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 +1,264 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 탑키드 만들기 경찰관 놀이 세트 3인용 가구/인테리어>수예>기타수예
+- text: 일상공방 코 손뜨개 6종세트 인디핑크 421114 가구/인테리어>수예>뜨개질>완제품
+- text: 퀼트가게6마 반폭롤 면 100 20수 도기 프렌즈 WS 792 원단 가구/인테리어>수예>퀼트/펠트>원단
+- text: 펠트 구절초 대 SET 환경꾸미기재료 가구/인테리어>수예>퀼트/펠트>도안
+- text: 광목침구 촬영용 빈티지 플라워 코튼 포플린 드레스 셔츠 섬유 린넨 대폭원단 가구/인테리어>수예>자수>원단
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: mini1013/master_domain
+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: accuracy
+      value: 1.0
+      name: Accuracy
+---
+
+# 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>'퀼트크로스백 퀼트완제품 가구/인테리어>수예>퀼트/펠트>완제품'</li><li>'현진 글리터토퍼 꽃길만걷자 GFT4-405 152094 가구/인테리어>수예>퀼트/펠트>완제품'</li><li>'스위티퀼트 퀼트 완제품 봄이 필통 파우치 가구/인테리어>수예>퀼트/펠트>완제품'</li></ul>                                             |
+| 4.0   | <ul><li>'쇼파 빈티지 요곤 가죽 질감 재킷 배경 가방 부드러운 안감천 레자원단 가구/인테리어>수예>원단'</li><li>'접착 레자 소파 고무 강력 자동차 인테리 인조 가죽 가구/인테리어>수예>원단'</li><li>'핸드메이드 가죽 소재 하운드투스 Y자 인조 PVC클러치 프린트 캐리어 DIY 가구/인테리어>수예>원단'</li></ul>                      |
+| 3.0   | <ul><li>'실밥뜯개 실뜯게 제거기 부자재 니퍼 실따개 바느질 마대바늘 모루인형눈 스킬바늘 재단가위 가구/인테리어>수예>수예용품/부자재'</li><li>'diy 가죽공예 세트 왁스실 가죽바늘 7종 가구/인테리어>수예>수예용품/부자재'</li><li>'단추 썬그립 500세트 똑딱이단추 고급 국산 티단추 스냅 선그립 79컬러 가구/인테리어>수예>수예용품/부자재'</li></ul> |
+| 1.0   | <ul><li>'도서 다루마 패턴북 6 가구/인테리어>수예>뜨개질>패키지'</li><li>'뜨개가방손잡이 우드 자연 큰 단단한 나무 잠금 가구/인테리어>수예>뜨개질>완제품'</li><li>'타월 담요 소파 손뜨개 여름 블랭킷 커버 코바늘 가구/인테리어>수예>뜨개질>완제품'</li></ul>                                                    |
+| 0.0   | <ul><li>'우돌아트 동물이름표 기린 네임텍 스텐실 도안 1243 가구/인테리어>수예>기타수예'</li><li>'모루 공예 재료 부드러운 모루 - 초��� 가구/인테리어>수예>기타수예'</li><li>'컬러점토 3개입 아모스 가구/인테리어>수예>기타수예'</li></ul>                                                              |
+| 5.0   | <ul><li>'OOE 덴마크 꽃실 자수실 510 727 가구/인테리어>수예>자수>실/바늘'</li><li>'데코샌드아트 명화도안 색모래 밤의 별매 중 X 2매입 가구/인테리어>수예>자수>도안'</li><li>'실십자수 동물 왕 사자 가족 대형 십자수 세트 패키지 DIY만들기 30x40 11CT HMA56704 가구/인테리어>수예>자수>패키지'</li></ul>           |
+| 2.0   | <ul><li>'누니액자 보석십자수 패브릭형 액자 60x90 프리미엄 클래식실버 가구/인테리어>수예>보석십자수'</li><li>'돈그림 황금돈나무 거실 현관 행운의 풍수 금전운 그-D 40x80 가구/인테리어>수예>보석십자수'</li><li>'보석십자수 빗 가구/인테리어>수예>보석십자수'</li></ul>                                           |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 1.0      |
+
+## 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_fi6")
+# Run inference
+preds = model("탑키드 만들기 경찰관 놀이 세트 3인용 가구/인테리어>수예>기타수예")
+```
+
+<!--
+### 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   | 2   | 8.8714 | 24  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 70                    |
+| 1.0   | 70                    |
+| 2.0   | 70                    |
+| 3.0   | 70                    |
+| 4.0   | 70                    |
+| 5.0   | 70                    |
+| 6.0   | 70                    |
+
+### Training Hyperparameters
+- batch_size: (256, 256)
+- num_epochs: (30, 30)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 50
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch   | Step | Training Loss | Validation Loss |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0104  | 1    | 0.5007        | -               |
+| 0.5208  | 50   | 0.4969        | -               |
+| 1.0417  | 100  | 0.4332        | -               |
+| 1.5625  | 150  | 0.0551        | -               |
+| 2.0833  | 200  | 0.0001        | -               |
+| 2.6042  | 250  | 0.0           | -               |
+| 3.125   | 300  | 0.0           | -               |
+| 3.6458  | 350  | 0.0           | -               |
+| 4.1667  | 400  | 0.0           | -               |
+| 4.6875  | 450  | 0.0           | -               |
+| 5.2083  | 500  | 0.0           | -               |
+| 5.7292  | 550  | 0.0           | -               |
+| 6.25    | 600  | 0.0           | -               |
