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 +1,263 @@
+---
+base_model: mini1013/master_domain
+library_name: setfit
+metrics:
+- metric
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: ipTIME AX3000M WiFi 6 기가비트 와이파이 공유기 메시 무선 유무선 인터넷  애플준웍스
+- text: 솔텍 SFC200-SCSW/A 광 컨버터 싱글모드 WDM 1코어  파워네트정보통신(주)
+- text: 7102KVM-4K  (주)이지넷유비쿼터스
+- text: 아이피타임 데스크탑 무선 랜카드 PCI-E Wi-Fi 6 기가 인터넷 와이파이 수신기 11AX 3000PX  주식회사 디앤에스티
+- text: '[공식 인증 판매점] IPTIME EFM네트웍스 아이피타임 Extender-A3MU WiFi 와이파이 듀얼밴드 무선AP 증폭기 확장기  (주)거북선비젼'
+inference: true
+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: metric
+      value: 0.9336257647466236
+      name: Metric
+---
+
+# 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:** 16 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                                                                                                                                                                                                                                                                               |
+|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 15    | <ul><li>'이지넷 NEXT-AV2303 HDMI to AV 오디오 추출기 [KF]  주식회사 케이에프컴퍼니'</li><li>'이지넷유비쿼터스 넥스트 NEXT-2000GSCS  디메이드 (DMADE)'</li><li>'ATEN UC3002A C타입 to VGA 변환 컨버터  레알몰'</li></ul>                                                                                                             |
+| 5     | <ul><li>'티피링크 TL-SM331T RJ45 SFP+ 지원 변환 TX모듈 광모듈  케이브몰'</li><li>'대흥정보기술 SPARROW SFP-1G-RJ45 광모듈  컴튜브 주식회사'</li><li>'티피링크 SM321B-2  주식회사 동행하기'</li></ul>                                                                                                                                |
+| 4     | <ul><li>'MBF) RJ-45 커넥터 CAT5E UTP 투명 100개 MBF-RJ45  교이노베이션'</li><li>'w 넥스트 NEXT-RJ45 CAT.5e 모듈러 커넥터 (100개)  (주)원영씨앤씨'</li><li>'Coms 커플러(RJ45) I형 8P8C BT228  (주)라니아씨앤씨'</li></ul>                                                                                                      |
+| 2     | <ul><li>'KM-021N USB3.0 KM 데이터 통신 컨버터(키보드/마우스 공유)  서준전자'</li><li>'USB3.0 KM LINK 케이블 JUC500  우노'</li><li>'NEXT-JUC500 USB 3.0 KM 스위치 Windows-Android 키보드&마우스 공유, 파일 공유  주식회사 토다스(todas Co., Ltd)'</li></ul>                                                                            |
+| 12    | <ul><li>'EFM ipTIME BT53XR 품위  주식회사 품위'</li><li>'크리에이티브 BT-W3  초록샵'</li><li>'[NEXI] NX1420 블루투스 V5.4 동글(NX-BT54)  (주)클루웨어'</li></ul>                                                                                                                                                   |
+| 3     | <ul><li>'IPTIME A3008-MU 기가 유무선 공유기 인터넷 와이파이 식당 매장 가정용 사무실 원룸  김윤자'</li><li>'(EFM) IPTIME RING-MINI2 AC1300 MU-MIMO WI-FI Mesh ㅅ  드림체이서'</li><li>'넥스트유 듀얼 밴드 무선 WiFi 확장기 NEXT-1204AC-AP  (주)이지넷유비쿼터스'</li></ul>                                                                        |
+| 8     | <ul><li>'이지넷유비쿼터스 넥스트 NEXT-3100K EX  와이지컴퍼니'</li><li>'티피링크 Archer T4U Plus  주식회사 영은정보'</li><li>'EFM ipTIME A3000U 무선랜카드  (주)위젤'</li></ul>                                                                                                                                              |
+| 10    | <ul><li>'[랜장비] NST NSB-200 ADSL 랜모뎀 전화선으로 1Km (13/26Mbps)  (주)랜장비'</li><li>'(IPD) NST 엔에스티정보통신 NSB-200 ADSL 랜모뎀 전화선으로 1Km (NSB-1000, NSB-260 후속모델)  (주)아이피드림'</li><li>'엔에스티정보통신 NSB-200 모뎀  (주)원영씨앤씨'</li></ul>                                                                        |
