You can read more about the importance and practical use of this model in this article: Mental Health Monitor
test_depres
This is a SetFit model that can be used for text 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.
Usage
To use this model for inference, first install the SetFit library:
python -m pip install setfit
You can then run inference as follows:
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("test_depres")
dict ={0:"positive", 1:"negative"}
# Run inference
preds = model(["What happened to me? I don't know what to do, where to go! Can anyone help me?"])
print(dict.get(preds.numpy()[0]))
Warning: This model cannot be used for medical diagnosis and is not a substitute for a physician!
BibTeX entry and citation info
@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}
}
Citing & Authors
@misc{Uaritm,
title={SetFit: Classification of medical texts},
author={Vitaliy Ostashko},
year={2023},
url={https://esemi.org}
}
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Inference API (serverless) does not yet support sentence-transformers models for this pipeline type.