Push model using huggingface_hub.

edbcf3a verified about 1 year ago

13.3 kB

	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: 돌반지 백일 호랑이 호랑이띠 목걸이 3.75g 토퍼없음_14k 아기목걸이 기본3푼줄 옐로우골드 출산/육아 > 유아동주얼리 > 순금돌반지
	- text: '[국제금거래소] (순도99.9%) 고급 순금 돌반지 1.875g 복(福)_고급케이스 출산/육아 > 유아동주얼리 > 순금돌반지'
	- text: 베블링 순금 아기 돌팔찌 첫돌 백일 돌선물 3.75g 5.625g 7.5g 11.25g 3.75g_02.Happy 100 Days_국문
	가는체 출산/육아 > 유아동주얼리 > 순금주얼리
	- text: 별별나라 24k 순금 돌반지 조카 첫돌 아기백일 선물 3.75g 행운의 토끼 돌반지_기본 케이스 출산/육아 > 유아동주얼리 > 순금돌반지
	- text: 바 탄생석 미아방지 실버세트/ 목걸이/ 팔찌 바 탄생석 미아방지 실버목걸이_1월(Garnet)_I타입(영문 우아체) 출산/육아 > 유아동주얼리
	> 주얼리세트
	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 \|
	\|:------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 0.0 \| <ul><li>'핸드메이드 어린이 귀걸이 안아픈 귀찌 실리콘 나사형 진주볼-화이트 출산/육아 > 유아동주얼리 > 귀걸이'</li><li>'초등학생 어린이 귀걸이 귀찌 클립형 나사형 퍼플 큐빅_클립형 귀찌(무니켈) 출산/육아 > 유아동주얼리 > 귀걸이'</li><li>'어린이 초등학생 여아 귀걸이 귀찌 e513-1.미니하트퍼플 드롭_귀찌 출산/육아 > 유아동주얼리 > 귀걸이'</li></ul> \|
	\| 4.0 \| <ul><li>'미니써클 미아방지 실버세트/ 목걸이/ 팔찌 미니써클 미아방지 실버세트_J타입(영문 흘림체)_F타입(영문 바른체) 출산/육아 > 유아동주얼리 > 주얼리세트'</li><li>'겨울왕국2 엘사 눈꽃 목걸이 팔찌 반지 3종세트 출산/육아 > 유아동주얼리 > 주얼리세트'</li><li>'스키니 네모 탄생석 미아방지 실버세트/ 목걸이/ 팔찌 스키니 네모 미아방지 실버세트_1월(Garnet)_H타입(영문 볼드체) 출산/육아 > 유아동주얼리 > 주얼리세트'</li></ul> \|
	\| 2.0 \| <ul><li>'리바스골드 99.9 순금 1g/1.875g/3.75g 돌반지 금수저 순금열쇠 돌팔찌 모음전 09. 돌반지(왕관하트)_1.875g_H. 금박 폴라로이드 액자 패키지 출산/육아 > 유아동주얼리 > 순금돌반지'</li><li>'컷팅 왕관 돌반지 돌팔찌 3.75g 7.5g 11.25g 순금 자개함 보자기포장 백일반지 컷팅 왕관 돌반지 3.75g_백일 남색 리본_고급와당케이스 출산/육아 > 유아동주얼리 > 순금돌반지'</li><li>'호랑이 토끼 띠 밴드 돌반지 3.75g 순금 자개함 아기 백일반지 리본 토끼 3.75g_빨강 수국_안음기본케이스 출산/육아 > 유아동주얼리 > 순금돌반지'</li></ul> \|
	\| 5.0 \| <ul><li>'민트 핑크 하트볼체인 실버 아기미아방지팔찌 3mm볼_빨강＋각인함★_14＋2 （5-7세） 출산/육아 > 유아동주얼리 > 팔찌'</li><li>'스틱 바 미아방지 목걸이 팔찌 아기 아이 어린이 유아동 실버 순은 팔찌로 제작해주세요_탄생석 2월 출산/육아 > 유아동주얼리 > 팔찌'</li><li>'미아방지팔찌(14K 18K 발도장 돌팔찌 돌선물 백일선물 탄생석) 1860B 3번 고딕체_10월 핑크투어마린_18K / 사다리체인 출산/육아 > 유아동주얼리 > 팔찌'</li></ul> \|
	\| 3.0 \| <ul><li>'순금 돌팔찌 3.75g 7.5g 11.25g 8종 모음전 24k 첫돌 백일 아기 조카 선물 ⑶11.25g_⑴천사날개 출산/육아 > 유아동주얼리 > 순금주얼리'</li><li>'순금 당근 토끼띠 아기 돌목걸이 백일목걸이 3.75g 7.5g (99.9%) 순금 당근 토끼띠 아기목걸이/7.5g_색동 복주머니 팩킹_여자아기 출산/육아 > 유아동주얼리 > 순금주얼리'</li><li>'금수저 책연필 돌팔찌 7.5g 11.25g 18.75g 순금 자개함 포장 백일팔찌 금수저7.5g_첫돌 빨강 리본_고급송학자개케이스 출산/육아 > 유아동주얼리 > 순금주얼리'</li></ul> \|
