Push model using huggingface_hub.

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+unigram.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "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

@@ -1,3 +1,379 @@
----
-license: cc-by-4.0
----
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 통화 중 수신함 신청
+- text: 핸드폰 변경 날짜 알려줘
+- text: 인증 번호가 안 와요
+- text: 분실
+- text: 스팸 차단 번호들 알고 싶어
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+---
+
+# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) 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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
+- **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:** 36 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                                                                                                         |
+|:---------------------------|:-----------------------------------------------------------------------------------------------------------------|
+| [단말기]모바일 U+Shop            | <ul><li>'새로운 폰 개통'</li><li>'그러다 폰 개통'</li><li>'아이폰 십육 프로 문의입니다'</li></ul>                                        |
+| [콜봇공통]장애처리                 | <ul><li>'통화품질'</li><li>'핸드폰을 바꿨는데 데이터가 안 터져요'</li><li>'데이터 속도 느림'</li></ul>                                      |
+| [콜봇상담]IPTV장애               | <ul><li>'티비 고장'</li><li>'티비가 안 나와요'</li><li>'저 티비가 안 나와요'</li></ul>                                              |
+| [콜봇상담]결합상품문의               | <ul><li>'핸드폰 결합'</li><li>'가족 묶음이요'</li><li>'결합 해제'</li></ul>                                                     |
+| [콜봇상담]기기변경문의               | <ul><li>'네 아이 핸드폰에 변경'</li><li>'아 휴대폰 요금 요금 변경 이런 거 좀 할라고요'</li><li>'기기변경 취소'</li></ul>                          |
+| [콜봇상담]납부방법변경               | <ul><li>'자동이체 변경'</li><li>'자동이체 신청'</li><li>'응 계좌 변경'</li></ul>                                                  |
+| [콜봇상담]납부확인서 발급             | <ul><li>'핸드폰 납부 요금 일월부터 시월까지 팩스로 보내주세요 작년도 거'</li><li>'요금 납부 내역'</li><li>'이십 삼년도 해놨네'</li></ul>                  |
+| [콜봇상담]듀얼넘버 문의              | <ul><li>'듀얼 넘버'</li><li>'듀얼 넘버'</li><li>'투넘버 서비스'</li></ul>                                                      |
+| [콜봇상담]모바일 부가서비스 가입 및 해지    | <ul><li>'부가서비스 해제'</li><li>'부가서비스'</li><li>'패스 해제'</li></ul>                                                     |
+| [콜봇상담]선택약정할인상태 안내 및 등록기능   | <ul><li>'선택 약정'</li><li>'선택약정 신청'</li><li>'선택약정할인'</li></ul>                                                     |
+| [콜봇상담]세금계산서발행              | <ul><li>'소득세 신고요'</li><li>'종합 종합소득세 신고'</li><li>'세금 납부 서류'</li></ul>                                             |
+| [콜봇상담]스팸차단 서비스 신청 및 해지     | <ul><li>'스팸 차단 메시지를 확인할 수 있어요'</li><li>'어 수신 수신 수신 차단 부 요 풀���주세요'</li><li>'스팸 문자'</li></ul>                       |
+| [콜봇상담]약정문의(공통)             | <ul><li>'약정 연장'</li><li>'약정기간'</li><li>'그의 뭐 하 그 전에 그 기 약정기간이 지난 걸 몰르고 요금이 많이 나오는데 그거는 어 저 뭐 할인 안 되나요'</li></ul> |
+| [콜봇상담]연체문의(공통)             | <ul><li>'핸드폰 미납'</li><li>'어 요금 미납'</li><li>'미납요금'</li></ul>                                                      |
+| [콜봇상담]요금납부                 | <ul><li>'분할 납부'</li><li>'예 요금 낼려구요'</li><li>'미납요금 분할 납부'</li></ul>                                               |
+| [콜봇상담]요금문의(공통)             | <ul><li>'요금'</li><li>'요 핸드폰 요금 관련해서 물어보는 겁니다'</li><li>'공구 요금 알려줘'</li></ul>                                      |
+| [콜봇상담]요금제변경                | <ul><li>'요금제 변경'</li><li>'요금 변경'</li><li>'요금제 변경'</li></ul>                                                      |
