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 +1,254 @@
+---
+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: 국산 전라도 겉절이 1kg+1kg 열무김치 1kg+1kg 주식회사 하루식품
+- text: 해남 황금절임배추 20kg / 노란 항암배추 국내산 김장 김치 해남 황금절임배추 20kg(7~10포기)_11/18(토) 바이곰
+- text: '[김권태농부] 옥과 맛있는 김치 배추 포기김치 2kg 김권태 배추포기김치 2kg 목화골 우리농산'
+- text: 황금배추로 만든 절임키트 19KG 황금절임키트 19kg_11월 16일 골드바이오스토어
+- text: '[마음심은] 겉절이 3kg / 익을수록 시원한  (주)강가의나무'
+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: 0.9429298436932024
+      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:** 14 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                                                                                                                                                                                                                  |
+|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1.0   | <ul><li>'수작업 완전 국내산 양념이 듬뿍 매운 전라도 얼갈이 겉절이 1kg 김장  오텀 골드 (AUTUMN GOLD)'</li><li>'국산 겉절이 2kg+Npay5% 매일생산 당일제조 수 빛 배추 김치 먹보야 수 국산 포기김치3kg+Npay5% (주)먹보야'</li><li>'명광성푸드 술안주로도 간식으로도 맛있는 고구마무스 1kg 고구마무스(1kg) 조이찬스'</li></ul> |
+| 6.0   | <ul><li>'종가집 백김치3kg  프라임 다모여'</li><li>'종가집 백김치 5kg (냉장포장)  주식회사 푸드공공칠'</li><li>'종가집 우리땅 백김치 (5kg) 국내산재료만사용 02.우리땅 백김치(숙성 5kg) 바이라이프'</li></ul>                                                                            |
+| 11.0  | <ul><li>'이킴 홍진경더김치 총각김치 3kg 동의 쉼포니'</li><li>'[피코크] 조선호텔 총각김치 1.5kg  주식회사 배한네트웍스'</li><li>'CJ제일제당 비비고 총각김치 1.5kg  오루고'</li></ul>                                                                                           |
+| 7.0   | <ul><li>'[CJ](신세계의정부점) 비비고 김치볶음 150g  주식회사 에스에스지닷컴'</li><li>'[CJ](신세계강남점) 비비고김치볶음150g  주식회사 에스에스지닷컴'</li><li>'피코크 조선호텔 무석박지 1kg  주식회사 맨도롱'</li></ul>                                                                      |
+| 2.0   | <ul><li>'사대부 국산 깍두기 3kg HACCP 인증  (주)우영채널'</li><li>'이킴 홍진경더김치 깍두기 2kg  겨자씨'</li><li>'예소담 특깍두기3kg  농업회사법인(주)예소담'</li></ul>                                                                                                 |
+| 5.0   | <ul><li>'예소담 특묵은지3kg 예소담 특묵은지3kg 원츄쟈챠'</li><li>'CJ제일제�� 비비고 묵은지 1.5kg  퓨어리실바'</li><li>'해남 화원농협 이맑은김치 묵은지 10kg  이세몰'</li></ul>                                                                                              |
+| 4.0   | <ul><li>'예소담 특동치미 3kg  농업회사법인(주)예소담'</li><li>'대상 종가집 동치미 2.5kg 1개  하스제이'</li><li>'[열우물]연동치미 450g x 1팩 연근가루로 맛을 낸 동치미  소백스토어 주식회사'</li></ul>                                                                               |
+| 9.0   | <ul><li>'이담채 상큼한 국내산 오이소박이 2kg 오이소박이 1kg 서부농산영농조합법인'</li><li>'100% 국산 전라도 오이소박이 1kg  제주나는 농산물'</li><li>'이담채 상큼한 국내산 오이소박이 2kg 오이소박이 3kg 서부농산영농조합법인'</li></ul>                                                             |
+| 12.0  | <ul><li>'종가집 파김치2.5kg  프라임 다모여'</li><li>'종가집 파김치 2.5kg  다올'</li><li>'아이스박스 발송 종가 파김치 1KG 코스트코 아이스팩 기본1개 도우닷컴'</li></ul>                                                                                                   |
+| 10.0  | <ul><li>'황금 김장 절인배추 강원도 고랭지절임배추 10kg 김장양념 고춧가루 12월 29일 (금)도착 큰장터'</li><li>'더맛있는 김장세트 3.5kg(절임배추+배추김치양념) 만들기 밀키트 집콕놀이 김장세트3.5kg 주식회사 삼창'</li><li>'GAP, 저탄소인증 농부삼촌 해남 절임배추 20kg 12월 13일(수) 농부삼촌영농조합법인'</li></ul>            |
+| 13.0  | <ul><li>'안동학가산김치 가정용 고랭지 포기김치 4kg (국내산) 3.포기김치 업소용 10kg고춧가루만 중국산 학가산김치서울직판장'</li><li>'김권태 전라도 곡성 옥과맛있는김치 포기 배추김치 김장 2kg 9_전라도 열무김치 2kg 5월~9월 제이엘컴퍼니(JL Company)'</li><li>'청풍 포기김치(실속형) 10kg 2kg 영신내추럴'</li></ul>          |
+| 8.0   | <ul><li>'씨제이 비비고 열무김치 900G  홈플러스'</li><li>'영동김치 열무 얼갈이 김치 5kg  영동김치'</li><li>'열무김치 열무 얼갈이 자박이 김치 100% 국내산 [먹부림마켓]  먹부림 마켓'</li></ul>                                                                                      |
+| 3.0   | <ul><li>'익을수록 맛있는 남도식 석박지 무김치 1kg  소복김치'</li><li>'종가집 담백한나박김치1.2kg(PET)  대상JJ'</li><li>'[산들바람김치] 나박물김치 3kg 국산100% 나박김치 반찬 속초  산들바람식품'</li></ul>                                                                           |
