Add SetFit model

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 +1,476 @@
+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+base_model: jhgan/ko-sbert-multitask
+metrics:
+- accuracy
+widget:
+- text: 제습기 전기세가 어느 정도 나오는지 궁금합니다. 요즘 비가 계속 오기도 했고 집 안의 습도가 너무 높아서 거의 하루 종일 제습기를 틀어놓고는
+    하는데 인터넷에서 제습기 전기세가 꽤나 많이 나온다는 내용의 글응 보았습니다. 제습기 전기세가 다른 가전제품에 비해서 어느 정도 나오는지 궁금합니다.
+- text: '우주선에 사용하는 전선은 일상생활에 사용하는것과 같은 재질인가요? 궁금한데요. 영화같은데보면 일반전선이랑 같은거같은데 실제로 그러진않겠죠?
+    지구대기랑 다른 악조건에 견디게 만들거같은데 어떨런지요? '
+- text: 부산 바다가 보이는 카페는 어떤 곳이 있나요? 다음달에 부산 여행을 갈 생각인데요. 부산 바다가 좀 보이는 카페를 가보고 싶어요. 하지만
+    너무 사람들이 많이 오는곳은 제외 하구요. 1층이 아니라 고층이면 더 좋습니다!
+- text: 경희대학교 토목공학과 전망 괜찮을까요? 경희대학교 토목공학과 전망 괜찮은지 궁금합니다.토목공학 자체가 지금 그렇게 좋은 느낌은 아닌
+    것 같은데..ㅠㅠ우스갯 소리로 잘되려면 통일이 되어야 토목공학과가 살아날 수 있다고 하던데ㅋ....그래도 학교 네임 밸류가 좋아서.. 괜찮을
+    것 같기도하고전망 어떤가요!?
+- text: 치아관리 치아가잇몸이올라가 치아뿌리분이조금보여요 제목그대로 치아뿌리가보이고 잇몸이 올라가있는거같아요 잇몸에좋은치료방법이있나요. 잇몸치약을쓰고있는데.
+    다른방법이있으면알려주세요
+pipeline_tag: text-classification
+inference: true
+model-index:
+- name: SetFit with jhgan/ko-sbert-multitask
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.5528846153846154
+      name: Accuracy
+---
+
+# SetFit with jhgan/ko-sbert-multitask
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [jhgan/ko-sbert-multitask](https://huggingface.co/jhgan/ko-sbert-multitask) 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:** [jhgan/ko-sbert-multitask](https://huggingface.co/jhgan/ko-sbert-multitask)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 128 tokens
+- **Number of Classes:** 208 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)
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.5529   |
+
+## 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("djsull/setfit_classifier")
+# Run inference
+preds = model("치아관리 치아가잇몸이올라가 치아뿌리분이조금보여요 제목그대로 치아뿌리가보이고 잇몸이 올라가있는거같아요 잇몸에좋은치료방법이있나요. 잇몸치약을쓰고있는데. 다른방법이있으면알려주세요")
+```
+
+<!--
+### 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   | 10  | 34.4464 | 468 |
+
+| Label     | Training Sample Count |
+|:----------|:----------------------|
+| PC·노트북    | 0                     |
+| 양도소득세     | 0                     |
+| 토목공학      | 7                     |
+| 책·독서      | 7                     |
+| 대인관계      | 7                     |
+| 기타 고민상담   | 7                     |
+| 공인노무사 자격증 | 7                     |
+| 서울·수도권    | 7                     |
+| 연세대학교     | 7                     |
+| 자동차 수리    | 7                     |
+| 반려동물 건강   | 7                     |
+| 예능        | 7                     |
+| 가압류·가처분   | 7                     |
+| 기타 육아상담   | 7                     |
+| 역사        | 7                     |
+| 손해사정사 자격증 | 7                     |
+| 환자 식단     | 7                     |
+| 서울시립대학교   | 7                     |
+| 기타 심리상담   | 0                     |
+| 형사        | 7                     |
+| 치과대학      | 7                     |
+| 예금·적금     | 7                     |
+| 물리        | 7                     |
+| 일본        | 7                     |
+| 낚시        | 7                     |
+| 현대중공업     | 7                     |
+| 교통사고      | 7                     |
+| 공인회계사 자격증 | 7                     |
+| 농구        | 7                     |
+| 종합부동산세    | 7                     |
+| 비뇨의학과     | 7                     |
+| 명예훼손·모욕   | 0                     |
+| 지게차운전기능사  | 7                     |
+| 가족·이혼     | 7                     |
+| 현대자동차     | 7                     |
+| 기계공학      | 7                     |
+| 영양제       | 7                     |
+| 연말정산      | 7                     |
+| 성균관대학교    | 7                     |
+| 미술        | 7                     |
+| 양육·훈육     | 7                     |
+| 경제용어      | 0                     |
+| 경희대학교     | 7                     |
+| 금융        | 7                     |
+| 스케이트      | 7                     |
+| 반려동물 훈련   | 7                     |
+| 한약        | 7                     |
+| 생물·생명     | 7                     |
+| 사회복지사 자격증 | 7                     |
+| 한의학       | 7                     |
+| 부동산·임대차   | 7                     |
+| 회생·파산     | 7                     |
+| 임금체불      | 7                     |
+| 변리사 자격증   | 7                     |
+| 피부과       | 7                     |
+| 놀이        | 7                     |
+| 대출        | 7                     |
+| 재산범죄      | 0                     |
+| 영유아·아동 식단 | 7                     |
+| 계절가전      | 7                     |
+| 재료공학      | 7                     |
+| 전라        | 7                     |
+| 다이어트 식단   | 7                     |
+| 경제정책      | 0                     |
+| 캠핑        | 7                     |
+| 철학        | 7                     |
+| 치과        | 7                     |
+| 서강대학교     | 7                     |
+| 자아·자기이해   | 0                     |
+| 환경·에너지    | 7                     |
+| 포항공과대학교   | 7                     |
+| 제주        | 7                     |
+| 행정사 자격증   | 7                     |
+| 임금·급여     | 7                     |
+| 세탁수선      | 0                     |
+| 중학교 생활    | 7                     |
+| 정보처리기사    | 7                     |
+| 수영        | 7                     |
+| 직장내괴롭힘    | 0                     |
+| 소아청소년과    | 7                     |
+| 재활·물리치료   | 7                     |
+| 부산        | 7                     |
+| 안과        | 7                     |
+| 런닝        | 0                     |
+| 지식재산권·IT  | 7                     |
+| 사진·영상     | 7                     |
+| 세무사 자격증   | 7                     |
+| 축구·풋살     | 7                     |
+| 미주·중남미    | 7                     |
+| 중앙대학교     | 7                     |
+| 음향기기      | 7                     |
+| 한화        | 7                     |
+| 세무조사·불복   | 7                     |
+| 교통사고 과실   | 0                     |
+| 서울대학교     | 7                     |
+| 의료        | 7                     |
+| 식습관·식이요법  | 7                     |
+| 구조조정      | 7                     |
+| 법인세       | 7                     |
+| 기타 영양���담   | 0                     |
+| 자동차       | 0                     |
+| KAIST     | 7                     |
+| 이비인후과     | 0                     |
+| 삼성        | 0                     |
+| 골프        | 7                     |
+| 경상        | 7                     |
+| 등산·클라이밍   | 7                     |
+| 기타 스포츠    | 7                     |
+| 성범죄       | 7                     |
