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: >-
한글과컴퓨터 한컴오피스 2024 한글 Open 라이선스 [기업용/영구/2User이상] 한컴오피스 2024 (한글/한셀/한쇼)
(주)유비소프트웨어
- text: 한글과컴퓨터 한글 2022 (기업용/패키지/USB방식) 아이코다(주)
- text: 한글과컴퓨터 한컴독스 기업용 ESD 1년 사용 (주)대성클라우드
- text: '[한글과컴퓨터] 한컴오피스 2022 [기업용/패키지/1년사용/제품키배송형] (주)컴퓨존'
- text: '[마이크로소프트코리아] MS Windows 7 Professional DSP 한글 64bit/정품라벨 (주)소프트존'
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: 1
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: 6 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 |
|---|---|
| 4 |
|
| 1 |
|
| 2 |
|
| 3 |
|
| 5 |
|
| 0 |
|
Evaluation
Metrics
| Label | Metric |
|---|---|
| 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_el12")
# Run inference
preds = model("한글과컴퓨터 한컴독스 기업용 ESD 1년 사용 (주)대성클라우드")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 6 | 11.8852 | 21 |
| Label | Training Sample Count |
|---|---|
| 0 | 3 |
| 1 | 34 |
| 2 | 33 |
| 3 | 50 |
| 4 | 50 |
| 5 | 13 |
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 |
|---|---|---|---|
| 0.0345 | 1 | 0.496 | - |
| 1.7241 | 50 | 0.0031 | - |
| 3.4483 | 100 | 0.0001 | - |
| 5.1724 | 150 | 0.0 | - |
| 6.8966 | 200 | 0.0 | - |
| 8.6207 | 250 | 0.0 | - |
| 10.3448 | 300 | 0.0 | - |
| 12.0690 | 350 | 0.0 | - |
| 13.7931 | 400 | 0.0 | - |
| 15.5172 | 450 | 0.0 | - |
| 17.2414 | 500 | 0.0 | - |
| 18.9655 | 550 | 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}
}