	\| 1.0 \| <ul><li>'하트 데이지 14k 미아방지목걸이 18k 아기 금목걸이 여아 딸 출산/육아 > 유아동주얼리 > 목걸이/펜던트'</li><li>'실버 미아방지 목걸이 하트 탄생석 아기 돌선물 순은925 어린이날 기념 각인 조카선물 출산/육아 > 유아동주얼리 > 목걸이/펜던트'</li><li>'실버 오목코인 키즈 아기목걸이 미아방지목걸이 출산/육아 > 유아동주얼리 > 목걸이/펜던트'</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_bc21")
	# Run inference
	preds = model("[국제금거래소] (순도99.9%) 고급 순금 돌반지 1.875g 복(福)_고급케이스 출산/육아 > 유아동주얼리 > 순금돌반지")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 7 \| 15.7703 \| 32 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0.0 \| 70 \|
	\| 1.0 \| 20 \|
	\| 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 \|
	\|:-------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0137 \| 1 \| 0.4867 \| - \|
	\| 0.6849 \| 50 \| 0.4987 \| - \|
	\| 1.3699 \| 100 \| 0.3808 \| - \|
	\| 2.0548 \| 150 \| 0.1425 \| - \|
	\| 2.7397 \| 200 \| 0.053 \| - \|
	\| 3.4247 \| 250 \| 0.0037 \| - \|
	\| 4.1096 \| 300 \| 0.0001 \| - \|
	\| 4.7945 \| 350 \| 0.0001 \| - \|
	\| 5.4795 \| 400 \| 0.0001 \| - \|
	\| 6.1644 \| 450 \| 0.0001 \| - \|
	\| 6.8493 \| 500 \| 0.0 \| - \|
	\| 7.5342 \| 550 \| 0.0 \| - \|
	\| 8.2192 \| 600 \| 0.0 \| - \|
	\| 8.9041 \| 650 \| 0.0 \| - \|
	\| 9.5890 \| 700 \| 0.0 \| - \|
	\| 10.2740 \| 750 \| 0.0 \| - \|
	\| 10.9589 \| 800 \| 0.0 \| - \|
	\| 11.6438 \| 850 \| 0.0 \| - \|
	\| 12.3288 \| 900 \| 0.0 \| - \|
	\| 13.0137 \| 950 \| 0.0 \| - \|
	\| 13.6986 \| 1000 \| 0.0 \| - \|
	\| 14.3836 \| 1050 \| 0.0 \| - \|
	\| 15.0685 \| 1100 \| 0.0 \| - \|
	\| 15.7534 \| 1150 \| 0.0 \| - \|
	\| 16.4384 \| 1200 \| 0.0 \| - \|
	\| 17.1233 \| 1250 \| 0.0 \| - \|
	\| 17.8082 \| 1300 \| 0.0 \| - \|
	\| 18.4932 \| 1350 \| 0.0 \| - \|
	\| 19.1781 \| 1400 \| 0.0 \| - \|
	\| 19.8630 \| 1450 \| 0.0 \| - \|
	\| 20.5479 \| 1500 \| 0.0 \| - \|
	\| 21.2329 \| 1550 \| 0.0 \| - \|
	\| 21.9178 \| 1600 \| 0.0 \| - \|
	\| 22.6027 \| 1650 \| 0.0 \| - \|
	\| 23.2877 \| 1700 \| 0.0 \| - \|
	\| 23.9726 \| 1750 \| 0.0 \| - \|
	\| 24.6575 \| 1800 \| 0.0 \| - \|
	\| 25.3425 \| 1850 \| 0.0 \| - \|
	\| 26.0274 \| 1900 \| 0.0 \| - \|
	\| 26.7123 \| 1950 \| 0.0 \| - \|
	\| 27.3973 \| 2000 \| 0.0 \| - \|
	\| 28.0822 \| 2050 \| 0.0 \| - \|
	\| 28.7671 \| 2100 \| 0.0 \| - \|
	\| 29.4521 \| 2150 \| 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}
	}
	```

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