+| 0.0   | <ul><li>'여수돌산갓김치 5kg 김치 국내산 100% 당일생산  미스터홍주부'</li><li>'여수 명물 돌산 갓김치 2kg 국내산 전라도 갓 김치  대한민국농수산'</li><li>'종가집 돌산갓김치3kg(온라인)  프라임 다모여'</li></ul>                                                                            |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9429 |
+
+## 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_fd3")
+# Run inference
+preds = model("[마음심은] 겉절이 3kg / 익을수록 시원한  (주)강가의나무")
+```
+
+<!--
+### 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   | 8.1522 | 18  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 23                    |
+| 1.0   | 50                    |
+| 2.0   | 50                    |
+| 3.0   | 24                    |
+| 4.0   | 31                    |
+| 5.0   | 50                    |
+| 6.0   | 50                    |
+| 7.0   | 40                    |
+| 8.0   | 23                    |
+| 9.0   | 32                    |
+| 10.0  | 50                    |
+| 11.0  | 50                    |
+| 12.0  | 29                    |
+| 13.0  | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0115  | 1    | 0.4872        | -               |
+| 0.5747  | 50   | 0.3163        | -               |
+| 1.1494  | 100  | 0.2368        | -               |
+| 1.7241  | 150  | 0.1362        | -               |
+| 2.2989  | 200  | 0.0482        | -               |
+| 2.8736  | 250  | 0.0183        | -               |
+| 3.4483  | 300  | 0.0142        | -               |
+| 4.0230  | 350  | 0.004         | -               |
+| 4.5977  | 400  | 0.0022        | -               |
+| 5.1724  | 450  | 0.008         | -               |
+| 5.7471  | 500  | 0.0003        | -               |
+| 6.3218  | 550  | 0.0004        | -               |
+| 6.8966  | 600  | 0.002         | -               |
+| 7.4713  | 650  | 0.0004        | -               |
+| 8.0460  | 700  | 0.0003        | -               |
+| 8.6207  | 750  | 0.0002        | -               |
+| 9.1954  | 800  | 0.0002        | -               |
+| 9.7701  | 850  | 0.0002        | -               |
+| 10.3448 | 900  | 0.0001        | -               |
+| 10.9195 | 950  | 0.0001        | -               |
+| 11.4943 | 1000 | 0.0001        | -               |
+| 12.0690 | 1050 | 0.0001        | -               |
+| 12.6437 | 1100 | 0.0001        | -               |
+| 13.2184 | 1150 | 0.0001        | -               |
+| 13.7931 | 1200 | 0.0001        | -               |
+| 14.3678 | 1250 | 0.0001        | -               |
+| 14.9425 | 1300 | 0.0001        | -               |
+| 15.5172 | 1350 | 0.0001        | -               |
+| 16.0920 | 1400 | 0.0001        | -               |
+| 16.6667 | 1450 | 0.0001        | -               |
+| 17.2414 | 1500 | 0.0001        | -               |
+| 17.8161 | 1550 | 0.0001        | -               |
+| 18.3908 | 1600 | 0.0001        | -               |
+| 18.9655 | 1650 | 0.0001        | -               |
+| 19.5402 | 1700 | 0.0001        | -               |
+
+### 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_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

@@ -0,0 +1,10 @@
+{
+  "__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 sha256:4b71c9ed74e677485253400933a62b7bc1ea8fe54f927dbc637f628ae2b94c52
+size 442494816

model_head.pkl

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+size 87047

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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+{
+  "bos_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "content": "[UNK]",
+      "lstrip": false,
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+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}