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: 이소닉 MR-120 8GB 동의 화이트선셋
- text: >-
이소닉 PCM-007 2G 무손실 PCM녹음 간단한사용법 볼펜녹음기 고성능 인터뷰 회의녹음/강의녹음/비밀녹음/녹취기/전화녹음 증거보존
초소형녹음기+USB메모리+MP3 원거리녹음 2GB 진경전자
- text: ICD-PX470 4GB 속기사녹음기 비밀녹음기 장시간 동백
- text: 베스타 전자사전 BK-100 핫앤쿨 (HNC)
- text: TASCAM 4트랙 디지털 오디오 레코더 DR-40X 고운소리사
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: 0.9616204690831557
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 |
|---|---|
| 5 |
|
| 1 |
|
| 0 |
|
| 2 |
|
| 3 |
|
| 4 |
|
Evaluation
Metrics
| Label | Metric |
|---|---|
| all | 0.9616 |
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_el23")
# Run inference
preds = model("이소닉 MR-120 8GB 동의 화이트선셋")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 3 | 10.4144 | 23 |
| Label | Training Sample Count |
|---|---|
| 0 | 50 |
| 1 | 42 |
| 2 | 50 |
| 3 | 11 |
| 4 | 12 |
| 5 | 16 |
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 | 0.4957 | - |
| 1.7241 | 50 | 0.0279 | - |
| 3.4483 | 100 | 0.0001 | - |
| 5.1724 | 150 | 0.0001 | - |
| 6.8966 | 200 | 0.0001 | - |
| 8.6207 | 250 | 0.0001 | - |
| 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}
}