metadata
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: 마사지 복 경락 잠옷 피부샵 세트 호텔 아로마 스파 HFG 그레이 남성_XXL 민물유통
- text: 바스템 리워터 히든커버 필터 샤워기 리워터 히든커버 교체필터 4개입 바스템
- text: 어메니티타올 환갑 칠순 팔순 구순 회갑 고희 답례품 40수 무형광 주방 고리수건 자수 화이트_동백 어메니티타올
- text: >-
[추가 5%할인] 바디럽 비타필터 2개 (녹물염소제거/보습효과/샤워기필터/비타민필터/비타샤워기) [NEW] 민티시트러스 NEW
우디오렌지_NEW 퓨어소피 메가글로벌002
- text: 깔끔디자인 욕실수건걸이 6 pcs 세트 가정용 워시 브러쉬 컵 액체 블랙수건걸이 컵세트 빨간 리마108
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: 0.6881059449647262
name: Metric
SetFit with mini1013/master_domain
This is a SetFit model that can be used for Text Classification. This SetFit model uses mini1013/master_domain as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- 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
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 12 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
| Label | Examples |
|---|---|
| 10.0 |
|
| 8.0 |
|
| 1.0 |
|
| 6.0 |
|
| 5.0 |
|
| 9.0 |
|
| 4.0 |
|
| 0.0 |
|
| 7.0 |
|
| 11.0 |
|
| 3.0 |
|
| 2.0 |
|
Evaluation
Metrics
| Label | Metric |
|---|---|
| all | 0.6881 |
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_cate_lh18")
# Run inference
preds = model("바스템 리워터 히든커버 필터 샤워기 리워터 히든커버 교체필터 4개입 바스템")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 3 | 10.42 | 26 |
| Label | Training Sample Count |
|---|---|
| 0.0 | 50 |
| 1.0 | 50 |
| 2.0 | 50 |
| 3.0 | 50 |
| 4.0 | 50 |
| 5.0 | 50 |
| 6.0 | 50 |
| 7.0 | 50 |
| 8.0 | 50 |
| 9.0 | 50 |
| 10.0 | 50 |
| 11.0 | 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 |
|---|---|---|---|
| 0.0106 | 1 | 0.4109 | - |
| 0.5319 | 50 | 0.305 | - |
| 1.0638 | 100 | 0.2044 | - |
| 1.5957 | 150 | 0.0728 | - |
| 2.1277 | 200 | 0.0314 | - |
| 2.6596 | 250 | 0.0054 | - |
| 3.1915 | 300 | 0.0036 | - |
| 3.7234 | 350 | 0.0103 | - |
| 4.2553 | 400 | 0.0047 | - |
| 4.7872 | 450 | 0.0002 | - |
| 5.3191 | 500 | 0.0001 | - |
| 5.8511 | 550 | 0.0001 | - |
| 6.3830 | 600 | 0.0001 | - |
| 6.9149 | 650 | 0.0001 | - |
| 7.4468 | 700 | 0.0001 | - |
| 7.9787 | 750 | 0.0001 | - |
| 8.5106 | 800 | 0.0001 | - |
| 9.0426 | 850 | 0.0 | - |
| 9.5745 | 900 | 0.0001 | - |
| 10.1064 | 950 | 0.0001 | - |
| 10.6383 | 1000 | 0.0 | - |
| 11.1702 | 1050 | 0.0 | - |
| 11.7021 | 1100 | 0.0 | - |
| 12.2340 | 1150 | 0.0 | - |
| 12.7660 | 1200 | 0.0001 | - |
| 13.2979 | 1250 | 0.0 | - |
| 13.8298 | 1300 | 0.0 | - |
| 14.3617 | 1350 | 0.0 | - |
| 14.8936 | 1400 | 0.0001 | - |
| 15.4255 | 1450 | 0.0 | - |
| 15.9574 | 1500 | 0.0 | - |
| 16.4894 | 1550 | 0.0 | - |
| 17.0213 | 1600 | 0.0 | - |
| 17.5532 | 1650 | 0.0 | - |
| 18.0851 | 1700 | 0.0001 | - |
| 18.6170 | 1750 | 0.0 | - |
| 19.1489 | 1800 | 0.0 | - |
| 19.6809 | 1850 | 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
@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}
}