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 +1,275 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 가퍼 스포츠 낚시 벨트 어깨 하 해상 스탠드업 물고기 싸움 로드 홀더 스포츠/레저>낚시>낚시의류/잡화>힙커버/힙가드
+- text: 낚시 태클박스 36리터 세트8 초경량 멀티 테이블 의자 받침대 루어 민물 바다 케리어 BSS158-3 스포츠/레저>낚시>낚시용품>태클박스
+- text: 메이저 크래프트 자이언트 킬링 Major Craft GK5SJ-B663 스포츠/레저>낚시>루어낚시>루어낚시세트
+- text: 갸프 낚싯대 용골 핸들 땀 흡수 스트랩 미끄럼 방지 절연 라켓 손잡이 커버 스포츠/레저>낚시>낚시용품>가프
+- text: 송어베이스 루어 세트 스푼 미끼 스피너 보빈 인공 스포츠/레저>낚시>루어낚시>루어낚시세트
+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:** 8 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                                                                                                                                                                                                                    |
+|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 7.0   | <ul><li>'다이와 DAIWA 한국다이와정공 소품케이스 클리어 파우치 S C 스포츠/레저>낚시>바다낚시>찌케이스'</li><li>'갓포스 고급 루어 낚시가방 루어대 원투대 하드 로드케이스 낚시대수납 단품 112CM-157CM 스포츠/레저>낚시>바다낚시>바다낚시가방'</li><li>'다이와 포터블 휴대용 로드케이스 B 140R 스포츠/레저>낚시>바다낚시>바다낚시가방'</li></ul>   |
+| 3.0   | <ul><li>'이공조구 원 포인트 바다루어낚싯대 S180 스포츠/레저>낚시>낚싯대>바다루어낚싯대'</li><li>'엔에스 블랙 매직아이 슬로우피치 바다루어낚싯대 B-592H3MF 스포츠/레저>낚시>낚싯대>바다루어낚싯대'</li><li>'은성 실스타 DHC 명파S 민물낚싯대 30칸 스포츠/레저>낚시>낚싯대>민물낚싯대'</li></ul>                                |
+| 1.0   | <ul><li>'메이호 태클박스 루어케이스 도구통 지그통 VS-388DD 스포츠/레저>낚시>낚시용품>태클박스'</li><li>'다이와 쿨라인 알파 3 펄 TS2000 스포츠/레저>낚시>낚시용품>쿨백'</li><li>'슬라이드 낚시 쪽가위 라인커터기 합사가위 T74464474 스포츠/레저>낚시>낚시공구>가위/라인커터/핀온릴'</li></ul>                             |
+| 5.0   | <ul><li>'다미끼 맘바2 러버지그-배스 루어 민물루어 1 2oz 스포츠/레저>낚시>루어낚시>하드베이트'</li><li>'루어 낚시 가물치 배스 5pcs 개구리 세트 프로그 스포츠/레저>낚시>루어낚시>루어낚시세트'</li><li>'KFP 미노우 KS01 하드베이트 싱킹타입 루어 포퍼 웜 크랭크 프로팅 싱킹 배스 미끼 농어 베이트 스포츠/레저>낚시>루어낚시>하드베이트'</li></ul>  |
+| 0.0   | <ul><li>'다이와 레브로스 스피닝릴 LT2500D-XH 스포츠/레저>낚시>낚시릴>스피닝릴'</li><li>'바낙스 LJ100x 장구통릴  티탄 스포츠/레저>낚시>낚시릴>베이트릴'</li><li>'시마노 FX 1000 스피닝릴 스포츠/레저>낚시>낚시릴>스피닝릴'</li></ul>                                                              |
+| 4.0   | <ul><li>'가마라 쇼크리더 카본 목줄 50m 6호 GFLUORO506 스포츠/레저>낚시>낚싯줄>카본라인'</li><li>'선라인 토네이도 마츠다 스페셜 블랙 스트림 낚싯줄 70m 1.75호 스포츠/레저>낚시>낚싯줄>카본라인'</li><li>'선라인 슈터 FC 스나이퍼 100m 4.5LB 스포츠/레저>낚시>낚싯줄>카본라인'</li></ul>                           |
+| 2.0   | <ul><li>'다이와 낚시화 부츠 운동화 스파이크 슈즈 DAIWA 일본직구 DS-2150CD 스포츠/레저>낚시>낚시의류/잡화>낚시신발'</li><li>'HDF 해동 피나투라 올컷 방한 덮개장갑 낚시장갑 스포츠/레저>낚시>낚시의류/잡화>낚시장갑'</li><li>'가마가츠 낚시 코듀라 힙가드 로우백 타입 단일사이즈 GM3727 스포츠/레저>낚시>낚시의류/잡화>힙커버/힙가드'</li></ul> |
+| 6.0   | <ul><li>'루웍스 빙어 초릿대 23cm 스포츠/레저>낚시>민물낚시>얼음낚시'</li><li>'바다 민물 고기 낚시대 보관 수납 가방 하드케이스 스포츠/레저>낚시>민물낚시>민물낚시가방'</li><li>'고급 내림찌케이스 대형찌보관함 플로팅 보관박스 스포츠/레저>낚시>민물낚시>찌케이스'</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_sl4")
+# Run inference
+preds = model("송어베이스 루어 세트 스푼 미끼 스피너 보빈 인공 스포츠/레저>낚시>루어낚시>루어낚시세트")
+```
+
+<!--
+### 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   | 7.8018 | 19  |
+
+| 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                    |
+
+### 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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0091  | 1    | 0.4946        | -               |
+| 0.4545  | 50   | 0.5017        | -               |
+| 0.9091  | 100  | 0.2322        | -               |
+| 1.3636  | 150  | 0.0559        | -               |
+| 1.8182  | 200  | 0.0182        | -               |
+| 2.2727  | 250  | 0.0165        | -               |
+| 2.7273  | 300  | 0.0018        | -               |
+| 3.1818  | 350  | 0.0001        | -               |
+| 3.6364  | 400  | 0.0001        | -               |
+| 4.0909  | 450  | 0.0001        | -               |
+| 4.5455  | 500  | 0.0           | -               |
