metadata
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 피카부 보넷 유아 신생아 모자 보닛 봄 가을 겨울 점핑 보넷_노랑_S(1-3세) 출산/육아 > 신생아의류 > 신생아모자/보닛
- text: (23겨울) 베베홀릭 레몬배앓이세트 M_크림 출산/육아 > 신생아의류 > 바디슈트/롬퍼
- text: >-
긴팔 매쉬 메쉬 반팔 나시 신생아 아기 바디 슈트 아기 옷 돌 50일 6개월 출산선물 21.버터바디슈트_12M_아이보리 출산/육아 >
신생아의류 > 바디슈트/롬퍼
- text: >-
아기 크리스마스 옷 산타 신생아 돌 아기옷 백일 50일 바디수트 루돌프 7.아기자기산타_화이트_73 출산/육아 > 신생아의류 >
바디슈트/롬퍼
- text: >-
편안행 신생아 레깅스 몸빼 고쟁이 멜빵 바지 돌 전 갓난 아기 영유아 옷 2개월 겨울 베베유발레깅스_크림_M6~12M) 출산/육아 >
신생아의류 > 레그/스패츠
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_bc11")
# Run inference
preds = model("(23겨울) 베베홀릭 레몬배앓이세트 M_크림 출산/육아 > 신생아의류 > 바디슈트/롬퍼")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 7 | 15.0589 | 26 |
| 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 | 0.4951 | - |
| 0.4545 | 50 | 0.5028 | - |
| 0.9091 | 100 | 0.4958 | - |
| 1.3636 | 150 | 0.2683 | - |
| 1.8182 | 200 | 0.0089 | - |
| 2.2727 | 250 | 0.0 | - |
| 2.7273 | 300 | 0.0 | - |
| 3.1818 | 350 | 0.0 | - |
| 3.6364 | 400 | 0.0 | - |
| 4.0909 | 450 | 0.0 | - |
| 4.5455 | 500 | 0.0 | - |
| 5.0 | 550 | 0.0 | - |
| 5.4545 | 600 | 0.0 | - |
| 5.9091 | 650 | 0.0 | - |
| 6.3636 | 700 | 0.0 | - |
| 6.8182 | 750 | 0.0 | - |
| 7.2727 | 800 | 0.0 | - |
| 7.7273 | 850 | 0.0 | - |
| 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}
}