readme: ---
  tags: autotrain
  language: unk
  widget:
  - text: "I love AutoTrain 🤗"
  datasets:
  - Yarn007/autotrain-data-Napkin
  co2_eq_emissions: 0.020162211418903533
  ---
  
  # Model Trained Using AutoTrain
  
  - Problem type: Multi-class Classification
  - Model ID: 872827783
  - CO2 Emissions (in grams): 0.020162211418903533
  
  ## Validation Metrics
  
  - Loss: 0.25198695063591003
  - Accuracy: 0.9325714285714286
  - Macro F1: 0.9254931094274171
  - Micro F1: 0.9325714285714286
  - Weighted F1: 0.9323540959391766
  - Macro Precision: 0.9286720054236212
  - Micro Precision: 0.9325714285714286
  - Weighted Precision: 0.9324375609546055
  - Macro Recall: 0.9227549386201338
  - Micro Recall: 0.9325714285714286
  - Weighted Recall: 0.9325714285714286
  
  
  ## 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/Yarn007/autotrain-Napkin-872827783
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("Yarn007/autotrain-Napkin-872827783", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("Yarn007/autotrain-Napkin-872827783", use_auth_token=True)
  
  inputs = tokenizer("I love AutoTrain", return_tensors="pt")
  
  outputs = model(**inputs)
  ```