+| GIST      | 7                     |
+| 가드닝       | 7                     |
+| 한국외국어대학교  | 7                     |
+| PC 주변기기   | 7                     |
+| 기타 장치     | 7                     |
+| 연애·이성     | 7                     |
+| 취득세·등록세   | 7                     |
+| 산부인과      | 7                     |
+| 기타 세금상담   | 0                     |
+| 헬스        | 7                     |
+| 간호조무사 자격증 | 7                     |
+| 전기기사·기능사  | 7                     |
+| 오세아니아     | 7                     |
+| 무역        | 7                     |
+| 청소        | 0                     |
+| 공인중개사 자격증 | 7                     |
+| 중국        | 0                     |
+| 인테리어      | 7                     |
+| 동남아       | 0                     |
+| 군대 생활     | 0                     |
+| 영상가전      | 0                     |
+| 상속세       | 0                     |
+| LG        | 7                     |
+| 부동산       | 0                     |
+| 기타 노무상담   | 0                     |
+| 웹툰·웹소설    | 0                     |
+| 뮤지컬·연극    | 0                     |
+| 네이버       | 0                     |
+| 근로계약      | 7                     |
+| 의료 보험     | 7                     |
+| 내과        | 7                     |
+| 세탁기·건조기   | 7                     |
+| 유럽·아프리카   | 7                     |
+| 기타 약료상담   | 7                     |
+| 드라마       | 7                     |
+| 반려동물 미용   | 0                     |
+| 스마트폰·태블릿  | 7                     |
+| 기타 의료상담   | 7                     |
+| 화학        | 7                     |
+| 청소기       | 7                     |
+| 테니스       | 7                     |
+| 충청        | 7                     |
+| 대학교 생활    | 7                     |
+| 야구        | 7                     |
+| 고등학교 생활   | 7                     |
+| 정형외과      | 7                     |
+| 재산 보험     | 7                     |
+| 화학공학      | 0                     |
+| 기타가전      | 7                     |
+| 문학        | 7                     |
+| 기타 법률상담   | 7                     |
+| 정신건강      | 7                     |
+| 크로스핏      | 0                     |
+| 요가·필라테스   | 7                     |
+| 스키·보드     | 7                     |
+| 보험설계사 자격증 | 7                     |
+| 해고·징계     | 0                     |
+| 휴일·휴가     | 0                     |
+| 성형외과      | 7                     |
+| 민사        | 7                     |
+| 음악        | 7                     |
+| 산업안전산업기사  | 7                     |
+| 유아교육      | 7                     |
+| 약 복용      | 7                     |
+| 주방가전      | 7                     |
+| 전기·전자     | 7                     |
+| 생활·미용가전   | 7                     |
+| 변호사 자격증   | 0                     |
+| 영화·애니     | 7                     |
+| 저축성 보험    | 7                     |
+| 증여세       | 7                     |
+| 회사 생활     | 7                     |
+| 기업·회사     | 7                     |
+| 자산관리      | 7                     |
+| 하이킹       | 7                     |
+| 연애·결혼     | 0                     |
+| 한의대학      | 7                     |
+| 지구과학·천문우주 | 7                     |
+| 롯데        | 7                     |
+| 산업재해      | 7                     |
+| 고려대학교     | 7                     |
