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: 맑은농산 리얼넛츠 베리앤요거트 하루건강견과 20g x 25개입  비트리
+- text: 23년 햅쌀 골든퀸3호 수향미 특등급 10kg / 순차출고  상상리허설
+- text: 산과들에 원데이오리지널 20g x 50개입 선물세트 동의 제이엠세일즈
+- text: 구운아몬드 1kg 견과류  에이케이에스앤디 (주) AK인터넷쇼핑몰
+- text: 필리핀 세부 건망고 80g 10개-쫀득한망고 말린망고 말린과일  대신유통
+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: 0.894413407821229
+      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>'[채울농산] 국산 장수상황버섯(baumii 최상품) 1개월분 (100g) 1개월분 채울농산'</li><li>'명이나물 2kg 산마늘잎 생명이나물 산나물 생채 명이장아찌 강원도 산마늘 명이 장아찌 2kg 토종농장'</li><li>'풀무원 한끼연두부 오리엔탈유자 (118gX2EA)  (주)풀무원'</li></ul>                                                |
+| 2.0   | <ul><li>'커클랜드 건 블루베리 567g 몸에 좋은 건과일 샐러드나 베이킹에 활용 코스트코  마인드 트레이드(mind trade)'</li><li>'웰프레쉬 냉동 블루베리 미국산 1kg  배동바지몰'</li><li>'커클랜드 냉동 블루베리 2.27kg 코스트코 아이스박스 요거트 과일 베리  라미의잡화점'</li></ul>                                                |
+| 5.0   | <ul><li>'2022년산 국산 서리태 2kg 검은콩 속청 전남 구례산 볶은 서리태가루 1kg 농업회사법인(주)한결유통'</li><li>'국산 서리태 2kg 검은콩 속청 전남 구례산 국산 서리태(특A) 1kg 농업회사법인(주)한결유통'</li><li>'잔다리마을 특허받은 공법으로 로스팅한 검은콩 서리태 볶음콩 250g / 영양 간식  주식회사 패스트뷰'</li></ul>                      |
+| 0.0   | <ul><li>'Sol Simple 태양열 건조 망고 6온스(1팩)_파인애플 시이부동'</li><li>'[푸드] KUNNA 쿤나 건망고 75g 3개 부담없이 젤리 망고 마른 과일 태국 간식 사무실 탕비실 건조과일 말린 망고  에스디지컴퍼니'</li><li>'너츠브라더 촉촉한 건망고 200g 건망고 1kg (주)조하'</li></ul>                                            |
+| 4.0   | <ul><li>'[카무트] 고대곡물 카무트 쌀 밀 500g  이푸른(주)'</li><li>'23년 국산 현미 쌀눈 2kg  주식회사 건강중심'</li><li>'[예약구매 할인] 저당 파로 800g 이탈리아 고대곡물 바비조아 저당밥 시리즈 특허공법 저항성전분  주식회사 바비조아'</li></ul>                                                                  |
+| 1.0   | <ul><li>'맛있는家 너트리 캘리포니아 생아몬드 500g x 2개  (주)씨제이이엔엠'</li><li>'길림양행 탐스팜 쿠키앤크림 아몬드 190g  바이트리스'</li><li>'머거본 커피땅콩 130g 6개/ 견과류 마른안주 주전부리  보마스'</li></ul>                                                                                   |
+| 3.0   | <ul><li>'웰루츠 A등급 냉동 블루베리 1kg 냉동과일 웰루츠 냉동 키위 다이스(중국) 1kg 웰루츠'</li><li>'뉴뜨레 냉동 블루베리 홀 1kg+1kg 무가당 세척블루베리 과일 모음 다이스 퓨레 뉴뜨레 냉동 그린키위 1kg x 2봉 주식회사 보금푸드'</li><li>'코스트코 커클랜드 냉동 블루베리 2.27kg / 아이스박스 포장발송 아이스팩 + 드라이아이스 발송 남들과 다르게'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.8944 |
+
+## 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_fd5")
+# Run inference
+preds = model("구운아몬드 1kg 견과류  에이케이에스앤디 (주) AK인터넷쇼핑몰")
+```
+
+<!--
+### 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   | 10.0886 | 25  |
+
+| 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    | 0.4119        | -               |
+| 0.9091  | 50   | 0.2564        | -               |
+| 1.8182  | 100  | 0.0407        | -               |
+| 2.7273  | 150  | 0.0157        | -               |
+| 3.6364  | 200  | 0.014         | -               |
+| 4.5455  | 250  | 0.0           | -               |
+| 5.4545  | 300  | 0.0           | -               |
+| 6.3636  | 350  | 0.0           | -               |
+| 7.2727  | 400  | 0.0           | -               |
+| 8.1818  | 450  | 0.0001        | -               |
+| 9.0909  | 500  | 0.0           | -               |
+| 10.0    | 550  | 0.0           | -               |
+| 10.9091 | 600  | 0.0           | -               |
+| 11.8182 | 650  | 0.0           | -               |
+| 12.7273 | 700  | 0.0           | -               |
+| 13.6364 | 750  | 0.0           | -               |
+| 14.5455 | 800  | 0.0           | -               |
+| 15.4545 | 850  | 0.0           | -               |
+| 16.3636 | 900  | 0.0           | -               |
+| 17.2727 | 950  | 0.0           | -               |
+| 18.1818 | 1000 | 0.0           | -               |
+| 19.0909 | 1050 | 0.0           | -               |
+| 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_fd",
+  "architectures": [
+    "RobertaModel"
+  ],
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+  "hidden_act": "gelu",
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+  "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"
+  },
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+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+  "normalize_embeddings": false
+}

model.safetensors

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modules.json

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+    "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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+}