---
You can read more about the importance and practical use of this model in this article: [Mental Health Monitor](https://medium.com/@uaritm/mental-health-monitor-b90f0b2ee7f6) 


# test_depres

This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Usage

To use this model for inference, first install the SetFit library:

```bash
python -m pip install setfit
```

You can then run inference as follows:

```python
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

```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}
}
```
## Citing & Authors
```
@misc{Uaritm,
      title={SetFit: Classification of medical texts}, 
      author={Vitaliy Ostashko},
      year={2023},
      url={https://esemi.org}
}

<!--- Describe where people can find more information -->
{"language": ["multilingual"], "license": "mit", "tags": ["setfit", "sentence-transformers", "text-classification"], "pipeline_tag": "text-classification"}
sentence-transformers pytorch xlm-roberta setfit text-classification multilingual arxiv:2209.11055 license:mit region:us
TAGS
#sentence-transformers #pytorch #xlm-roberta #setfit #text-classification #multilingual #arxiv-2209.11055 #license-mit #region-us 

---
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:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Usage

To use this model for inference, first install the SetFit library:



You can then run inference as follows:




## BibTeX entry and citation info


## Citing & Authors
'''
@misc{Uaritm,
      title={SetFit: Classification of medical texts}, 
      author={Vitaliy Ostashko},
      year={2023},
      url={URL}
}


# 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:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage

To use this model for inference, first install the SetFit library:



You can then run inference as follows: ## BibTeX entry and citation info ## Citing & Authors
'''
@misc{Uaritm,
      title={SetFit: Classification of medical texts}, 
      author={Vitaliy Ostashko},
      year={2023},
      url={URL}
}
TAGS
#sentence-transformers #pytorch #xlm-roberta #setfit #text-classification #multilingual #arxiv-2209.11055 #license-mit #region-us 
 # 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:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage

To use this model for inference, first install the SetFit library:



You can then run inference as follows: ## BibTeX entry and citation info ## Citing & Authors
'''
@misc{Uaritm,
      title={SetFit: Classification of medical texts}, 
      author={Vitaliy Ostashko},
      year={2023},
      url={URL}
}
TAGS
#sentence-transformers #pytorch #xlm-roberta #setfit #text-classification #multilingual #arxiv-2209.11055 #license-mit #region-us 
# 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:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.## Usage

To use this model for inference, first install the SetFit library:



You can then run inference as follows:## BibTeX entry and citation info## Citing & Authors
'''
@misc{Uaritm,
      title={SetFit: Classification of medical texts}, 
      author={Vitaliy Ostashko},
      year={2023},
      url={URL}
}