+| 관세사 자격증   | 7                     |
+| 경제동향      | 7                     |
+| 회계자문      | 7                     |
+| 웨어러블 기기   | 7                     |
+| UNIST     | 7                     |
+| SK        | 7                     |
+| 주식·가상화폐   | 7                     |
+| 강원        | 7                     |
+| 부가가치세     | 0                     |
+| 신경외과      | 7                     |
+| 로스쿨       | 7                     |
+| 상해 보험     | 7                     |
+| 한양대학교     | 7                     |
+| 종합소득세     | 7                     |
+| 치아 관리     | 7                     |
+| 폭행·협박     | 7                     |
+| 의과대학      | 7                     |
+
+### Training Hyperparameters
+- batch_size: (128, 128)
+- num_epochs: (2, 2)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 80
+- 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
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch   | Step     | Training Loss | Validation Loss |
+|:-------:|:--------:|:-------------:|:---------------:|
+| 0.0005  | 1        | 0.1182        | -               |
+| 0.0275  | 50       | 0.1246        | -               |
+| 0.0549  | 100      | 0.0879        | -               |
+| 0.0824  | 150      | 0.0493        | -               |
+| 0.1099  | 200      | 0.0559        | -               |
+| 0.1374  | 250      | 0.0323        | -               |
+| 0.1648  | 300      | 0.025         | -               |
+| 0.1923  | 350      | 0.0296        | -               |
+| 0.2198  | 400      | 0.0198        | -               |
+| 0.2473  | 450      | 0.0163        | -               |
+| 0.2747  | 500      | 0.013         | -               |
+| 0.3022  | 550      | 0.013         | -               |
+| 0.3297  | 600      | 0.0193        | -               |
+| 0.3571  | 650      | 0.0161        | -               |
+| 0.3846  | 700      | 0.0163        | -               |
+| 0.4121  | 750      | 0.0095        | -               |
+| 0.4396  | 800      | 0.0055        | -               |
+| 0.4670  | 850      | 0.0178        | -               |
+| 0.4945  | 900      | 0.0094        | -               |
+| 0.5220  | 950      | 0.025         | -               |
+| 0.5495  | 1000     | 0.017         | -               |
+| 0.5769  | 1050     | 0.006         | -               |
+| 0.6044  | 1100     | 0.003         | -               |
+| 0.6319  | 1150     | 0.0108        | -               |
+| 0.6593  | 1200     | 0.0069        | -               |
+| 0.6868  | 1250     | 0.006         | -               |
+| 0.7143  | 1300     | 0.0025        | -               |
+| 0.7418  | 1350     | 0.0178        | -               |
+| 0.7692  | 1400     | 0.0042        | -               |
+| 0.7967  | 1450     | 0.0018        | -               |
+| 0.8242  | 1500     | 0.0046        | -               |
+| 0.8516  | 1550     | 0.0077        | -               |
+| 0.8791  | 1600     | 0.0041        | -               |
+| 0.9066  | 1650     | 0.0057        | -               |
+| 0.9341  | 1700     | 0.0115        | -               |
+| 0.9615  | 1750     | 0.0106        | -               |
+| 0.9890  | 1800     | 0.0086        | -               |
