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---
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- hsaglamlar/autotrain-data-stress_v2
co2_eq_emissions: 2.7282806494855265
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1178743973
- CO2 Emissions (in grams): 2.7282806494855265
## Validation Metrics
- Loss: 0.431733638048172
- Accuracy: 0.7976190476190477
- Precision: 0.6918918918918919
- Recall: 0.8205128205128205
- AUC: 0.8952141608391608
- F1: 0.7507331378299119
## Usage
This model finds self-reported stress from txt.
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/hsaglamlar/autotrain-stress_v2-1178743973
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("hsaglamlar/autotrain-stress_v2-1178743973", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("hsaglamlar/autotrain-stress_v2-1178743973", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |