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: 파고라 테라스 조립식 정자 방갈로 펜션 바베큐장 정원 팔각정 원두막 가구/인테리어>아웃도어가구>정자
+- text: 흔들 의자 회전 공중 바구니 그네 라탄 베란다 로맨 -화이트-팔걸이 있음 가구/인테리어>아웃도어가구>정원그네
+- text: 엔틱 철제 벤치 의자 등받이 정원 수목원 펜션 가구/인테리어>아웃도어가구>야외벤치
+- text: 공원벤치 야외의자 철제 원목 방부목 평벤치 등받이 휴식 긴의자 테이블 세트 옥외용 가구/인테리어>아웃도어가구>야외벤치
+- text: 폴리카보네이트 렉산골판 비가림 지붕자재 차양 FRP 가구/인테리어>아웃도어가구>기타아웃도어가구
+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>'최화정 썬베드 피크닉 낮잠 비치체어 캠핑 접이식 휴대용 좌식의자 야외 가구/인테리어>아웃도어가구>야외의자'</li><li>'편의점플라스틱의자비치행사야외캠핑주점테이블파라솔 가구/인테리어>아웃도어가구>야외의자'</li><li>'수영장 카바나 풀파티 펜션 대형 소파 호텔 비치 태닝의자 카페 베드 썬베드 가구/인테리어>아웃도어가구>야외의자'</li></ul>                                                |
+| 0.0   | <ul><li>'아크릴 투명 어닝 고정 테라스 차양 현관 캐노피 베이 가구/인테리어>아웃도어가구>기타아웃도어가구'</li><li>'캐노픽스 렉산 차양 비막이 비가림 어닝 650X2100 가구/인테리어>아웃도어가구>기타아웃도어가구'</li><li>'기와 플라스틱 지붕자재 한옥 옛 중국식 기와 주택 벽장식 가구/인테리어>아웃도어가구>기타아웃도어가구'</li></ul>                                                  |
+| 5.0   | <ul><li>'조립식정자 펜션 정원 정자만들기 조립식-3x3 미터 4 기둥 파빌리온 3면 시트 보드 테이블 가구/인테리어>아웃도어가구>정자'</li><li>'조립식평상 테라스 낚시터 세트 옥상 원목 정원 정자 접이식 베란다 피크닉 가구/인테리어>아웃도어가구>정자'</li><li>'시골 원두막 가구/인테리어>아웃도어가구>정자'</li></ul>                                                             |
+| 4.0   | <ul><li>'야외 흔들의자 그네 벤치 원목 나무 카페 정원 펜션 가구/인테리어>아웃도어가구>정원그네'</li><li>'야외 흔들 의자 스윙체어 소파 흔들그네 카페 차양 가구/인테리어>아웃도어가구>정원그네'</li><li>'라탄 그네의자 덩굴의자 쿠션 요람 흔들의자 새둥지 가구/인테리어>아웃도어가구>정원그네'</li></ul>                                                                      |
+| 3.0   | <ul><li>'체어팩토리 마누카 테이블 600 철재 야외 테라스 정원 커피숍 카페 업소용 T5192 가구/인테리어>아웃도어가구>야외테이블'</li><li>'라탄 원목 디자이너 의자 테이블 세트 야외 방수 파 -의자4개 60cm스틸카본원탁조합 가구/인테리어>아웃도어가구>야외테이블'</li><li>'착한테이블 야외용테이블 원목 식탁 편의점 테라스 옥상 데크 4인 야외테이블 원목 농막 마당 테라스 가구/인테리어>아웃도어가구>야외테이블'</li></ul> |
+| 1.0   | <ul><li>'야외 벤치 의자 체어 정원 광장 공원 철제 카페 테라스 야외용 가구/인테리어>아웃도어가구>야외벤치'</li><li>'웨이팅의자 야외벤치 버스 정류장 벤치 카페 공원 투명 휴게실 가구/인테리어>아웃도어가구>야외벤치'</li><li>'야외 벤치 의자 공원 카페 테라스 나무 스테인레스 원목 정류장 평벤치 긴 대기 가구/인테리어>아웃도어가구>야외벤치'</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_fi8")
+# Run inference
+preds = model("엔틱 철제 벤치 의자 등받이 정원 수목원 펜션 가구/인테리어>아웃도어가구>야외벤치")
+```
+
+<!--
+### 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   | 10.1333 | 21  |
+
+| 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    | 0.4943        | -               |
+| 0.6024  | 50   | 0.497         | -               |
+| 1.2048  | 100  | 0.4986        | -               |
+| 1.8072  | 150  | 0.158         | -               |
+| 2.4096  | 200  | 0.015         | -               |
+| 3.0120  | 250  | 0.0001        | -               |
+| 3.6145  | 300  | 0.0           | -               |
+| 4.2169  | 350  | 0.0           | -               |
+| 4.8193  | 400  | 0.0           | -               |
+| 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 | 0.0           | -               |
+| 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_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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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,
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+  }
+}

tokenizer_config.json

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