master_cate_el8 / README.md

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	---
	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: HDTOP USB3.0 to HDMI 4K 영상 캡처보드15cm/HT-3C009/입력 4K 60Hz/녹화 1080P 60Hz/딜레이
	없는 실시간 녹화/알루미늄 하우징/금도금 커넥터 디피시스템
	- text: 넥시 CAP02 USB HDMI 캡쳐보드 젠더타입 주식회사 디앤에스티
	- text: 블랙매직 DeckLink 8K Pro 덱링크 8k pro 디지탈A/V세상
	- text: 브리츠 BZ-SP600X 화이트 커브드 게이밍 사운드바 (주)에이치앤인터내셔널
	- text: AVerMedia Live Gamer 4K 2.1 GC575 초이스컴퓨터 주식회사
	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: 0.8028770510227017
	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: 10 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 \|
	\|:------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 3 \| <ul><li>'Britz 브리츠인터내셔널 BA-UMK120 다크실버 주식회사 꿈누리'</li><li>'Britz Accessories BA-R9 SoundBar 스피커 [화이트] (주)조이젠'</li><li>'크리에이티브 PEBBLE V2 (주)아이티블루'</li></ul> \|
	\| 2 \| <ul><li>'GN-2000S 구즈넥 마이크 콘덴서 (회의, 강연, 설교, 스피치, 교회, 법원, 방송) 사운드스토리'</li><li>'컴스 MT195 회의실용 콘덴서 마이크 아이코다(주)'</li><li>'고독스 EM68 RGB 카디오이드 USB 콘덴서 마이크 스탠드 / 납품 세금계산서 가능 주식회사 모즈인터내셔날'</li></ul> \|
	\| 8 \| <ul><li>'레이저코리아 Razer Kiyo X 키요 X 웹캠 YT 주식회사 옐로우트리'</li><li>'앱코 APC930 QHD 웹캠 (블랙) 주식회사 동행하기'</li><li>'[병행,벌크]로지텍 C922 Pro Stream 웹캠 더블유에이취제이(WHJ)'</li></ul> \|
	\| 5 \| <ul><li>'포커스라이트 스칼렛2i2 3세대 FocusriScarlett 2i2 3rd Gen 와이지스토어(주) (YG store Co., Ltd)'</li><li>'Focusrite 포커스라이트 Scarlett 18i8 3세대 오디오 인터페이스 씨엠뮤직(CM music)'</li><li>'크리에이티브 Creative 사운드 블라스터 X5 (주)아토닉스'</li></ul> \|
	\| 4 \| <ul><li>'CORSAIR VOID RGB ELITE WIRELESS (화이트, 정품) 주식회사 꿈누리'</li><li>'TFG CH240 컬러풀 7.1Ch 게이밍헤드셋 (초경량 / 노이즈캔슬링 / 로스트아크) 블랙 (주)한성'</li><li>'로지텍 PRO X 2 LIGHTSPEED (핑크) 주식회사 조이쿨'</li></ul> \|
	\| 7 \| <ul><li>'HD60X 주식회사 글렌트리'</li><li>'블랙매직 Blackmagic Design ATEM Mini Pro 아템미니프로 어썸팩토리(awesome factory)'</li><li>'AVerMedia ER330 EzRecorder PVR(독립형 녹화장치) (주)스트림텍'</li></ul> \|
	\| 0 \| <ul><li>'이지넷유비쿼터스 NEXT-4516HDP 16채널 비디오 발룬 수신기 에이치엠에스'</li><li>'하이크비젼 DS-7604NI-K1/4P 4채널 IP POE NVR CCTV테크'</li><li>'[HIKVISION 공식 수입원] 하이크비전 DS-7608NI-I2/8P UHD 4K IP카메라 네트워크 녹화기 (주)씨넥스존'</li></ul> \|
	\| 6 \| <ul><li>'스카이디지탈 DT-800 HDTV 안테나 (주)컴퓨존'</li><li>'(스카이디지탈) DT-800 HDTV 안테나 /안테나 엠지솔루션'</li><li>'무료 스카이디지탈 SKY DT-800 HDTV 지상파 안테나 주식회사에프엘인텍'</li></ul> \|
	\| 1 \| <ul><li>'서진네트웍스 유니콘 AV-M9 UHD4K 안드로이드 셋탑박스 디빅스미디어플레이어 광고용디스플레이 (주)컴퓨존'</li><li>'유니콘 AV-M7 2세대 디빅스플레이어 UHD 4K지원 미디어플레이어 더원'</li><li>'서진네트웍스 UNICORN AV-M9 정품 멀티미디어 플레이어/영샵 영 샵'</li></ul> \|
	\| 9 \| <ul><li>'옴니트로닉 MSP-Q1 2채널 휴대용 마이크스피커 핸드+핸드마이크 에이스전자'</li><li>'[공식] 에버미디어 AS311 Speakerphon 휴대용 스피커폰 AI 소음감지 USB전원 주식회사 이선디지탈'</li><li>'브리츠 BE-MC100 야외설치 아웃도어 방수 스피커 (주)담다몰'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Metric \|
	\|:--------\|:-------\|
	\| all \| 0.8029 \|

	## 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_el8")
	# Run inference
	preds = model("넥시 CAP02 USB HDMI 캡쳐보드 젠더타입 주식회사 디앤에스티")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 3 \| 9.3503 \| 26 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 49 \|
	\| 1 \| 25 \|
	\| 2 \| 50 \|
	\| 3 \| 50 \|
	\| 4 \| 50 \|
	\| 5 \| 50 \|
	\| 6 \| 15 \|
	\| 7 \| 50 \|
	\| 8 \| 50 \|
	\| 9 \| 5 \|

	### 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0161 \| 1 \| 0.496 \| - \|
	\| 0.8065 \| 50 \| 0.2401 \| - \|
	\| 1.6129 \| 100 \| 0.0385 \| - \|
	\| 2.4194 \| 150 \| 0.025 \| - \|
	\| 3.2258 \| 200 \| 0.0181 \| - \|
	\| 4.0323 \| 250 \| 0.0004 \| - \|
	\| 4.8387 \| 300 \| 0.0002 \| - \|
	\| 5.6452 \| 350 \| 0.0001 \| - \|
	\| 6.4516 \| 400 \| 0.0002 \| - \|
	\| 7.2581 \| 450 \| 0.0001 \| - \|
	\| 8.0645 \| 500 \| 0.0001 \| - \|
	\| 8.8710 \| 550 \| 0.0001 \| - \|
	\| 9.6774 \| 600 \| 0.0001 \| - \|
	\| 10.4839 \| 650 \| 0.0001 \| - \|
	\| 11.2903 \| 700 \| 0.0001 \| - \|
	\| 12.0968 \| 750 \| 0.0 \| - \|
	\| 12.9032 \| 800 \| 0.0 \| - \|
	\| 13.7097 \| 850 \| 0.0 \| - \|
	\| 14.5161 \| 900 \| 0.0 \| - \|
	\| 15.3226 \| 950 \| 0.0 \| - \|
	\| 16.1290 \| 1000 \| 0.0 \| - \|
	\| 16.9355 \| 1050 \| 0.0 \| - \|
	\| 17.7419 \| 1100 \| 0.0 \| - \|
	\| 18.5484 \| 1150 \| 0.0 \| - \|
	\| 19.3548 \| 1200 \| 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}
	}
	```

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