+| 6.7708  | 650  | 0.0           | -               |
+| 7.2917  | 700  | 0.0           | -               |
+| 7.8125  | 750  | 0.0           | -               |
+| 8.3333  | 800  | 0.0           | -               |
+| 8.8542  | 850  | 0.0           | -               |
+| 9.375   | 900  | 0.0           | -               |
+| 9.8958  | 950  | 0.0           | -               |
+| 10.4167 | 1000 | 0.0           | -               |
+| 10.9375 | 1050 | 0.0           | -               |
+| 11.4583 | 1100 | 0.0           | -               |
+| 11.9792 | 1150 | 0.0           | -               |
+| 12.5    | 1200 | 0.0           | -               |
+| 13.0208 | 1250 | 0.0           | -               |
+| 13.5417 | 1300 | 0.0           | -               |
+| 14.0625 | 1350 | 0.0           | -               |
+| 14.5833 | 1400 | 0.0           | -               |
+| 15.1042 | 1450 | 0.0           | -               |
+| 15.625  | 1500 | 0.0           | -               |
+| 16.1458 | 1550 | 0.0           | -               |
+| 16.6667 | 1600 | 0.0           | -               |
+| 17.1875 | 1650 | 0.0           | -               |
+| 17.7083 | 1700 | 0.0           | -               |
+| 18.2292 | 1750 | 0.0           | -               |
+| 18.75   | 1800 | 0.0           | -               |
+| 19.2708 | 1850 | 0.0           | -               |
+| 19.7917 | 1900 | 0.0           | -               |
+| 20.3125 | 1950 | 0.0           | -               |
+| 20.8333 | 2000 | 0.0           | -               |
+| 21.3542 | 2050 | 0.0           | -               |
+| 21.875  | 2100 | 0.0           | -               |
+| 22.3958 | 2150 | 0.0           | -               |
+| 22.9167 | 2200 | 0.0           | -               |
+| 23.4375 | 2250 | 0.0           | -               |
+| 23.9583 | 2300 | 0.0           | -               |
+| 24.4792 | 2350 | 0.0           | -               |
+| 25.0    | 2400 | 0.0           | -               |
+| 25.5208 | 2450 | 0.0           | -               |
+| 26.0417 | 2500 | 0.0           | -               |
+| 26.5625 | 2550 | 0.0           | -               |
+| 27.0833 | 2600 | 0.0           | -               |
+| 27.6042 | 2650 | 0.0           | -               |
+| 28.125  | 2700 | 0.0           | -               |
+| 28.6458 | 2750 | 0.0           | -               |
+| 29.1667 | 2800 | 0.0           | -               |
+| 29.6875 | 2850 | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0
+- Sentence Transformers: 3.3.1
+- Transformers: 4.44.2
+- PyTorch: 2.2.0a0+81ea7a4
+- Datasets: 3.2.0
+- Tokenizers: 0.19.1
+
+## 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_fi",
+  "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": "4.44.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.2.0a0+81ea7a4"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+{
+  "normalize_embeddings": false,
+  "labels": null
+}

model.safetensors

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+size 442494816

model_head.pkl

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+size 43935

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,
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+    "single_word": false
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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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+{
+  "added_tokens_decoder": {
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+      "content": "[CLS]",
+      "lstrip": false,
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+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
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+  "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",
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+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}