+| 7     | <ul><li>'MikroTik 마이크로틱 L009UiGS-RM 방화벽 Router  컴튜브 주식회사'</li><li>'w 티피링크 TP-LINK ER7412-M2 라우터  (주)원영씨앤씨'</li><li>'MikroTik 마이크로틱 L009UiGS-RM 라우터  태성에프앤비(주)'</li></ul>                                                                                                               |
+| 14    | <ul><li>'EFM ipTIME 아이피타임 N007 안테나  (주)엠티에프시스템'</li><li>'ipTIME N007 외장형 N타입 안테나 듀얼밴드 5G 2.4G 7dbi 케이블길이 약1.5M 무상보증 1년 / 주말영업 / 방문수령 가능 / 재고보유  (주)티오피컴'</li><li>'이에프엠 ipTIME N007 무선안테나 연장안테나  (주)엘제이컴퍼니'</li></ul>                                                                   |
+| 1     | <ul><li>'NEXTU NEXT-1020KVM-IP  에스앤와이'</li><li>'ATEN KL1516AM 17인치 16포트 Cat 5 듀얼레일 LCD KVM 스위치  (주)아이웍스'</li><li>'에이텐 KE8950T KVM over IP 매트릭스 시스템 (수신기) 1년 보증연장 (주)퍼니케이블'</li></ul>                                                                                                   |
+| 13    | <ul><li>'아이피타임 ipTIME H705 스위치허브  에스온코리아 주식회사'</li><li>'아이피타임 H705 스위칭허브  컴온씨앤씨(주)'</li><li>'아이피타임 H6008 8포트 기가비트 스위치허브  (주)엠티에프시스템'</li></ul>                                                                                                                                         |
+| 6     | <ul><li>'NEXT-868LTT /랜테스터기 UTP/동축라인길이/케이블탐지  (주)엘제이컴퍼니'</li><li>'인네트워크 탐지용 멀티 테스터기IN-468R  디메이드 (DMADE)'</li><li>'MBF T1 분리형 랜테스터기  조은 정보'</li></ul>                                                                                                                                  |
+| 9     | <ul><li>'이지넷 산업용 POE+ 리피터 NEXT-POE201EX 최대300M/RJ-45 [KF]  주식회사 케이에프컴퍼니'</li><li>'이지넷유비쿼터스 넥스트 산업용 USB 2.0 리피터 50m (NEXT-USB404)  주식회사 조이쿨'</li><li>'넥시 HDMI 리피터 송수신기 세트 50M NX-HR50 NX509  하나샵'</li></ul>                                                                             |
+| 11    | <ul><li>'[랜장비] NST NSB-230S SDSL 랜모뎀, 모뎀에서 전화선으로 최대 4.5km 까지 데이타 전송  (주)랜장비'</li><li>'(IPD) NST 엔에스티정보통신 NSB-230S SDSL 랜모뎀, 모뎀에서 전화선으로 최대 4.5km 까지 데이타 전송  (주)아이피드림'</li><li>'TL-POE10R 티피링크 [PoE 스플리터] 1000Mbps IEEE 802.3af 전압 조절스위치 5V 9V 12V 선택 가능 전송거리 100M 지원  비전네트워크'</li></ul> |
+| 0     | <ul><li>'EFM ipTIME Ring-AX 무선확장기  디에스큐브스토어'</li><li>'1300K 유무선공유기 AX1800 빔포밍 MU MIMO 기가비트 듀얼밴드 [0001]단일상품 CJONSTYLE'</li><li>'ipTIME Ring-AX WiFi 6 PoE 무선AP 기가 메시 와이파이확장기 증폭기 중계기  애플준웍스'</li></ul>                                                                                  |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9336 |
+
+## 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_el5")
+# Run inference
+preds = model("7102KVM-4K  (주)이지넷유비쿼터스")
+```
+
+<!--
+### 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   | 3   | 8.8470 | 24  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 4                     |
+| 1     | 50                    |
+| 2     | 26                    |
+| 3     | 50                    |
+| 4     | 50                    |
+| 5     | 50                    |
+| 6     | 50                    |
+| 7     | 32                    |
+| 8     | 50                    |
+| 9     | 50                    |
+| 10    | 6                     |
+| 11    | 3                     |
+| 12    | 50                    |
+| 13    | 50                    |
+| 14    | 50                    |
+| 15    | 50                    |
+
+### Training Hyperparameters
+- batch_size: (512, 512)
+- num_epochs: (20, 20)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 40
+- body_learning_rate: (2e-05, 2e-05)
+- head_learning_rate: 2e-05
+- 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: False
+
+### Training Results
+| Epoch   | Step | Training Loss | Validation Loss |
+|:-------:|:----:|:-------------:|:---------------:|
+| 0.0102  | 1    | 0.4967        | -               |
