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 +1,288 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 배드민턴 스윙연습 연습기 연습 훈련 트레이닝 손목 효과 스포츠/레저>배드민턴>연습용품
+- text: 요넥스 나노지 배드민턴스트링 NBG 98-2 200M 스포츠/레저>배드민턴>스트링
+- text: 동호회 배트민턴채 관리 교체용 롤스트링 배드민턴스트링 스포츠/레저>배드민턴>스트링
+- text: 배드민턴연습기 스윙 셀프 훈련 서브 트레이닝 혼자 레슨 스포츠/레저>배드민턴>연습용품
+- text: 키모니 납테이프 알파 플러스 라켓 밸런스 테이프 KBN261 스포츠/레저>배드민턴>기타배드민턴용품
+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:** 10 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                                                                                                                                                                                               |
+|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 8.0   | <ul><li>'토알슨 배드민턴 롤 스트링 거트 200m BL-6700 스포츠/레저>배드민턴>스트링'</li><li>'배드민턴줄교체 테니스 리폼 조정 스트링 기계 텐션 스포츠/레저>배드민턴>스트링'</li><li>'테니스 거트 라켓줄 라켓 스트링 1 25 원형 네츄럴 스포츠/레저>배드민턴>스트링'</li></ul>                       |
+| 6.0   | <ul><li>'요넥스 이클립션Z 미드 남녀공용 배드민턴화 스포츠/레저>배드민턴>배드민턴화'</li><li>'미즈노 남여 배드민턴화 웨이브 스텔스 네오 에너지 스포츠/레저>배드민턴>배드민턴화'</li><li>'미즈노 배드민턴화 체대입시화 사이클론 스피드 3 BM10314574 스포츠/레저>배드민턴>배드민턴화'</li></ul>                |
+| 4.0   | <ul><li>'어태커 W-FORCE 배드민턴라켓 스포츠/레저>배드민턴>배드민턴라켓'</li><li>'미즈노 알티우스 01 필 배드민턴라켓 스포츠/레저>배드민턴>배드민턴라켓'</li><li>'TAAN 미라지 100 배드민턴라켓 스포츠/레저>배드민턴>배드민턴라켓'</li></ul>                                           |
+| 2.0   | <ul><li>'요넥스 테니스가방 베드민턴 백팩 라켓백 BAG2328-007 스포츠/레저>배드민턴>배드민턴가방'</li><li>'2023 요넥스 배드민턴 테니스 백팩 가방 BA02312EX 스포츠/레저>배드민턴>배드민턴가방'</li><li>'테크니스트 배드민턴 미니파우치 가방 TBS-32 TBS-33 스포츠/레저>배드민턴>배드민턴가방'</li></ul> |
+| 0.0   | <ul><li>'배드민턴 그립 밴드 라켓 손잡이 오버 테이프 스포츠/레저>배드민턴>그립'</li><li>'키모니 타올그립 1롤 10m 6컬러 KGT116 스포츠/레저>배드민턴>그립'</li><li>'배드민턴 골프 심 라켓 그립 테이프 테니스 손잡이 스포츠/레저>배드민턴>그립'</li></ul>                                   |
+| 1.0   | <ul><li>'낫소 배드민턴라켓 템테이션 카본 레저용 라켓세트 학교체육 스포츠/레저>배드민턴>기타배드민턴용품'</li><li>'익스트리모 LiNing 배드민턴 라켓 스트링 미포함 AYPM438-4 스포츠/레저>배드민턴>기타배드민턴용품'</li><li>'트라이온 스트링머신 X-700 스포츠/레저>배드민턴>기타배드민턴용품'</li></ul>         |
+| 7.0   | <ul><li>'셔틀콕 1박스 25타 배드민턴셔틀콕 KK7000 스포츠/레저>배드민턴>셔틀콕'</li><li>'요넥스 배드민턴 셔틀콕 FEATHER 12개입 AS-10EX 스포츠/레저>배드민턴>셔틀콕'</li><li>'경원 셔틀콕 배드민턴공 스포츠/레저>배드민턴>셔틀콕'</li></ul>                                      |
+| 3.0   | <ul><li>'니스포 멀티 지주네트 5m 세트 스포츠/레저>배드민턴>배드민턴네트'</li><li>'런웨이브 배드민턴 네트 LW-0187 스포츠/레저>배드민턴>배드민턴네트'</li><li>'이고진 배드민턴 네트 스포츠/레저>배드민턴>배드민턴네트'</li></ul>                                                    |
+| 9.0   | <ul><li>'배드민턴 연습기 트레이닝 성인 초등학생 리턴콕 실내 3 5m 기본형 공1 야광1 스포츠/레저>배드민턴>연습용품'</li><li>'배드민턴 스윙 연습기 셀프 트레이닝 훈련 스매싱 스포츠/레저>배드민턴>연습용품'</li><li>'배드민턴 셀프 트레이닝 스파링 연습기 스매싱 서브 스포츠/레저>배드민턴>연습용품'</li></ul>         |
+| 5.0   | <ul><li>'패기앤코 남성 티셔츠 DT-118 스포츠/레저>배드민턴>배드민턴의류'</li><li>'요넥스 아노락 바람막이 긴팔 티셔츠 BE 231JJ003U 스포츠/레저>배드민턴>배드민턴의류'</li><li>'요넥스 여성 바람막이 긴팔티셔츠 반바지 233JJ002U 231PH002FNV 스포츠/레저>배드민턴>배드민턴의류'</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_sl12")
+# Run inference
+preds = model("요넥스 나노지 배드민턴스트링 NBG 98-2 200M 스포츠/레저>배드민턴>스트링")
+```
+
+<!--
+### 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   | 8.0186 | 22  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 70                    |
+| 1.0   | 70                    |
+| 2.0   | 70                    |
+| 3.0   | 16                    |
+| 4.0   | 70                    |
+| 5.0   | 70                    |
+| 6.0   | 70                    |
+| 7.0   | 70                    |
+| 8.0   | 70                    |
+| 9.0   | 69                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0079  | 1    | 0.475         | -               |
+| 0.3968  | 50   | 0.4972        | -               |
+| 0.7937  | 100  | 0.2864        | -               |
+| 1.1905  | 150  | 0.1285        | -               |
+| 1.5873  | 200  | 0.0559        | -               |
+| 1.9841  | 250  | 0.0233        | -               |
+| 2.3810  | 300  | 0.007         | -               |
+| 2.7778  | 350  | 0.0026        | -               |
+| 3.1746  | 400  | 0.0006        | -               |
+| 3.5714  | 450  | 0.0004        | -               |
+| 3.9683  | 500  | 0.0002        | -               |
+| 4.3651  | 550  | 0.0001        | -               |
+| 4.7619  | 600  | 0.0001        | -               |
+| 5.1587  | 650  | 0.0001        | -               |
