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 +1,238 @@
+---
+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: 명절선물 동원참치 S12호 참치선물세트 설선물 한가위 동원참치 S12호 제이에스포
+- text: 동원참치 덕용 업소용 대용량 덕용 참치 1.88kg  주식회사 이너피스(inner peace)
+- text: 사조 자연산 골뱅이 400g  주식회사 당장만나
+- text: 목우촌 뚝심 340g  장보고가
+- text: 농심 알쿠니아 황도 2절 통조림 850g 알쿠니아 황도 통조림 200g x 3개입 지에스(GS) 금성상회
+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: 0.9854036341971999
+      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>'그린올리브 365g 동서 리치스 올리브 샐러드  화남F.C'</li><li>'동서 리치스 슬라이스 오이피클 3kg  무성유통'</li><li>'리치스 슬라이스 오이피클 3kg 피클  화남F.C'</li></ul>                                                                             |
+| 3.0   | <ul><li>'CJ제일제당 스팸12호 1세트  위드'</li><li>'CJ제일제당 스팸 복합 5호 선물세트  보담유통'</li><li>'스팸복합5호 햄 카놀라유 선물세트 복합 명절 추석 세트  땡그리나'</li></ul>                                                                                |
+| 4.0   | <ul><li>'동원 스위트콘 340g 골든 동원 저스트 스위트콘 340g(리뉴얼) 중앙 리테일'</li><li>'오뚜기 스위트콘 옥수수통조림 340g 스위트콘 340g x 1개 주식회사 로씨네'</li><li>'동서 리치스 홀커널 스위트콘 425g 원터치 옥수수 캔 통조림  주식회사 당장만나'</li></ul>                             |
+| 7.0   | <ul><li>'스팸 마일드 25% 라이트 340g 외 스팸 4종 1. 스팸 클래식 200g 주식회사 하포테크'</li><li>'CJ제일제당 스팸 싱글 클래식 80g CJ제일제당 스팸 싱글 25% 라이트 80g 삼영유통'</li><li>'통조림 CJ제일제당 스팸 클래식 200g/햄통조림 ~통조림/캔햄_쿡샵 스위트콘 (태국산) 420g 단비마켓'</li></ul> |
+| 2.0   | <ul><li>'샘표 김치찌개용꽁치280g/김치찌개전용꽁치통조림  주식회사 달인식자재'</li><li>'샘표 고등어 원터치 400g  조이텍'</li><li>'통조림 오뚜기 고등어 400g/참치캔 ~150g이상참치_동원 고추참치 150g 모두유통주식회사'</li></ul>                                                    |
+| 1.0   | <ul><li>'화풍 양송이 편 2.8Kg  다유몰'</li><li>'디벨라 렌틸스 400g /렌즈콩  (주)푸드올마켓'</li><li>'몬 코코넛밀크 400ml 02_콕_코코넛밀크_400ml 정앤남'</li></ul>                                                                                  |
+| 0.0   | <ul><li>'유동 자연산 골뱅이 230g /s/ 번데기 술안주 비빔면 소면 무침 국수 야식 통조림  (주)강남상사'</li><li>'동원에프앤비 동원 자연산 골뱅이 230g  주식회사 진현유통'</li><li>'자연산 골뱅이캔삼포140g  스완인터내셔널'</li></ul>                                                  |
+| 5.0   | <ul><li>'동원참치 고추참치 통조림 100g 동원 참치 12종_17.동원 고추 참치 150g (주)다누림글로벌'</li><li>'오뚜기 참치빅캔 살코기 1.88kg  플랜트더퓨처'</li><li>'동원 참치 3kg 대용량 참치캔 업소용 코스트코  태양팜스'</li></ul>                                                |
+| 8.0   | <ul><li>'샘표 통조림캔 황도 400g 조림용고등어 400g (주)두배로'</li><li>'동서 리치스 파인애플 슬라이스 836g  (주)푸드팜'</li><li>'동서 리치스 후르츠칵테일 3kg 미동의 제이모리'</li></ul>                                                                         |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9854 |
+
+## 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_fd21")
+# Run inference
+preds = model("목우촌 뚝심 340g  장보고가")
+```
+
+<!--
+### 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   | 8.4489 | 22  |
+
+| 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    | 0.4416        | -               |
+| 0.7042  | 50   | 0.297         | -               |
+| 1.4085  | 100  | 0.1016        | -               |
+| 2.1127  | 150  | 0.0599        | -               |
+| 2.8169  | 200  | 0.0339        | -               |
+| 3.5211  | 250  | 0.0256        | -               |
+| 4.2254  | 300  | 0.0235        | -               |
+| 4.9296  | 350  | 0.0019        | -               |
+| 5.6338  | 400  | 0.0113        | -               |
+| 6.3380  | 450  | 0.0002        | -               |
+| 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  | 0.0001        | -               |
+| 11.2676 | 800  | 0.0001        | -               |
+| 11.9718 | 850  | 0.0001        | -               |
+| 12.6761 | 900  | 0.0001        | -               |
+| 13.3803 | 950  | 0.0001        | -               |
+| 14.0845 | 1000 | 0.0001        | -               |
+| 14.7887 | 1050 | 0.0001        | -               |
+| 15.4930 | 1100 | 0.0001        | -               |
+| 16.1972 | 1150 | 0.0001        | -               |
+| 16.9014 | 1200 | 0.0           | -               |
+| 17.6056 | 1250 | 0.0001        | -               |
+| 18.3099 | 1300 | 0.0001        | -               |
+| 19.0141 | 1350 | 0.0001        | -               |
+| 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

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+{
+  "_name_or_path": "mini1013/master_item_fd",
+  "architectures": [
+    "RobertaModel"
+  ],
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+  "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": {},
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+  "similarity_fn_name": null
+}

config_setfit.json

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+  "normalize_embeddings": false
+}

model.safetensors

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model_head.pkl

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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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+}