+| 5.0     | 550  | 0.0           | -               |
+| 5.4545  | 600  | 0.0           | -               |
+| 5.9091  | 650  | 0.0           | -               |
+| 6.3636  | 700  | 0.0           | -               |
+| 6.8182  | 750  | 0.0           | -               |
+| 7.2727  | 800  | 0.0           | -               |
+| 7.7273  | 850  | 0.0           | -               |
+| 8.1818  | 900  | 0.0           | -               |
+| 8.6364  | 950  | 0.0           | -               |
+| 9.0909  | 1000 | 0.0           | -               |
+| 9.5455  | 1050 | 0.0           | -               |
+| 10.0    | 1100 | 0.0           | -               |
+| 10.4545 | 1150 | 0.0           | -               |
+| 10.9091 | 1200 | 0.0           | -               |
+| 11.3636 | 1250 | 0.0           | -               |
+| 11.8182 | 1300 | 0.0           | -               |
+| 12.2727 | 1350 | 0.0           | -               |
+| 12.7273 | 1400 | 0.0           | -               |
+| 13.1818 | 1450 | 0.0           | -               |
+| 13.6364 | 1500 | 0.0           | -               |
+| 14.0909 | 1550 | 0.0           | -               |
+| 14.5455 | 1600 | 0.0           | -               |
+| 15.0    | 1650 | 0.0           | -               |
+| 15.4545 | 1700 | 0.0           | -               |
+| 15.9091 | 1750 | 0.0           | -               |
+| 16.3636 | 1800 | 0.0           | -               |
+| 16.8182 | 1850 | 0.0           | -               |
+| 17.2727 | 1900 | 0.0           | -               |
+| 17.7273 | 1950 | 0.0           | -               |
+| 18.1818 | 2000 | 0.0           | -               |
+| 18.6364 | 2050 | 0.0           | -               |
+| 19.0909 | 2100 | 0.0           | -               |
+| 19.5455 | 2150 | 0.0           | -               |
+| 20.0    | 2200 | 0.0           | -               |
+| 20.4545 | 2250 | 0.0           | -               |
+| 20.9091 | 2300 | 0.0           | -               |
+| 21.3636 | 2350 | 0.0           | -               |
+| 21.8182 | 2400 | 0.0           | -               |
+| 22.2727 | 2450 | 0.0           | -               |
+| 22.7273 | 2500 | 0.0           | -               |
+| 23.1818 | 2550 | 0.0           | -               |
+| 23.6364 | 2600 | 0.0           | -               |
+| 24.0909 | 2650 | 0.0           | -               |
+| 24.5455 | 2700 | 0.0           | -               |
+| 25.0    | 2750 | 0.0           | -               |
+| 25.4545 | 2800 | 0.0           | -               |
+| 25.9091 | 2850 | 0.0           | -               |
+| 26.3636 | 2900 | 0.0           | -               |
+| 26.8182 | 2950 | 0.0           | -               |
+| 27.2727 | 3000 | 0.0           | -               |
+| 27.7273 | 3050 | 0.0           | -               |
+| 28.1818 | 3100 | 0.0           | -               |
+| 28.6364 | 3150 | 0.0           | -               |
+| 29.0909 | 3200 | 0.0           | -               |
+| 29.5455 | 3250 | 0.0           | -               |
+| 30.0    | 3300 | 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

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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 50087

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
+  },
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+    "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,
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+    "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
+    },
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+      "lstrip": false,
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+      "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]"
+}