---
tags:
- autotrain
- text-classification
- cognitive distortions
- psychology
- depression
language:
- unk
widget:
- text: I love AutoTrain
datasets:
- halilbabacan/autotrain-data-cognitive_distortions
co2_eq_emissions:
  emissions: 0.8368333755010434
---
The article is under publication. For communication, you can send an e-mail to hakki.babacan@erzincan.edu.tr.

# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 73482139269
- CO2 Emissions (in grams): 0.8368

## Validation Metrics

- Loss: 0.076
- Accuracy: 0.973
- Precision: 0.912
- Recall: 0.995
- AUC: 0.997
- F1: 0.951

## Usage

You can use cURL to access this model:

```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/halilbabacan/autotrain-cognitive_distortions-73482139269
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("halilbabacan/autotrain-cognitive_distortions-73482139269", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("halilbabacan/autotrain-cognitive_distortions-73482139269", use_auth_token=True)

inputs = tokenizer("I love AutoTrain", return_tensors="pt")

outputs = model(**inputs)
```