+| [콜봇상담]유심 구매 및 이동 문의        | <ul><li>'유심칩 교체'</li><li>'유심'</li><li>'이심 옮기지'</li></ul>                                                         |
+| [콜봇상담]이전설치                 | <ul><li>'이사하려고 하는데 서비스 받고 싶어서요'</li><li>'포인트 사용 이전요'</li><li>'주소변경'</li></ul>                                    |
+| [콜봇상담]인터넷 해지               | <ul><li>'인터넷 해제'</li><li>'인터넷 해제'</li><li>'와이파이 해제'</li></ul>                                                    |
+| [콜봇상담]인터넷장애                | <ul><li>'와이파이가 인터넷이 안돼 연결은 되는데'</li><li>'인터넷이 잘 안돼요'</li><li>'인터넷 고장'</li></ul>                                  |
+| [콜봇상담]일반상담(공통)             | <ul><li>'변경'</li><li>'티비'</li><li>'예 서비스 이용이요'</li></ul>                                                         |
+| [콜봇상담]일시정지 및 일시정지 해제(공통)   | <ul><li>'일시정지'</li><li>'지금 쓰고 있는 전화 번호 잠시 정지하고 싶어'</li><li>'전화기를 잠시 정지시키려고 합니다'</li></ul>                        |
+| [콜봇상담]일시정지 및 일시정지 해제(모)    | <ul><li>'핸드폰 정지'</li><li>'휴대폰 일시정지'</li><li>'휴대폰 정지'</li></ul>                                                   |
+| [콜봇상담]청구요금조회               | <ul><li>'청구요금'</li><li>'네 사월 달에 사월 이십 이일 날 날 청구요금과 국민은행으로 자동이체 된 거하고 확인할라고요'</li><li>'청구요금'</li></ul>            |
+| [콜봇상담]통화연결음 가입 및 해지        | <ul><li>'컬러링 없애죠'</li><li>'브이 컬러링 해제'</li><li>'말해 달라 컬러링'</li></ul>                                              |
+| [콜봇상담]통화중대기 가입 및 해지        | <ul><li>'통화 중 대기 부가서비스'</li><li>'예 통화 대기 중'</li><li>'통화 중 대기'</li></ul>                                          |
+| [콜봇상담]해지(공통)               | <ul><li>'계약 취소'</li><li>'워치 해제'</li><li>'어 뭐지'</li></ul>                                                         |
+| [콜봇상담]홈서비스 가입              | <ul><li>'티비가입'</li><li>'티비 설치 문의'</li><li>'인터넷 설치'</li></ul>                                                     |
+| [콜봇상담]환불_이중납부              | <ul><li>'이중납부'</li><li>'요금 납부가 이중으로'</li><li>'이중 교체'</li></ul>                                                   |
+| [콜봇상담]휴대폰 분실문의             | <ul><li>'핸드폰 분실'</li><li>'분실 신고'</li><li>'분실신고'</li></ul>                                                        |
+| [콜봇상담]휴대폰결제 한도변경           | <ul><li>'소액결제 한도'</li><li>'소액 한도 확인'</li><li>'휴대폰 결제 한도 상향'</li></ul>                                            |
+| [콜봇상담]휴대폰결제(공통)            | <ul><li>'소액결제'</li><li>'핸드폰 소액결제'</li><li>'핸드폰 결제 핸드폰 결제'</li></ul>                                              |
+| [콜봇상담]휴대폰보험문의및보상신청         | <ul><li>'파손 보험'</li><li>'의료보험공단이요'</li><li>'휴대폰 보험 확인'</li></ul>                                                 |
+| [콜봇이벤트]로밍상담 재질의            | <ul><li>'해외 로밍 부탁드려요'</li><li>'해외 로밍'</li><li>'로밍'</li></ul>                                                     |
+| [프리미어요금제약정할인]프리미어 요금제 약정할인 | <ul><li>'프리미엄 요금 약정 신청'</li><li>'프리미엄 요금제 약정 할�� 재가입'</li><li>'프리미엄 요금제 할인 재신청 해줘'</li></ul>                      |
+
+## 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("huiyeong/setfit-callbot-keyword-neg")
+# Run inference
+preds = model("분실")
+```
+
+<!--
+### 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   | 1   | 3.1023 | 27  |
+
+| Label                      | Training Sample Count |
+|:---------------------------|:----------------------|
+| [단말기]모바일 U+Shop            | 59                    |
+| [콜봇공통]장애처리                 | 58                    |
+| [콜봇상담]IPTV장애               | 66                    |
+| [콜봇상담]결합상품문의               | 60                    |
+| [콜봇상담]기기변경문의               | 61                    |
+| [콜봇상담]납부방법변경               | 58                    |
+| [콜봇상담]납부확인서 발급             | 62                    |