+| **1.0** | **1820** | **-**         | **0.0718**      |
+| 1.0165  | 1850     | 0.0029        | -               |
+| 1.0440  | 1900     | 0.0078        | -               |
+| 1.0714  | 1950     | 0.0018        | -               |
+| 1.0989  | 2000     | 0.0066        | -               |
+| 1.1264  | 2050     | 0.0026        | -               |
+| 1.1538  | 2100     | 0.0014        | -               |
+| 1.1813  | 2150     | 0.0022        | -               |
+| 1.2088  | 2200     | 0.0038        | -               |
+| 1.2363  | 2250     | 0.0018        | -               |
+| 1.2637  | 2300     | 0.0169        | -               |
+| 1.2912  | 2350     | 0.0084        | -               |
+| 1.3187  | 2400     | 0.0022        | -               |
+| 1.3462  | 2450     | 0.0013        | -               |
+| 1.3736  | 2500     | 0.0015        | -               |
+| 1.4011  | 2550     | 0.0036        | -               |
+| 1.4286  | 2600     | 0.0012        | -               |
+| 1.4560  | 2650     | 0.0023        | -               |
+| 1.4835  | 2700     | 0.0013        | -               |
+| 1.5110  | 2750     | 0.0082        | -               |
+| 1.5385  | 2800     | 0.0073        | -               |
+| 1.5659  | 2850     | 0.0049        | -               |
+| 1.5934  | 2900     | 0.0013        | -               |
+| 1.6209  | 2950     | 0.0012        | -               |
+| 1.6484  | 3000     | 0.002         | -               |
+| 1.6758  | 3050     | 0.0038        | -               |
+| 1.7033  | 3100     | 0.0056        | -               |
+| 1.7308  | 3150     | 0.001         | -               |
+| 1.7582  | 3200     | 0.004         | -               |
+| 1.7857  | 3250     | 0.0012        | -               |
+| 1.8132  | 3300     | 0.0063        | -               |
+| 1.8407  | 3350     | 0.0016        | -               |
+| 1.8681  | 3400     | 0.0016        | -               |
+| 1.8956  | 3450     | 0.0063        | -               |
+| 1.9231  | 3500     | 0.0013        | -               |
+| 1.9505  | 3550     | 0.0033        | -               |
+| 1.9780  | 3600     | 0.0025        | -               |
+| 2.0     | 3640     | -             | 0.0775          |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.7.0
+- Transformers: 4.40.2
+- PyTorch: 2.0.1
+- Datasets: 2.19.1
+- 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 +1,25 @@
+{
+  "_name_or_path": "checkpoints/step_1820",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

@@ -0,0 +1,9 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.1.0",
+    "transformers": "4.13.0",
+    "pytorch": "1.7.0+cu110"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

@@ -0,0 +1,212 @@
+{
+  "labels": [
+    "PC\u00b7\ub178\ud2b8\ubd81",
+    "\uc591\ub3c4\uc18c\ub4dd\uc138",
+    "\ud1a0\ubaa9\uacf5\ud559",
+    "\ucc45\u00b7\ub3c5\uc11c",
+    "\ub300\uc778\uad00\uacc4",
+    "\uae30\ud0c0 \uace0\ubbfc\uc0c1\ub2f4",
+    "\uacf5\uc778\ub178\ubb34\uc0ac \uc790\uaca9\uc99d",
+    "\uc11c\uc6b8\u00b7\uc218\ub3c4\uad8c",
+    "\uc5f0\uc138\ub300\ud559\uad50",
+    "\uc790\ub3d9\ucc28 \uc218\ub9ac",
+    "\ubc18\ub824\ub3d9\ubb3c \uac74\uac15",
