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 +1,233 @@
+---
+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: 1883 시럽 1000ml 바닐라 3병 Vanilla 바닐라 달콤한푸린
+- text: 모닌 바닐라 시럽 1000ml MONIN 홈카페 커피시럽 로스티드 헤이즐넛 700ml 아르타
+- text: 리고 초코 시럽 585g 2개세트  (주)비앤씨인터내셔널
+- text: 옳곡 국내산 피넛버터 땅콩잼 무첨가 땅콩버터 200g 크런치 스무스 03.스무스+크런치 조은스토어2
+- text: 페레로 누텔라 헤이즐넛 코코아 스프레드 370g 5개 누텔라 헤이즐넛 코코아 스프레드 370g 5개 홈마트
+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: 0.6548139319295457
+      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:** 8 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>'아이언맥스 프로틴 쨈 스프레드 초코 아몬드 250g 2팩 IronMaxx  블레스윤'</li><li>'페레로 누텔라 헤이즐넛 코코아 스프레드 370g 3개 누텔라 헤이즐넛 코코아 스프레드 370g 3개 홈마트'</li><li>'누텔라 헤이즐넛 코코아 스프레드 370g x 2개 [라면] 봉지라면_오뚜기 짜슐랭 145g 20개 옐로우로켓'</li></ul> |
+| 1.0   | <ul><li>'[가당딸기] 국산 냉동 가당딸기 2kg 아이스베리 (6개/박스)  주식회사 커피바바'</li><li>'복음자리 진심의 딸기 1kg 딸기청 🍓진심의 딸기 1kg 5개🍓 담다'</li><li>'초록원 과일잼 1kg x 2개 딸기잼 1021653 딸기잼1kg 블루베리잼1kg_파인애플망고잼1kg 앤디월드'</li></ul>                      |
+| 5.0   | <ul><li>'Torani 무설탕 소스, 다크 초콜릿, 1.9L(64온스) 화이트 초콜릿_64 Fl Oz (Pack of 1) 저무리5'</li><li>'모카믹스 다크소스 초콜렛 2kg 1박스 6개 초코소스  엠씨컴퍼니 (주)'</li><li>'매일유업 테너소스 초콜렛 1.35kg 1병 카라멜 1.35kg 티피컨테이너'</li></ul>                  |
+| 4.0   | <ul><li>'오뚜기 맛있는 사과쨈 300G 홈카페 식재료 토스트 브런치 캠핑 아이들 간식  봄날스토어'</li><li>'오뚜기 Light sugar 사과쨈 290g 4개  007스테이지스'</li><li>'[달콤한 맛있는] 밀크스프레드 얼그레이 235g [블루베리 딸기 사과 포도 버터맛]  레인보우'</li></ul>                            |
+| 0.0   | <ul><li>'포모나 얼그레이 하이볼 시럽 밀크티 홍차 1000ml 06-포모나 카라멜 시럽 주식회사 커피창고'</li><li>'프프프베이커리 빵에 발라먹는 버터스프레드 얼그레이 맛 【1개】 허니 데칼컴퍼니(Decal Company)'</li><li>'매일 테너베이스 청포도 에이드 스무디 농축액 1.2kg 1022147 오렌지 1.2kg 가이던스'</li></ul>  |
+| 3.0   | <ul><li>'LB 메이플시럽189ml(병) (N2)  주식회사 에스에스지닷컴'</li><li>'마누카 헬스 Manuka health 마누카 허니 MGO 250+ 시럽 100ml  K&G GmbH'</li><li>'시럽 초콜렛 네이처 컨트리  라몬제이'</li></ul>                                                        |
+| 7.0   | <ul><li>'커피시럽 카페시럽 1.5L x2병 대상 롯데 파우더 커피 대상 로즈버드 그린티 파우더 500g 가루녹차 하늘담아'</li><li>'토라니 카라멜 미니 토핑용소스 468g / 카라멜마끼야또 카라멜라떼  (주)오케이푸드'</li><li>'1883 헤이즐넛시럽 1883 라임 시럽 1000ml 엔에프 컴퍼니'</li></ul>                    |
+| 2.0   | <ul><li>'[신세계 가공](신세계본점)리고땅콩버터크리미 462g  주식회사 에스에스지닷컴'</li><li>'스키피 땅콩버터1.36kg 스키피 크리미 땅콩버터 2.27kg 두두유통'</li><li>'피비핏 버터 오리지널 파우더 피넛 프리 프로틴 글루텐 850g  에코프리'</li></ul>                                            |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.6548 |
+
+## 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_fd16")
+# Run inference
+preds = model("리고 초코 시럽 585g 2개세트  (주)비앤씨인터내셔널")
+```
+
+<!--
+### 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   | 10.8025 | 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                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0159  | 1    | 0.4035        | -               |
+| 0.7937  | 50   | 0.322         | -               |
+| 1.5873  | 100  | 0.125         | -               |
+| 2.3810  | 150  | 0.0315        | -               |
+| 3.1746  | 200  | 0.0111        | -               |
+| 3.9683  | 250  | 0.0005        | -               |
+| 4.7619  | 300  | 0.0002        | -               |
+| 5.5556  | 350  | 0.0001        | -               |
+| 6.3492  | 400  | 0.0001        | -               |
+| 7.1429  | 450  | 0.0001        | -               |
+| 7.9365  | 500  | 0.0001        | -               |
+| 8.7302  | 550  | 0.0001        | -               |
+| 9.5238  | 600  | 0.0001        | -               |
+| 10.3175 | 650  | 0.0001        | -               |
+| 11.1111 | 700  | 0.0           | -               |
+| 11.9048 | 750  | 0.0001        | -               |
+| 12.6984 | 800  | 0.0           | -               |
+| 13.4921 | 850  | 0.0           | -               |
+| 14.2857 | 900  | 0.0           | -               |
+| 15.0794 | 950  | 0.0           | -               |
+| 15.8730 | 1000 | 0.0           | -               |
+| 16.6667 | 1050 | 0.0           | -               |
+| 17.4603 | 1100 | 0.0           | -               |
+| 18.2540 | 1150 | 0.0001        | -               |
+| 19.0476 | 1200 | 0.0           | -               |
+| 19.8413 | 1250 | 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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+  "normalize_embeddings": false
+}

model.safetensors

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model_head.pkl

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modules.json

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+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
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+    "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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tokenizer_config.json

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+}