+| 5.5556  | 700  | 0.0001        | -               |
+| 5.9524  | 750  | 0.0001        | -               |
+| 6.3492  | 800  | 0.0001        | -               |
+| 6.7460  | 850  | 0.0002        | -               |
+| 7.1429  | 900  | 0.0001        | -               |
+| 7.5397  | 950  | 0.0001        | -               |
+| 7.9365  | 1000 | 0.0           | -               |
+| 8.3333  | 1050 | 0.0           | -               |
+| 8.7302  | 1100 | 0.0           | -               |
+| 9.1270  | 1150 | 0.0           | -               |
+| 9.5238  | 1200 | 0.0           | -               |
+| 9.9206  | 1250 | 0.0           | -               |
+| 10.3175 | 1300 | 0.0           | -               |
+| 10.7143 | 1350 | 0.0           | -               |
+| 11.1111 | 1400 | 0.0           | -               |
+| 11.5079 | 1450 | 0.0           | -               |
+| 11.9048 | 1500 | 0.0           | -               |
+| 12.3016 | 1550 | 0.0           | -               |
+| 12.6984 | 1600 | 0.0           | -               |
+| 13.0952 | 1650 | 0.0           | -               |
+| 13.4921 | 1700 | 0.0           | -               |
+| 13.8889 | 1750 | 0.0           | -               |
+| 14.2857 | 1800 | 0.0           | -               |
+| 14.6825 | 1850 | 0.0           | -               |
+| 15.0794 | 1900 | 0.0           | -               |
+| 15.4762 | 1950 | 0.0           | -               |
+| 15.8730 | 2000 | 0.0           | -               |
+| 16.2698 | 2050 | 0.0           | -               |
+| 16.6667 | 2100 | 0.0           | -               |
+| 17.0635 | 2150 | 0.0           | -               |
+| 17.4603 | 2200 | 0.0           | -               |
+| 17.8571 | 2250 | 0.0           | -               |
+| 18.2540 | 2300 | 0.0           | -               |
+| 18.6508 | 2350 | 0.0           | -               |
+| 19.0476 | 2400 | 0.0           | -               |
+| 19.4444 | 2450 | 0.0           | -               |
+| 19.8413 | 2500 | 0.0           | -               |
+| 20.2381 | 2550 | 0.0           | -               |
+| 20.6349 | 2600 | 0.0           | -               |
+| 21.0317 | 2650 | 0.0           | -               |
+| 21.4286 | 2700 | 0.0           | -               |
+| 21.8254 | 2750 | 0.0           | -               |
+| 22.2222 | 2800 | 0.0           | -               |
+| 22.6190 | 2850 | 0.0           | -               |
+| 23.0159 | 2900 | 0.0           | -               |
+| 23.4127 | 2950 | 0.0           | -               |
+| 23.8095 | 3000 | 0.0           | -               |
+| 24.2063 | 3050 | 0.0           | -               |
+| 24.6032 | 3100 | 0.0           | -               |
+| 25.0    | 3150 | 0.0           | -               |
+| 25.3968 | 3200 | 0.0           | -               |
+| 25.7937 | 3250 | 0.0           | -               |
+| 26.1905 | 3300 | 0.0           | -               |
+| 26.5873 | 3350 | 0.0           | -               |
+| 26.9841 | 3400 | 0.0           | -               |
+| 27.3810 | 3450 | 0.0           | -               |
+| 27.7778 | 3500 | 0.0           | -               |
+| 28.1746 | 3550 | 0.0           | -               |
+| 28.5714 | 3600 | 0.0           | -               |
+| 28.9683 | 3650 | 0.0           | -               |
+| 29.3651 | 3700 | 0.0           | -               |
+| 29.7619 | 3750 | 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_sl_org_gtcate",
+  "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

@@ -0,0 +1,10 @@
+{
+  "__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

@@ -0,0 +1,4 @@
+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b835ebd07245976cd1dbf97120cf64a90c22a78af8abfd8ac6f4e83ffa1a6029
+size 442494816

model_head.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:98fb4d2bc25d2893cc3a8ce242b706b8dd7ff35c8bffcad00724507edd6faf6e
+size 62407

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "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

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "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

@@ -0,0 +1,66 @@
+{
+  "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
+    },
+    "2": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "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]"
+}