+    "\uc608\ub2a5",
+    "\uac00\uc555\ub958\u00b7\uac00\ucc98\ubd84",
+    "\uae30\ud0c0 \uc721\uc544\uc0c1\ub2f4",
+    "\uc5ed\uc0ac",
+    "\uc190\ud574\uc0ac\uc815\uc0ac \uc790\uaca9\uc99d",
+    "\ud658\uc790 \uc2dd\ub2e8",
+    "\uc11c\uc6b8\uc2dc\ub9bd\ub300\ud559\uad50",
+    "\uae30\ud0c0 \uc2ec\ub9ac\uc0c1\ub2f4",
+    "\ud615\uc0ac",
+    "\uce58\uacfc\ub300\ud559",
+    "\uc608\uae08\u00b7\uc801\uae08",
+    "\ubb3c\ub9ac",
+    "\uc77c\ubcf8",
+    "\ub09a\uc2dc",
+    "\ud604\ub300\uc911\uacf5\uc5c5",
+    "\uad50\ud1b5\uc0ac\uace0",
+    "\uacf5\uc778\ud68c\uacc4\uc0ac \uc790\uaca9\uc99d",
+    "\ub18d\uad6c",
+    "\uc885\ud569\ubd80\ub3d9\uc0b0\uc138",
+    "\ube44\ub1e8\uc758\ud559\uacfc",
+    "\uba85\uc608\ud6fc\uc190\u00b7\ubaa8\uc695",
+    "\uc9c0\uac8c\ucc28\uc6b4\uc804\uae30\ub2a5\uc0ac",
+    "\uac00\uc871\u00b7\uc774\ud63c",
+    "\ud604\ub300\uc790\ub3d9\ucc28",
+    "\uae30\uacc4\uacf5\ud559",
+    "\uc601\uc591\uc81c",
+    "\uc5f0\ub9d0\uc815\uc0b0",
+    "\uc131\uade0\uad00\ub300\ud559\uad50",
+    "\ubbf8\uc220",
+    "\uc591\uc721\u00b7\ud6c8\uc721",
+    "\uacbd\uc81c\uc6a9\uc5b4",
+    "\uacbd\ud76c\ub300\ud559\uad50",
+    "\uae08\uc735",
+    "\uc2a4\ucf00\uc774\ud2b8",
+    "\ubc18\ub824\ub3d9\ubb3c \ud6c8\ub828",
+    "\ud55c\uc57d",
+    "\uc0dd\ubb3c\u00b7\uc0dd\uba85",
+    "\uc0ac\ud68c\ubcf5\uc9c0\uc0ac \uc790\uaca9\uc99d",
+    "\ud55c\uc758\ud559",
+    "\ubd80\ub3d9\uc0b0\u00b7\uc784\ub300\ucc28",
+    "\ud68c\uc0dd\u00b7\ud30c\uc0b0",
+    "\uc784\uae08\uccb4\ubd88",
+    "\ubcc0\ub9ac\uc0ac \uc790\uaca9\uc99d",
+    "\ud53c\ubd80\uacfc",
+    "\ub180\uc774",
+    "\ub300\ucd9c",
+    "\uc7ac\uc0b0\ubc94\uc8c4",
+    "\uc601\uc720\uc544\u00b7\uc544\ub3d9 \uc2dd\ub2e8",
+    "\uacc4\uc808\uac00\uc804",
+    "\uc7ac\ub8cc\uacf5\ud559",
+    "\uc804\ub77c",
+    "\ub2e4\uc774\uc5b4\ud2b8 \uc2dd\ub2e8",
+    "\uacbd\uc81c\uc815\ucc45",
+    "\ucea0\ud551",
+    "\ucca0\ud559",
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+    "\uc11c\uac15\ub300\ud559\uad50",
+    "\uc790\uc544\u00b7\uc790\uae30\uc774\ud574",
+    "\ud658\uacbd\u00b7\uc5d0\ub108\uc9c0",
+    "\ud3ec\ud56d\uacf5\uacfc\ub300\ud559\uad50",
+    "\uc81c\uc8fc",
+    "\ud589\uc815\uc0ac \uc790\uaca9\uc99d",
+    "\uc784\uae08\u00b7\uae09\uc5ec",
+    "\uc138\ud0c1\uc218\uc120",
+    "\uc911\ud559\uad50 \uc0dd\ud65c",
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+    "\uc218\uc601",
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+    "\uc0ac\uc9c4\u00b7\uc601\uc0c1",
+    "\uc138\ubb34\uc0ac \uc790\uaca9\uc99d",
+    "\ucd95\uad6c\u00b7\ud48b\uc0b4",
+    "\ubbf8\uc8fc\u00b7\uc911\ub0a8\ubbf8",
+    "\uc911\uc559\ub300\ud559\uad50",
+    "\uc74c\ud5a5\uae30\uae30",
+    "\ud55c\ud654",
+    "\uc138\ubb34\uc870\uc0ac\u00b7\ubd88\ubcf5",
+    "\uad50\ud1b5\uc0ac\uace0 \uacfc\uc2e4",
+    "\uc11c\uc6b8\ub300\ud559\uad50",
+    "\uc758\ub8cc",
+    "\uc2dd\uc2b5\uad00\u00b7\uc2dd\uc774\uc694\ubc95",
+    "\uad6c\uc870\uc870\uc815",
+    "\ubc95\uc778\uc138",
+    "\uae30\ud0c0 \uc601\uc591\uc0c1\ub2f4",
+    "\uc790\ub3d9\ucc28",
+    "KAIST",
+    "\uc774\ube44\uc778\ud6c4\uacfc",