+| [콜봇상담]듀얼넘버 문의              | 57                    |
+| [콜봇상담]모바일 부가서비스 가입 및 해지    | 62                    |
+| [콜봇상담]선택약정할인상태 안내 및 등록기능   | 63                    |
+| [콜봇상담]세금계산서발행              | 63                    |
+| [콜봇상담]스팸차단 서비스 신청 및 해지     | 56                    |
+| [콜봇상담]약정문의(공통)             | 59                    |
+| [콜봇상담]연체문의(공통)             | 57                    |
+| [콜봇상담]요금납부                 | 58                    |
+| [콜봇상담]요금문의(공통)             | 60                    |
+| [콜봇상담]요금제변경                | 57                    |
+| [콜봇상담]유심 구매 및 이동 문의        | 59                    |
+| [콜봇상담]이전설치                 | 61                    |
+| [콜봇상담]인터넷 해지               | 58                    |
+| [콜봇상담]인터넷장애                | 57                    |
+| [콜봇상담]일반상담(공통)             | 59                    |
+| [콜봇상담]일시정지 및 일시정지 해제(공통)   | 63                    |
+| [콜봇상담]일시정지 및 일시정지 해제(모)    | 57                    |
+| [콜봇상담]청구요금조회               | 61                    |
+| [콜봇상담]통화연결음 가입 및 해지        | 55                    |
+| [콜봇상담]통화중대기 가입 및 해지        | 64                    |
+| [콜봇상담]해지(공통)               | 61                    |
+| [콜봇상담]홈서비스 가입              | 59                    |
+| [콜봇상담]환불_이중납부              | 61                    |
+| [콜봇상담]휴대폰 분실문의             | 64                    |
+| [콜봇상담]휴대폰결제 한도변경           | 63                    |
+| [콜봇상담]휴대폰결제(공통)            | 56                    |
+| [콜봇상담]휴대폰보험문의및보상신청         | 60                    |
+| [콜봇이벤트]로밍상담 재질의            | 60                    |
+| [프리미어요금제약정할인]프리미어 요금제 약정할인 | 66                    |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (5, 5)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 5
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0007 | 1    | 0.2327        | -               |
+| 0.0370 | 50   | 0.2495        | -               |
+| 0.0741 | 100  | 0.2222        | -               |
+| 0.1111 | 150  | 0.2231        | -               |
+| 0.1481 | 200  | 0.1941        | -               |
+| 0.1852 | 250  | 0.1796        | -               |
+| 0.2222 | 300  | 0.1705        | -               |
+| 0.2593 | 350  | 0.1623        | -               |
+| 0.2963 | 400  | 0.1512        | -               |
+| 0.3333 | 450  | 0.1317        | -               |
+| 0.3704 | 500  | 0.1156        | -               |
+| 0.4074 | 550  | 0.1071        | -               |
+| 0.4444 | 600  | 0.1148        | -               |
+| 0.4815 | 650  | 0.1024        | -               |
+| 0.5185 | 700  | 0.1044        | -               |
+| 0.5556 | 750  | 0.1047        | -               |
+| 0.5926 | 800  | 0.0879        | -               |
+| 0.6296 | 850  | 0.0781        | -               |
+| 0.6667 | 900  | 0.078         | -               |
+| 0.7037 | 950  | 0.0824        | -               |
+| 0.7407 | 1000 | 0.0635        | -               |
+| 0.7778 | 1050 | 0.073         | -               |
+| 0.8148 | 1100 | 0.078         | -               |
+| 0.8519 | 1150 | 0.0676        | -               |
+| 0.8889 | 1200 | 0.0677        | -               |
+| 0.9259 | 1250 | 0.0714        | -               |
+| 0.9630 | 1300 | 0.0548        | -               |
+| 1.0    | 1350 | 0.0531        | -               |
+| 1.0370 | 1400 | 0.0499        | -               |
+| 1.0741 | 1450 | 0.0467        | -               |
+| 1.1111 | 1500 | 0.0532        | -               |
+| 1.1481 | 1550 | 0.0477        | -               |
