README.md

metadata

tags:
  - autotrain
  - text-classification
  - cognitive distortion
  - psychology
language:
  - unk
widget:
  - text: I love AutoTrain
datasets:
  - halilbabacan/autotrain-data-cognitive_distortion_gpt_roberta
co2_eq_emissions:
  emissions: 1.5120249278420834

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: 73173139143
• CO2 Emissions (in grams): 1.5120

Validation Metrics

• Loss: 0.000
• Accuracy: 1.000
• Precision: 1.000
• Recall: 1.000
• AUC: 1.000
• F1: 1.000

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_distortion_gpt_roberta-73173139143

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("halilbabacan/autotrain-cognitive_distortion_gpt_roberta-73173139143", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("halilbabacan/autotrain-cognitive_distortion_gpt_roberta-73173139143", use_auth_token=True)

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

outputs = model(**inputs)