readme: ---
  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)
  ```