+| 1.1852 | 1600 | 0.0456        | -               |
+| 1.2222 | 1650 | 0.0399        | -               |
+| 1.2593 | 1700 | 0.0438        | -               |
+| 1.2963 | 1750 | 0.0472        | -               |
+| 1.3333 | 1800 | 0.0432        | -               |
+| 1.3704 | 1850 | 0.0419        | -               |
+| 1.4074 | 1900 | 0.0475        | -               |
+| 1.4444 | 1950 | 0.0401        | -               |
+| 1.4815 | 2000 | 0.0402        | -               |
+| 1.5185 | 2050 | 0.0341        | -               |
+| 1.5556 | 2100 | 0.035         | -               |
+| 1.5926 | 2150 | 0.033         | -               |
+| 1.6296 | 2200 | 0.0368        | -               |
+| 1.6667 | 2250 | 0.0266        | -               |
+| 1.7037 | 2300 | 0.0325        | -               |
+| 1.7407 | 2350 | 0.0307        | -               |
+| 1.7778 | 2400 | 0.0321        | -               |
+| 1.8148 | 2450 | 0.0309        | -               |
+| 1.8519 | 2500 | 0.0273        | -               |
+| 1.8889 | 2550 | 0.0277        | -               |
+| 1.9259 | 2600 | 0.0312        | -               |
+| 1.9630 | 2650 | 0.0282        | -               |
+| 2.0    | 2700 | 0.0294        | -               |
+| 2.0370 | 2750 | 0.027         | -               |
+| 2.0741 | 2800 | 0.0268        | -               |
+| 2.1111 | 2850 | 0.0243        | -               |
+| 2.1481 | 2900 | 0.0227        | -               |
+| 2.1852 | 2950 | 0.0226        | -               |
+| 2.2222 | 3000 | 0.0257        | -               |
+| 2.2593 | 3050 | 0.0261        | -               |
+| 2.2963 | 3100 | 0.0192        | -               |
+| 2.3333 | 3150 | 0.0232        | -               |
+| 2.3704 | 3200 | 0.0263        | -               |
+| 2.4074 | 3250 | 0.0252        | -               |
+| 2.4444 | 3300 | 0.024         | -               |
+| 2.4815 | 3350 | 0.0236        | -               |
+| 2.5185 | 3400 | 0.0193        | -               |
+| 2.5556 | 3450 | 0.0226        | -               |
+| 2.5926 | 3500 | 0.024         | -               |
+| 2.6296 | 3550 | 0.0198        | -               |
+| 2.6667 | 3600 | 0.0278        | -               |
+| 2.7037 | 3650 | 0.0223        | -               |
+| 2.7407 | 3700 | 0.0256        | -               |
+| 2.7778 | 3750 | 0.0223        | -               |
+| 2.8148 | 3800 | 0.0217        | -               |
+| 2.8519 | 3850 | 0.0165        | -               |
+| 2.8889 | 3900 | 0.0223        | -               |
+| 2.9259 | 3950 | 0.0241        | -               |
+| 2.9630 | 4000 | 0.0182        | -               |
+| 3.0    | 4050 | 0.0213        | -               |
+| 3.0370 | 4100 | 0.0196        | -               |
+| 3.0741 | 4150 | 0.0218        | -               |
+| 3.1111 | 4200 | 0.0142        | -               |
+| 3.1481 | 4250 | 0.0185        | -               |
+| 3.1852 | 4300 | 0.0203        | -               |
+| 3.2222 | 4350 | 0.0173        | -               |
+| 3.2593 | 4400 | 0.018         | -               |
+| 3.2963 | 4450 | 0.0161        | -               |
+| 3.3333 | 4500 | 0.0143        | -               |
+| 3.3704 | 4550 | 0.0151        | -               |
+| 3.4074 | 4600 | 0.0115        | -               |
+| 3.4444 | 4650 | 0.0152        | -               |
+| 3.4815 | 4700 | 0.0166        | -               |
+| 3.5185 | 4750 | 0.0215        | -               |
+| 3.5556 | 4800 | 0.0138        | -               |
+| 3.5926 | 4850 | 0.017         | -               |
