SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 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 Sources
Model Labels
Label |
Examples |
2 |
- 'Rapid onset of confusion and weakness, urgent evaluation needed.'
- 'Unconscious patient found, immediate medical response required.'
- 'Urgent: Suspected heart attack, immediate medical attention required.'
|
1 |
- 'Reminder: Your dental check-up is scheduled for Monday, February 05.'
- 'Reminder: Your dental check-up is scheduled for Saturday, February 24.'
- 'Nutritionist appointment reminder for Sunday, January 21.'
|
0 |
- 'Could you verify your lifestyle contact details in our records?'
- 'Kindly update your emergency contact list at your earliest convenience.'
- 'We request you to update your wellness information for our records.'
|
Evaluation
Metrics
Label |
Accuracy |
all |
0.9633 |
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
model = SetFitModel.from_pretrained("konsman/setfit-messages-generated-v2")
preds = model("Sudden severe chest pain, suspecting a cardiac emergency.")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
7 |
9.25 |
12 |
Label |
Training Sample Count |
0 |
8 |
1 |
8 |
2 |
8 |
Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- body_learning_rate: (2.2041595048800003e-05, 2.2041595048800003e-05)
- head_learning_rate: 2.2041595048800003e-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 |
0.0042 |
1 |
0.1564 |
- |
0.2083 |
50 |
0.0039 |
- |
0.4167 |
100 |
0.0006 |
- |
0.625 |
150 |
0.0003 |
- |
0.8333 |
200 |
0.0003 |
- |
1.0417 |
250 |
0.0002 |
- |
1.25 |
300 |
0.0002 |
- |
1.4583 |
350 |
0.0002 |
- |
1.6667 |
400 |
0.0002 |
- |
1.875 |
450 |
0.0002 |
- |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.2
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.0
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}
}