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 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 발열양말 방한 보온양말 등산 낚시 스키 스노우보드 스케이트 야외작업 스포츠/레저>스키/보드>스키/보드방한용품>양말
+- text: 무크 엠 무크 펠로 데크 다크네이비 517413203ZB 스포츠/레저>스키/보드>스노보드장비>데크
+- text: 스키복 성인 자켓 상의 여성용 JACKET 스키자켓 남성 스포츠/레저>스키/보드>스키복>상의
+- text: Toko Edge Tuner Pro 스노우보드 엣지 튜닝 컷팅 스포츠/레저>스키/보드>스키/보드용품>보수장비
+- text: 헬리아 주니어 고글 카이로스 무광퍼플블랙 보드고글 스포츠고글 스포츠/레저>스키/보드>스키/보드용품>고글
+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:** 6 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                                                                                                                                                                                                                                         |
+|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 2.0   | <ul><li>'충전식 열선양말 발열 스키장 보드 스키 등산 스포츠/레저>스키/보드>스키/보드방한용품>양말'</li><li>'우주마켓 겨울 방한 마스크 보온 등산 골프 따뜻한 자전거 귀덮개 귀마개 마스크 스포츠/레저>스키/보드>스키/보드방한용품>귀마개'</li><li>'다이나핏 폴디드 스몰로고 비니 Dark 스포츠/레저>스키/보드>스키/보드방한용품>비니'</li></ul>                                |
+| 0.0   | <ul><li>'방풍 방수 여성 스노우 보드 플레이 여자 복 어스투 점퍼 점프 슈트 수트 스키 가프 스포츠/레저>스키/보드>보드복>재킷'</li><li>'2023 여성용 원피스 스키 슈트 겨울 야외 스포츠 방풍 방수 보온 스노보드 점프슈트 스포츠/레저>스키/보드>보드복>상하세트'</li><li>'여성용 스노우보드 점프수트 여성 일체형 스키복 방 -남성용 민트 그린 수트 스포츠/레저>스키/보드>보드복>상하세트'</li></ul> |
+| 5.0   | <ul><li>'2223 헤드 스키 PURE JOY 여성용 스포츠/레저>스키/보드>스키장비>플레이트'</li><li>'미니 스키 부츠 스케이트 썰매 스노우 숏부츠 스포츠/레저>스키/보드>스키장비>부츠'</li><li>'PHOENIX 피닉스 주니어 스키 팀복 2223 PHENIX KOREA JR TEAM RD 스포츠/레저>스키/보드>스키장비>플레이트'</li></ul>                                   |
+| 4.0   | <ul><li>'스키복 세트 여성 남성 방한 방풍 스포츠/레저>스키/보드>스키복>상하세트'</li><li>'스파이더 남성 보르미오 GTX 스키 팬츠 SPFWCISP401MBLK LE1216929158 스포츠/레저>스키/보드>스키복>하의'</li><li>'카르포스 스키바지 남자 겨울 2521013 스포츠/레저>스키/보드>스키복>하의'</li></ul>                                             |
+| 3.0   | <ul><li>'XCMAN 4겹콘 스터드 디아 7 87인치 알루미늄 스노우보드 스톰프 패드 9pcs 스포츠/레저>스키/보드>스키/보드용품>스티커용품'</li><li>'Thule RoundTrip 스키 스노보드 더플 백 90L 다크 슬레이트 142322 스포츠/레저>스키/보드>스키/보드용품>보드가방'</li><li>'ToeJamR 스노우보드 스톰프 패드 나비 스포츠/레저>스키/보드>스키/보드용품>스티커용품'</li></ul>   |
+| 1.0   | <ul><li>'스노우 스키 여성 부츠 보드 롱 털 따듯한 스노보드 스포츠/레저>스키/보드>스노보드장비>부츠'</li><li>'나이트로 팀 바인딩 2223 NITRO Team 스포츠/레저>스키/보드>스노보드장비>바인딩'</li><li>'헌터 WOMEN 인트레피드 리플렉티브 카모 숏 스노우부츠 - 패턴그레이 WFS1004PCTPTG 스포츠/레저>스키/보드>스노보드장비>부츠'</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_sl19")
+# Run inference
+preds = model("스키복 성인 자켓 상의 여성용 JACKET 스키자켓 남성 스포츠/레저>스키/보드>스키복>상의")
+```
+
+<!--
+### 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   | 9.4619 | 18  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0.0   | 70                    |
+| 1.0   | 70                    |
+| 2.0   | 70                    |
+| 3.0   | 70                    |
+| 4.0   | 70                    |
+| 5.0   | 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0120  | 1    | 0.4926        | -               |
+| 0.6024  | 50   | 0.497         | -               |
+| 1.2048  | 100  | 0.5003        | -               |
+| 1.8072  | 150  | 0.1918        | -               |
+| 2.4096  | 200  | 0.0218        | -               |
+| 3.0120  | 250  | 0.0004        | -               |
+| 3.6145  | 300  | 0.0003        | -               |
+| 4.2169  | 350  | 0.0001        | -               |
+| 4.8193  | 400  | 0.0001        | -               |
+| 5.4217  | 450  | 0.0           | -               |
+| 6.0241  | 500  | 0.0           | -               |
+| 6.6265  | 550  | 0.0           | -               |
+| 7.2289  | 600  | 0.0           | -               |
+| 7.8313  | 650  | 0.0           | -               |
+| 8.4337  | 700  | 0.0           | -               |
+| 9.0361  | 750  | 0.0           | -               |
+| 9.6386  | 800  | 0.0           | -               |
+| 10.2410 | 850  | 0.0           | -               |
+| 10.8434 | 900  | 0.0           | -               |
+| 11.4458 | 950  | 0.0           | -               |
+| 12.0482 | 1000 | 0.0           | -               |
+| 12.6506 | 1050 | 0.0001        | -               |
+| 13.2530 | 1100 | 0.0           | -               |
+| 13.8554 | 1150 | 0.0           | -               |
+| 14.4578 | 1200 | 0.0           | -               |
+| 15.0602 | 1250 | 0.0           | -               |
+| 15.6627 | 1300 | 0.0           | -               |
+| 16.2651 | 1350 | 0.0           | -               |
+| 16.8675 | 1400 | 0.0           | -               |
+| 17.4699 | 1450 | 0.0           | -               |
+| 18.0723 | 1500 | 0.0           | -               |
+| 18.6747 | 1550 | 0.0           | -               |
+| 19.2771 | 1600 | 0.0           | -               |
+| 19.8795 | 1650 | 0.0           | -               |
+| 20.4819 | 1700 | 0.0           | -               |
+| 21.0843 | 1750 | 0.0           | -               |
+| 21.6867 | 1800 | 0.0           | -               |
+| 22.2892 | 1850 | 0.0           | -               |
+| 22.8916 | 1900 | 0.0           | -               |
+| 23.4940 | 1950 | 0.0           | -               |
+| 24.0964 | 2000 | 0.0           | -               |
+| 24.6988 | 2050 | 0.0           | -               |
+| 25.3012 | 2100 | 0.0           | -               |
+| 25.9036 | 2150 | 0.0           | -               |
+| 26.5060 | 2200 | 0.0           | -               |
+| 27.1084 | 2250 | 0.0           | -               |
+| 27.7108 | 2300 | 0.0           | -               |
+| 28.3133 | 2350 | 0.0           | -               |
+| 28.9157 | 2400 | 0.0           | -               |
+| 29.5181 | 2450 | 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

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

model.safetensors

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model_head.pkl

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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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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+  "tokenizer_class": "BertTokenizer",
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+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}