+| 3.6296 | 4900 | 0.0192        | -               |
+| 3.6667 | 4950 | 0.0206        | -               |
+| 3.7037 | 5000 | 0.0172        | -               |
+| 3.7407 | 5050 | 0.0169        | -               |
+| 3.7778 | 5100 | 0.0226        | -               |
+| 3.8148 | 5150 | 0.017         | -               |
+| 3.8519 | 5200 | 0.0176        | -               |
+| 3.8889 | 5250 | 0.0183        | -               |
+| 3.9259 | 5300 | 0.0155        | -               |
+| 3.9630 | 5350 | 0.0156        | -               |
+| 4.0    | 5400 | 0.0174        | -               |
+| 4.0370 | 5450 | 0.0121        | -               |
+| 4.0741 | 5500 | 0.0132        | -               |
+| 4.1111 | 5550 | 0.0159        | -               |
+| 4.1481 | 5600 | 0.0144        | -               |
+| 4.1852 | 5650 | 0.0179        | -               |
+| 4.2222 | 5700 | 0.0175        | -               |
+| 4.2593 | 5750 | 0.0118        | -               |
+| 4.2963 | 5800 | 0.0136        | -               |
+| 4.3333 | 5850 | 0.0151        | -               |
+| 4.3704 | 5900 | 0.0171        | -               |
+| 4.4074 | 5950 | 0.0185        | -               |
+| 4.4444 | 6000 | 0.017         | -               |
+| 4.4815 | 6050 | 0.0166        | -               |
+| 4.5185 | 6100 | 0.0195        | -               |
+| 4.5556 | 6150 | 0.0127        | -               |
+| 4.5926 | 6200 | 0.0096        | -               |
+| 4.6296 | 6250 | 0.0127        | -               |
+| 4.6667 | 6300 | 0.0202        | -               |
+| 4.7037 | 6350 | 0.0148        | -               |
+| 4.7407 | 6400 | 0.0163        | -               |
+| 4.7778 | 6450 | 0.0155        | -               |
+| 4.8148 | 6500 | 0.0126        | -               |
+| 4.8519 | 6550 | 0.0155        | -               |
+| 4.8889 | 6600 | 0.0162        | -               |
+| 4.9259 | 6650 | 0.0157        | -               |
+| 4.9630 | 6700 | 0.0165        | -               |
+| 5.0    | 6750 | 0.0122        | -               |
+
+### Framework Versions
+- Python: 3.11.13
+- SetFit: 1.1.2
+- Sentence Transformers: 4.1.0
+- Transformers: 4.52.4
+- PyTorch: 2.6.0+cu124
+- Datasets: 3.6.0
+- Tokenizers: 0.21.2
+
+## 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

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+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "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": "4.52.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.52.4",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+{
+  "normalize_embeddings": false,
+  "labels": [
+    "[\ub2e8\ub9d0\uae30]\ubaa8\ubc14\uc77c U+Shop",
+    "[\ucf5c\ubd07\uacf5\ud1b5]\uc7a5\uc560\ucc98\ub9ac",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]IPTV\uc7a5\uc560",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uacb0\ud569\uc0c1\ud488\ubb38\uc758",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uae30\uae30\ubcc0\uacbd\ubb38\uc758",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\ub0a9\ubd80\ubc29\ubc95\ubcc0\uacbd",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\ub0a9\ubd80\ud655\uc778\uc11c \ubc1c\uae09",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\ub4c0\uc5bc\ub118\ubc84 \ubb38\uc758",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\ubaa8\ubc14\uc77c \ubd80\uac00\uc11c\ube44\uc2a4 \uac00\uc785 \ubc0f \ud574\uc9c0",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uc120\ud0dd\uc57d\uc815\ud560\uc778\uc0c1\ud0dc \uc548\ub0b4 \ubc0f \ub4f1\ub85d\uae30\ub2a5",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uc138\uae08\uacc4\uc0b0\uc11c\ubc1c\ud589",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uc2a4\ud338\ucc28\ub2e8 \uc11c\ube44\uc2a4 \uc2e0\uccad \ubc0f \ud574\uc9c0",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uc57d\uc815\ubb38\uc758(\uacf5\ud1b5)",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uc5f0\uccb4\ubb38\uc758(\uacf5\ud1b5)",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uc694\uae08\ub0a9\ubd80",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uc694\uae08\ubb38\uc758(\uacf5\ud1b5)",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uc694\uae08\uc81c\ubcc0\uacbd",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uc720\uc2ec \uad6c\ub9e4 \ubc0f \uc774\ub3d9 \ubb38\uc758",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uc774\uc804\uc124\uce58",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uc778\ud130\ub137 \ud574\uc9c0",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uc778\ud130\ub137\uc7a5\uc560",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uc77c\ubc18\uc0c1\ub2f4(\uacf5\ud1b5)",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uc77c\uc2dc\uc815\uc9c0 \ubc0f \uc77c\uc2dc\uc815\uc9c0 \ud574\uc81c(\uacf5\ud1b5)",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uc77c\uc2dc\uc815\uc9c0 \ubc0f \uc77c\uc2dc\uc815\uc9c0 \ud574\uc81c(\ubaa8)",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\uccad\uad6c\uc694\uae08\uc870\ud68c",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\ud1b5\ud654\uc5f0\uacb0\uc74c \uac00\uc785 \ubc0f \ud574\uc9c0",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\ud1b5\ud654\uc911\ub300\uae30 \uac00\uc785 \ubc0f \ud574\uc9c0",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\ud574\uc9c0(\uacf5\ud1b5)",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\ud648\uc11c\ube44\uc2a4 \uac00\uc785",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\ud658\ubd88_\uc774\uc911\ub0a9\ubd80",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\ud734\ub300\ud3f0 \ubd84\uc2e4\ubb38\uc758",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\ud734\ub300\ud3f0\uacb0\uc81c \ud55c\ub3c4\ubcc0\uacbd",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\ud734\ub300\ud3f0\uacb0\uc81c(\uacf5\ud1b5)",
+    "[\ucf5c\ubd07\uc0c1\ub2f4]\ud734\ub300\ud3f0\ubcf4\ud5d8\ubb38\uc758\ubc0f\ubcf4\uc0c1\uc2e0\uccad",
+    "[\ucf5c\ubd07\uc774\ubca4\ud2b8]\ub85c\ubc0d\uc0c1\ub2f4 \uc7ac\uc9c8\uc758",
+    "[\ud504\ub9ac\ubbf8\uc5b4\uc694\uae08\uc81c\uc57d\uc815\ud560\uc778]\ud504\ub9ac\ubbf8\uc5b4 \uc694\uae08\uc81c \uc57d\uc815\ud560\uc778"
+  ]
+}

model.safetensors

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+oid sha256:72897486056056f5007798b5c8e6ab8500024087b39c862ac426f21a6ced22ac
+size 470637416

model_head.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:41767c36dae94e7b190999c5085185edd4071cf08160e93b6567a1ed0d1988ea
+size 115463

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

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
+size 17082987

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "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": "</s>",
+      "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
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 128,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

unigram.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:da145b5e7700ae40f16691ec32a0b1fdc1ee3298db22a31ea55f57a966c4a65d
+size 14763260