metadata
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: LSA 흡착식판 친환경 아이 캠핑 실리콘 이유식 식판 접시 블루 출산/육아 > 이유식용품 > 유아식기
- text: "캐치티니핑 수저세트 교정젓가락 유아 교정용 아기젓가락 어린이젓가락 연습용 5.\uFEFF티니핑 물컵 스텐컵_3.퐁당핑 논슬립 스텐컵 출산/육아 > 이유식용품 > 연습용젓가락"
- text: >-
닥터브라운 흘림방지 360도컵 3개 (반투명 트레이닝 아기안전컵 - 9종 중 택3) 3) 300ml(손잡이) 블루_4)
300ml(손잡이) 그린_3) 300ml(손잡이) 블루 출산/육아 > 이유식용품 > 유아컵
- text: >-
귀여운 유아식기 흡착볼 접시 컵 스푼 포크 세트 이유식식기 돌아기식판 아기선물 3.디너세트(식판+볼+컵+스푼&포크)_01 Rainy
출산/육아 > 이유식용품 > 유아식기
- text: 4p 투데코 이유식 도자기 조리기세트 화이트 출산/육아 > 이유식용품 > 조리기
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
name: Accuracy
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: 8 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 |
|---|---|
| 7.0 |
|
| 3.0 |
|
| 1.0 |
|
| 5.0 |
|
| 0.0 |
|
| 4.0 |
|
| 2.0 |
|
| 6.0 |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 1.0 |
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_bc26")
# Run inference
preds = model("4p 투데코 이유식 도자기 조리기세트 화이트 출산/육아 > 이유식용품 > 조리기")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 7 | 15.075 | 30 |
| Label | Training Sample Count |
|---|---|
| 0.0 | 70 |
| 1.0 | 70 |
| 2.0 | 70 |
| 3.0 | 70 |
| 4.0 | 70 |
| 5.0 | 70 |
| 6.0 | 70 |
| 7.0 | 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 |
|---|---|---|---|
| 0.0091 | 1 | 0.4946 | - |
| 0.4545 | 50 | 0.5017 | - |
| 0.9091 | 100 | 0.4932 | - |
| 1.3636 | 150 | 0.3697 | - |
| 1.8182 | 200 | 0.0968 | - |
| 2.2727 | 250 | 0.0213 | - |
| 2.7273 | 300 | 0.0175 | - |
| 3.1818 | 350 | 0.0186 | - |
| 3.6364 | 400 | 0.0187 | - |
| 4.0909 | 450 | 0.0136 | - |
| 4.5455 | 500 | 0.0007 | - |
| 5.0 | 550 | 0.0001 | - |
| 5.4545 | 600 | 0.0001 | - |
| 5.9091 | 650 | 0.0001 | - |
| 6.3636 | 700 | 0.0001 | - |
| 6.8182 | 750 | 0.0001 | - |
| 7.2727 | 800 | 0.0001 | - |
| 7.7273 | 850 | 0.0001 | - |
| 8.1818 | 900 | 0.0 | - |
| 8.6364 | 950 | 0.0 | - |
| 9.0909 | 1000 | 0.0 | - |
| 9.5455 | 1050 | 0.0 | - |
| 10.0 | 1100 | 0.0 | - |
| 10.4545 | 1150 | 0.0 | - |
| 10.9091 | 1200 | 0.0 | - |
| 11.3636 | 1250 | 0.0 | - |
| 11.8182 | 1300 | 0.0 | - |
| 12.2727 | 1350 | 0.0 | - |
| 12.7273 | 1400 | 0.0 | - |
| 13.1818 | 1450 | 0.0 | - |
| 13.6364 | 1500 | 0.0 | - |
| 14.0909 | 1550 | 0.0 | - |
| 14.5455 | 1600 | 0.0 | - |
| 15.0 | 1650 | 0.0 | - |
| 15.4545 | 1700 | 0.0 | - |
| 15.9091 | 1750 | 0.0 | - |
| 16.3636 | 1800 | 0.0 | - |
| 16.8182 | 1850 | 0.0 | - |
| 17.2727 | 1900 | 0.0 | - |
| 17.7273 | 1950 | 0.0 | - |
| 18.1818 | 2000 | 0.0 | - |
| 18.6364 | 2050 | 0.0 | - |
| 19.0909 | 2100 | 0.0 | - |
| 19.5455 | 2150 | 0.0 | - |
| 20.0 | 2200 | 0.0 | - |
| 20.4545 | 2250 | 0.0 | - |
| 20.9091 | 2300 | 0.0 | - |
| 21.3636 | 2350 | 0.0 | - |
| 21.8182 | 2400 | 0.0 | - |
| 22.2727 | 2450 | 0.0 | - |
| 22.7273 | 2500 | 0.0 | - |
| 23.1818 | 2550 | 0.0 | - |
| 23.6364 | 2600 | 0.0 | - |
| 24.0909 | 2650 | 0.0 | - |
| 24.5455 | 2700 | 0.0 | - |
| 25.0 | 2750 | 0.0 | - |
| 25.4545 | 2800 | 0.0 | - |
| 25.9091 | 2850 | 0.0 | - |
| 26.3636 | 2900 | 0.0 | - |
| 26.8182 | 2950 | 0.0 | - |
| 27.2727 | 3000 | 0.0 | - |
| 27.7273 | 3050 | 0.0 | - |
| 28.1818 | 3100 | 0.0 | - |
| 28.6364 | 3150 | 0.0 | - |
| 29.0909 | 3200 | 0.0 | - |
| 29.5455 | 3250 | 0.0 | - |
| 30.0 | 3300 | 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
@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}
}