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: 타공판닷컴 세계지도 대형 월드맵 세계지도03_600x900 (주)오빌
- text: >-
스프링 제본 PDF 흑백 고품질 레이저 출력 - 흑백 양면인쇄 모조지80g 50p 스프링 흑백양면●●_모조지100g_167~170
page 도서출판 법현
- text: '[달페이퍼] 달페이퍼 미니미니 6종 엽서 postcard 인테리어엽서 6 미니미니 일하는 주식회사 천유닷컴'
- text: >-
환갑 현수막 회갑 생신 잔치 플랜카드 C00 네임 소형100x70cm C22 얼쑤(남자)-자유문구포토형_소형 100x70cm
(주)엔비웨일인터렉티브
- text: >-
스프링 제본 PDF 흑백 고품질 레이저 출력 - 흑백 양면인쇄 모조지80g 50p 스프링 흑백단면●_모조지80g_179~182
page 도서출판 법현
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: 0.964332367808258
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: 17 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 |
|---|---|
| 6.0 |
|
| 2.0 |
|
| 5.0 |
|
| 13.0 |
|
| 11.0 |
|
| 10.0 |
|
| 4.0 |
|
| 0.0 |
|
| 14.0 |
|
| 7.0 |
|
| 16.0 |
|
| 15.0 |
|
| 9.0 |
|
| 12.0 |
|
| 3.0 |
|
| 1.0 |
|
| 8.0 |
|
Evaluation
Metrics
| Label | Metric |
|---|---|
| all | 0.9643 |
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_lh8")
# Run inference
preds = model("타공판닷컴 세계지도 대형 월드맵 세계지도03_600x900 (주)오빌")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 4 | 11.1176 | 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 |
| 12.0 | 50 |
| 13.0 | 50 |
| 14.0 | 50 |
| 15.0 | 50 |
| 16.0 | 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 |
|---|---|---|---|
| 0.0075 | 1 | 0.4622 | - |
| 0.3759 | 50 | 0.3276 | - |
| 0.7519 | 100 | 0.2741 | - |
| 1.1278 | 150 | 0.167 | - |
| 1.5038 | 200 | 0.082 | - |
| 1.8797 | 250 | 0.0368 | - |
| 2.2556 | 300 | 0.0406 | - |
| 2.6316 | 350 | 0.0331 | - |
| 3.0075 | 400 | 0.0282 | - |
| 3.3835 | 450 | 0.0144 | - |
| 3.7594 | 500 | 0.005 | - |
| 4.1353 | 550 | 0.0036 | - |
| 4.5113 | 600 | 0.0036 | - |
| 4.8872 | 650 | 0.0005 | - |
| 5.2632 | 700 | 0.0003 | - |
| 5.6391 | 750 | 0.0003 | - |
| 6.0150 | 800 | 0.0002 | - |
| 6.3910 | 850 | 0.0003 | - |
| 6.7669 | 900 | 0.0002 | - |
| 7.1429 | 950 | 0.0002 | - |
| 7.5188 | 1000 | 0.0001 | - |
| 7.8947 | 1050 | 0.0001 | - |
| 8.2707 | 1100 | 0.0001 | - |
| 8.6466 | 1150 | 0.0001 | - |
| 9.0226 | 1200 | 0.0001 | - |
| 9.3985 | 1250 | 0.0001 | - |
| 9.7744 | 1300 | 0.0001 | - |
| 10.1504 | 1350 | 0.0001 | - |
| 10.5263 | 1400 | 0.0001 | - |
| 10.9023 | 1450 | 0.0001 | - |
| 11.2782 | 1500 | 0.0001 | - |
| 11.6541 | 1550 | 0.0001 | - |
| 12.0301 | 1600 | 0.0001 | - |
| 12.4060 | 1650 | 0.0001 | - |
| 12.7820 | 1700 | 0.0001 | - |
| 13.1579 | 1750 | 0.0001 | - |
| 13.5338 | 1800 | 0.0001 | - |
| 13.9098 | 1850 | 0.0001 | - |
| 14.2857 | 1900 | 0.0001 | - |
| 14.6617 | 1950 | 0.0001 | - |
| 15.0376 | 2000 | 0.0001 | - |
| 15.4135 | 2050 | 0.0001 | - |
| 15.7895 | 2100 | 0.0001 | - |
| 16.1654 | 2150 | 0.0001 | - |
| 16.5414 | 2200 | 0.0001 | - |
| 16.9173 | 2250 | 0.0001 | - |
| 17.2932 | 2300 | 0.0001 | - |
| 17.6692 | 2350 | 0.0001 | - |
| 18.0451 | 2400 | 0.0001 | - |
| 18.4211 | 2450 | 0.0001 | - |
| 18.7970 | 2500 | 0.0001 | - |
| 19.1729 | 2550 | 0.0001 | - |
| 19.5489 | 2600 | 0.0001 | - |
| 19.9248 | 2650 | 0.0001 | - |
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}
}