readme: ---
  tags: autotrain
  language: unk
  widget:
  - text: "ACE2 overexpression in AAV cell lines"
  datasets:
  - Mim/autotrain-data-procell-expert
  co2_eq_emissions: 0.004814823138367317
  ---
  
  # Model Trained Using AutoTrain
  
  - Problem type: Binary Classification
  - Model ID: 800724769
  - CO2 Emissions (in grams): 0.004814823138367317
  
  ## Validation Metrics
  
  - Loss: 0.4749071002006531
  - Accuracy: 0.9
  - Precision: 0.8928571428571429
  - Recall: 0.9615384615384616
  - AUC: 0.9065934065934066
  - F1: 0.9259259259259259
  
  ## 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/Mim/autotrain-procell-expert-800724769
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("Mim/autotrain-procell-expert-800724769", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("Mim/autotrain-procell-expert-800724769", use_auth_token=True)
  
  inputs = tokenizer("I love AutoTrain", return_tensors="pt")
  
  outputs = model(**inputs)
  ```