readme: ---
  tags: biobert
  language: unk
  widget:
  - text: "Cell lines expressing proteins 🤗"
  datasets:
  - Mim/autotrain-data-biobert-procell
  co2_eq_emissions: 0.5988414315305852
  ---
  
  # Model Trained Using biobert
  
  - Problem type: Binary Classification
  - Model ID: 896229149
  - CO2 Emissions (in grams): 0.5988414315305852
  
  ## Validation Metrics
  
  - Loss: 0.4045306444168091
  - Accuracy: 0.8028169014084507
  - Precision: 0.8070175438596491
  - Recall: 0.9387755102040817
  - AUC: 0.8812615955473099
  - F1: 0.8679245283018868
  
  ## 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": "Cell lines expressing proteins"}' https://api-inference.huggingface.co/models/Mim/autotrain-biobert-procell-896229149
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("Mim/autotrain-biobert-procell-896229149", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("Mim/autotrain-biobert-procell-896229149", use_auth_token=True)
  
  inputs = tokenizer("Cell lines expressing proteins", return_tensors="pt")
  
  outputs = model(**inputs)
  ```