readme: ---
  tags: autotrain
  language: unk
  widget:
  - text: "I love AutoTrain 🤗"
  datasets:
  - Ritvik19/autotrain-data-sentiment_polarity
  co2_eq_emissions: 4.280488237750762
  ---
  
  # Model Trained Using AutoTrain
  
  - Problem type: Binary Classification
  - Model ID: 918130222
  - CO2 Emissions (in grams): 4.280488237750762
  
  ## Validation Metrics
  
  - Loss: 0.13608604669570923
  - Accuracy: 0.9504804036293305
  - Precision: 0.9792047060317863
  - Recall: 0.9647185343057701
  - AUC: 0.9791895292939061
  - F1: 0.9719076444852428
  
  ## 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/Ritvik19/autotrain-sentiment_polarity-918130222
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("Ritvik19/autotrain-sentiment_polarity-918130222", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("Ritvik19/autotrain-sentiment_polarity-918130222", use_auth_token=True)
  
  inputs = tokenizer("I love AutoTrain", return_tensors="pt")
  
  outputs = model(**inputs)
  ```