+| 0.5102  | 50   | 0.3039        | -               |
+| 1.0204  | 100  | 0.1904        | -               |
+| 1.5306  | 150  | 0.0492        | -               |
+| 2.0408  | 200  | 0.0328        | -               |
+| 2.5510  | 250  | 0.0146        | -               |
+| 3.0612  | 300  | 0.0101        | -               |
+| 3.5714  | 350  | 0.0137        | -               |
+| 4.0816  | 400  | 0.0023        | -               |
+| 4.5918  | 450  | 0.0002        | -               |
+| 5.1020  | 500  | 0.0001        | -               |
+| 5.6122  | 550  | 0.0001        | -               |
+| 6.1224  | 600  | 0.0037        | -               |
+| 6.6327  | 650  | 0.0001        | -               |
+| 7.1429  | 700  | 0.0001        | -               |
+| 7.6531  | 750  | 0.0001        | -               |
+| 8.1633  | 800  | 0.0039        | -               |
+| 8.6735  | 850  | 0.0039        | -               |
+| 9.1837  | 900  | 0.002         | -               |
+| 9.6939  | 950  | 0.0007        | -               |
+| 10.2041 | 1000 | 0.0001        | -               |
+| 10.7143 | 1050 | 0.0001        | -               |
+| 11.2245 | 1100 | 0.0001        | -               |
+| 11.7347 | 1150 | 0.0           | -               |
+| 12.2449 | 1200 | 0.0           | -               |
+| 12.7551 | 1250 | 0.0002        | -               |
+| 13.2653 | 1300 | 0.0001        | -               |
+| 13.7755 | 1350 | 0.0001        | -               |
+| 14.2857 | 1400 | 0.0           | -               |
+| 14.7959 | 1450 | 0.0           | -               |
+| 15.3061 | 1500 | 0.0002        | -               |
+| 15.8163 | 1550 | 0.0           | -               |
+| 16.3265 | 1600 | 0.0001        | -               |
+| 16.8367 | 1650 | 0.0023        | -               |
+| 17.3469 | 1700 | 0.0           | -               |
+| 17.8571 | 1750 | 0.0001        | -               |
+| 18.3673 | 1800 | 0.0001        | -               |
+| 18.8776 | 1850 | 0.0           | -               |
+| 19.3878 | 1900 | 0.0           | -               |
+| 19.8980 | 1950 | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0.dev0
+- Sentence Transformers: 3.1.1
+- Transformers: 4.46.1
+- PyTorch: 2.4.0+cu121
+- Datasets: 2.20.0
+- Tokenizers: 0.20.0
+
+## 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_el",
+  "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": "4.46.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.46.1",
+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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

model.safetensors

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+oid sha256:803b59d7213ba2e7c703021b878c1a00bd454e75623e1db361e436b056dbdd42
+size 442494816

model_head.pkl

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+oid sha256:606a3aec0daead1d3ab2414ede61210b243217d053b590964e0172d73b067bfe
+size 99399

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
+  },
+  "cls_token": {
+    "content": "[CLS]",
+    "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,
+    "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 +1,66 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[CLS]",
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+  "cls_token": "[CLS]",
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+  "do_lower_case": false,
+  "eos_token": "[SEP]",
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+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
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+  "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]"
+}