README.md

metadata

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)