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 +1,295 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 스타 더프로페셔널 축구공 5호 SB3015 스포츠/레저>축구>축구공
+- text: 미즈노 미즈노 스피드 마하 2 주니어 런닝화 K1GC2222-51 스포츠/레저>축구>축구화
+- text: 스타 풋살공 매치업 접착구 4호 스쿠알로 공가방 스쿠알로 펌프 FB514TB 스포츠/레저>축구>축구공
+- text: 타비오 TABIO 축구 발가락 양말 풋살 양말 숏 그립 삭스 스포츠/레저>축구>축구양말
+- text: 스타 매니아 풋살공 FB61 볼 축구공 싸커 피구 발야구 WBA134E 스포츠/레저>축구>축구공
+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                                                                                                                                                                                                          |
+|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 9.0   | <ul><li>'엄브로 풋살화 주니어 엑셀레이터 사라 WIDE IN UF2V02W 스포츠/레저>축구>풋살화'</li><li>'푸마 퓨처 7 플레이 TT V 주니어 10774001 스포츠/레저>축구>풋살화'</li><li>'아동 키즈 주니어 풋살화 아식스 1154A090 011 스포츠/레저>축구>풋살화'</li></ul>                               |
+| 6.0   | <ul><li>'22-23시즌 파리생제르망 메시유니폼 네이마르 음바페 아동용 성인용 유소년 축구유니폼 키즈 PSG 스포츠/레저>축구>축구의류'</li><li>'토트넘 유니폼 어웨이 축구유니폼 반팔 어센틱 스포츠/레저>축구>축구의류'</li><li>'미즈노 베이직 디자인 남여공용 빅로고 라운드넥 피스테 3C 그린 32YE3020 스포츠/레저>축구>축구의류'</li></ul> |
+| 5.0   | <ul><li>'바주카골 고급 팝업 골대 충격흡수 접이식 휴대용 풋살 동호회 학교 체육 튼튼한 교구 스포츠/레저>축구>축구연습용품'</li><li>'프로맥스 풋살골대 축구 미니게임 훈련 골대 6300TA2 스포츠/레저>축구>축구연습용품'</li><li>'축구 접시콘 스포츠/레저>축구>축구연습용품'</li></ul>                                  |
+| 2.0   | <ul><li>'풋살공 게임볼 4호 풋살볼 풋살 축구용품 스타 축구 FB52405 스포츠/레저>축구>축구공'</li><li>'스타 축구공 더 폴라리스 2000 4호 흰색노랑색 SB234 스포츠/레저>축구>축구공'</li><li>'키카 이글200 축구공 그린 KFS-N102 스포츠/레저>축구>축구공'</li></ul>                                 |
+| 8.0   | <ul><li>'미즈노 다이나팩 모렐리아 2 프로 AS 풋살화 형광 GD241445 270 스포츠/레저>축구>축구화'</li><li>'미즈노 축구화 모렐리아 네오 3 프로 MD P1GA238304 스포츠/레저>축구>축구화'</li><li>'아디다스 코파 센스 1 HG 축구화 FZ3712 스포츠/레저>축구>��구화'</li></ul>                         |
+| 3.0   | <ul><li>'PARIS SAINT-GERMAIN 비밀 파리생제르망 신가드 S M L SN-01 스포츠/레저>축구>축구보호대'</li><li>'미즈노 축구용품 신가드 제로글라이드 정강이보호대 스포츠/레저>축구>축구보호대'</li><li>'풋볼몬스터 신가드 슬리브 2p 정강이보호대 다리보호대 스포츠/레저>축구>축구보호대'</li></ul>                    |
+| 1.0   | <ul><li>'TOP셀러 더블백 더플백 헬스 헬스장 망치 가방 짐백 축구 복싱 운동 가방 스포츠/레저>축구>축구가방>필드용'</li><li>'디아도라 테니스 보스턴백 OFF 스포츠/레저>축구>축구가방>필드용'</li><li>'스포츠트라이브 짐백 축구공가방 공가방 신발주머니 스포츠/레저>축구>축구가방>슈즈백'</li></ul>                           |
+| 4.0   | <ul><li>'성인용 논슬립 양말 스포츠/레저>축구>축구양말'</li><li>'훌리건 싱글 논슬립 스타킹 hss001whns2 축구 양말 스포츠/레저>축구>축구양말'</li><li>'아디다스 성인 어른 축구화 풋살 양말 스타킹 260 남성 장목 양발 경기 남자 삭스 스포츠/레저>축구>축구양말'</li></ul>                                   |
+| 0.0   | <ul><li>'동호회 체육대회 축구 심판 접이식 수첩 경고카드 연필 심판용품 스포츠/레저>축구>기타축구용품'</li><li>'볼 트래핑 축구 연습 리프팅 연습 2호 5호 어린이 축구교실 풋살 트레인 훈련 스포츠/레저>축구>기타축구용품'</li><li>'선수교체판 번호사인 스포츠/레저>축구>기타축구용품'</li></ul>                              |
+| 7.0   | <ul><li>'미즈노 모렐리아 축구 풋살 겨울 방한 남성 필드 장갑 스포츠/레저>축구>축구장갑'</li><li>'나이키 매치 FA20 골키퍼장갑 CQ7799-637 스포츠/레저>축구>축구장갑'</li><li>'푸마 퓨쳐 얼티메이트 NC 골키퍼장갑 04192301 스포츠/레저>축구>축구장갑'</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_sl27")
+# Run inference
+preds = model("스타 더프로페셔널 축구공 5호 SB3015 스포츠/레저>축구>축구공")
+```
+
+<!--
+### 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   | 8.7243 | 20  |
+
+| 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                    |
+| 7.0   | 70                    |
+| 8.0   | 70                    |
+| 9.0   | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0073  | 1    | 0.4831        | -               |
+| 0.3650  | 50   | 0.4992        | -               |
+| 0.7299  | 100  | 0.3805        | -               |
+| 1.0949  | 150  | 0.1114        | -               |
+| 1.4599  | 200  | 0.0275        | -               |
+| 1.8248  | 250  | 0.0177        | -               |
+| 2.1898  | 300  | 0.0104        | -               |
+| 2.5547  | 350  | 0.0014        | -               |
+| 2.9197  | 400  | 0.0001        | -               |
+| 3.2847  | 450  | 0.0001        | -               |
+| 3.6496  | 500  | 0.0001        | -               |
+| 4.0146  | 550  | 0.0001        | -               |
+| 4.3796  | 600  | 0.0           | -               |
+| 4.7445  | 650  | 0.0           | -               |
+| 5.1095  | 700  | 0.0           | -               |
+| 5.4745  | 750  | 0.0           | -               |
+| 5.8394  | 800  | 0.0           | -               |
+| 6.2044  | 850  | 0.0           | -               |
+| 6.5693  | 900  | 0.0           | -               |
+| 6.9343  | 950  | 0.0           | -               |
+| 7.2993  | 1000 | 0.0           | -               |
+| 7.6642  | 1050 | 0.0           | -               |
+| 8.0292  | 1100 | 0.0           | -               |
+| 8.3942  | 1150 | 0.0           | -               |
+| 8.7591  | 1200 | 0.0           | -               |
+| 9.1241  | 1250 | 0.0           | -               |
+| 9.4891  | 1300 | 0.0           | -               |
+| 9.8540  | 1350 | 0.0           | -               |
+| 10.2190 | 1400 | 0.0           | -               |
+| 10.5839 | 1450 | 0.0           | -               |
+| 10.9489 | 1500 | 0.0           | -               |
+| 11.3139 | 1550 | 0.0           | -               |
+| 11.6788 | 1600 | 0.0           | -               |
+| 12.0438 | 1650 | 0.0           | -               |
+| 12.4088 | 1700 | 0.0           | -               |
+| 12.7737 | 1750 | 0.0           | -               |
+| 13.1387 | 1800 | 0.0           | -               |
+| 13.5036 | 1850 | 0.0           | -               |
+| 13.8686 | 1900 | 0.0           | -               |
+| 14.2336 | 1950 | 0.0           | -               |
+| 14.5985 | 2000 | 0.0           | -               |
+| 14.9635 | 2050 | 0.0           | -               |
+| 15.3285 | 2100 | 0.0           | -               |
+| 15.6934 | 2150 | 0.0           | -               |
+| 16.0584 | 2200 | 0.0           | -               |
+| 16.4234 | 2250 | 0.0           | -               |
+| 16.7883 | 2300 | 0.0           | -               |
+| 17.1533 | 2350 | 0.0           | -               |
+| 17.5182 | 2400 | 0.0           | -               |
+| 17.8832 | 2450 | 0.0           | -               |
+| 18.2482 | 2500 | 0.0           | -               |
+| 18.6131 | 2550 | 0.0           | -               |
+| 18.9781 | 2600 | 0.0           | -               |
+| 19.3431 | 2650 | 0.0           | -               |
+| 19.7080 | 2700 | 0.0           | -               |
+| 20.0730 | 2750 | 0.0           | -               |
+| 20.4380 | 2800 | 0.0           | -               |
+| 20.8029 | 2850 | 0.0           | -               |
+| 21.1679 | 2900 | 0.0           | -               |
+| 21.5328 | 2950 | 0.0           | -               |
+| 21.8978 | 3000 | 0.0           | -               |
+| 22.2628 | 3050 | 0.0           | -               |
+| 22.6277 | 3100 | 0.0           | -               |
+| 22.9927 | 3150 | 0.0           | -               |
+| 23.3577 | 3200 | 0.0           | -               |
+| 23.7226 | 3250 | 0.0           | -               |
+| 24.0876 | 3300 | 0.0           | -               |
+| 24.4526 | 3350 | 0.0           | -               |
+| 24.8175 | 3400 | 0.0           | -               |
+| 25.1825 | 3450 | 0.0           | -               |
+| 25.5474 | 3500 | 0.0           | -               |
+| 25.9124 | 3550 | 0.0           | -               |
+| 26.2774 | 3600 | 0.0           | -               |
+| 26.6423 | 3650 | 0.0           | -               |
+| 27.0073 | 3700 | 0.0           | -               |
+| 27.3723 | 3750 | 0.0           | -               |
+| 27.7372 | 3800 | 0.0           | -               |
+| 28.1022 | 3850 | 0.0           | -               |
+| 28.4672 | 3900 | 0.0           | -               |
+| 28.8321 | 3950 | 0.0           | -               |
+| 29.1971 | 4000 | 0.0           | -               |
+| 29.5620 | 4050 | 0.0           | -               |
+| 29.9270 | 4100 | 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 sha256:23c782a10e711d1fb5cce7626964c815b2d2be5486f5356ee040207c66ff9c78
+size 442494816

model_head.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:121fd0291de164749a2c1eac8f3e87937ae4eeefeceb8a2f322eca8dc9707ff3
+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]"
+}