+    "\uc0bc\uc131",
+    "\uace8\ud504",
+    "\uacbd\uc0c1",
+    "\ub4f1\uc0b0\u00b7\ud074\ub77c\uc774\ubc0d",
+    "\uae30\ud0c0 \uc2a4\ud3ec\uce20",
+    "\uc131\ubc94\uc8c4",
+    "GIST",
+    "\uac00\ub4dc\ub2dd",
+    "\ud55c\uad6d\uc678\uad6d\uc5b4\ub300\ud559\uad50",
+    "PC \uc8fc\ubcc0\uae30\uae30",
+    "\uae30\ud0c0 \uc7a5\uce58",
+    "\uc5f0\uc560\u00b7\uc774\uc131",
+    "\ucde8\ub4dd\uc138\u00b7\ub4f1\ub85d\uc138",
+    "\uc0b0\ubd80\uc778\uacfc",
+    "\uae30\ud0c0 \uc138\uae08\uc0c1\ub2f4",
+    "\ud5ec\uc2a4",
+    "\uac04\ud638\uc870\ubb34\uc0ac \uc790\uaca9\uc99d",
+    "\uc804\uae30\uae30\uc0ac\u00b7\uae30\ub2a5\uc0ac",
+    "\uc624\uc138\uc544\ub2c8\uc544",
+    "\ubb34\uc5ed",
+    "\uccad\uc18c",
+    "\uacf5\uc778\uc911\uac1c\uc0ac \uc790\uaca9\uc99d",
+    "\uc911\uad6d",
+    "\uc778\ud14c\ub9ac\uc5b4",
+    "\ub3d9\ub0a8\uc544",
+    "\uad70\ub300 \uc0dd\ud65c",
+    "\uc601\uc0c1\uac00\uc804",
+    "\uc0c1\uc18d\uc138",
+    "LG",
+    "\ubd80\ub3d9\uc0b0",
+    "\uae30\ud0c0 \ub178\ubb34\uc0c1\ub2f4",
+    "\uc6f9\ud230\u00b7\uc6f9\uc18c\uc124",
+    "\ubba4\uc9c0\uceec\u00b7\uc5f0\uadf9",
+    "\ub124\uc774\ubc84",
+    "\uadfc\ub85c\uacc4\uc57d",
+    "\uc758\ub8cc \ubcf4\ud5d8",
+    "\ub0b4\uacfc",
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+    "\uc720\ub7fd\u00b7\uc544\ud504\ub9ac\uce74",
+    "\uae30\ud0c0 \uc57d\ub8cc\uc0c1\ub2f4",
+    "\ub4dc\ub77c\ub9c8",
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+    "\uc2a4\ub9c8\ud2b8\ud3f0\u00b7\ud0dc\ube14\ub9bf",
+    "\uae30\ud0c0 \uc758\ub8cc\uc0c1\ub2f4",
+    "\ud654\ud559",
+    "\uccad\uc18c\uae30",
+    "\ud14c\ub2c8\uc2a4",
+    "\ucda9\uccad",
+    "\ub300\ud559\uad50 \uc0dd\ud65c",
+    "\uc57c\uad6c",
+    "\uace0\ub4f1\ud559\uad50 \uc0dd\ud65c",
+    "\uc815\ud615\uc678\uacfc",
+    "\uc7ac\uc0b0 \ubcf4\ud5d8",
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+    "\ubb38\ud559",
+    "\uae30\ud0c0 \ubc95\ub960\uc0c1\ub2f4",
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+    "\ud06c\ub85c\uc2a4\ud54f",
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+    "\uc2a4\ud0a4\u00b7\ubcf4\ub4dc",
+    "\ubcf4\ud5d8\uc124\uacc4\uc0ac \uc790\uaca9\uc99d",
+    "\ud574\uace0\u00b7\uc9d5\uacc4",
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+    "\uc6e8\uc5b4\ub7ec\ube14 \uae30\uae30",
+    "UNIST",
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+    "\uac15\uc6d0",
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+  "normalize_embeddings": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:33d6cf3b048c0313d349211fe91639608d8926a186510dcce63456cc019f20bb
+size 442491744

model_head.pkl

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+oid sha256:e0dcb6b8e0f06aadc9d4054c23b5f3207baed066f02e1d0612198ed703d96768
+size 1282046

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": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
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+    "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 +1,64 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "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]"
+}