul><li>'신일 냉동 골드빵가루 2kg  (주)우주식품디씨오피'</li><li>'오뚜기 빵가루 1KG 자취 대용량 식자재 선물 튀김 제사 명절 부침개 간식  하나칭구'</li><li>'오뚜기 빵가루 200g  이고지고'</li></ul>                                                                                               |
+| 2.0   | <ul><li>'백설 박력밀가루 1kg (박력분)  주식회사 몬즈컴퍼니'</li><li>'아티장 밀가루 T55 20KG 백설  베이킹스타'</li><li>'박력밀가루(큐원 1K)  썬샤인웍스'</li></ul>                                                                                                                 |
+| 5.0   | <ul><li>'[대두식품] 강력쌀가루(국산) 15kg  (주)대두식품서울지점'</li><li>'싸리재 유기농 습식 쌀가루 [ 백미 멥쌀가루 1kg ] 떡만들기 베이킹 비건요리 무염백미찹쌀가루 1kg 농업회사법인콩사랑유한회사'</li><li>'햇쌀마루 박력쌀가루 3kg  이캔유통'</li></ul>                                                               |
+| 9.0   | <ul><li>'뚜레반 날콩가루 1kg  (주)울산팡'</li><li>'복만네 콩국수용 콩가루 850g 05.해늘이볶은콩가루1kg 바른에프에스'</li><li>'[복만네] 콩국수용 콩가루 850g / 콩국 선식  (주)유영유통'</li></ul>                                                                                             |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9630 |
+
+## 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_fd0")
+# Run inference
+preds = model("CJ제일제당 백설 강력밀가루 2.5kg  둘레푸드")
+```
+
+<!--
+### 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   | 8.9308 | 24  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 50                    |
+| 1.0   | 22                    |
+| 2.0   | 50                    |
+| 3.0   | 50                    |
+| 4.0   | 50                    |
+| 5.0   | 32                    |
+| 6.0   | 18                    |
+| 7.0   | 50                    |
+| 8.0   | 26                    |
+| 9.0   | 50                    |
+| 10.0  | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0143  | 1    | 0.4619        | -               |
+| 0.7143  | 50   | 0.2999        | -               |
+| 1.4286  | 100  | 0.1066        | -               |
+| 2.1429  | 150  | 0.0721        | -               |
+| 2.8571  | 200  | 0.0457        | -               |
+| 3.5714  | 250  | 0.03          | -               |
+| 4.2857  | 300  | 0.0045        | -               |
+| 5.0     | 350  | 0.002         | -               |
+| 5.7143  | 400  | 0.004         | -               |
+| 6.4286  | 450  | 0.002         | -               |
+| 7.1429  | 500  | 0.0077        | -               |
+| 7.8571  | 550  | 0.002         | -               |
+| 8.5714  | 600  | 0.006         | -               |
+| 9.2857  | 650  | 0.0019        | -               |
+| 10.0    | 700  | 0.0001        | -               |
+| 10.7143 | 750  | 0.0001        | -               |
+| 11.4286 | 800  | 0.0001        | -               |
+| 12.1429 | 850  | 0.0           | -               |
+| 12.8571 | 900  | 0.0           | -               |
+| 13.5714 | 950  | 0.0           | -               |
+| 14.2857 | 1000 | 0.0           | -               |
+| 15.0    | 1050 | 0.0           | -               |
+| 15.7143 | 1100 | 0.0           | -               |
+| 16.4286 | 1150 | 0.0           | -               |
+| 17.1429 | 1200 | 0.0           | -               |
+| 17.8571 | 1250 | 0.0           | -               |
+| 18.5714 | 1300 | 0.0           | -               |
+| 19.2857 | 1350 | 0.0           | -               |
+| 20.0    | 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

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+{
+  "_name_or_path": "mini1013/master_item_fd",
+  "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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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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+    "lstrip": false,
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+  "model_max_length": 512,
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+  "padding_side": "right",
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}