readme: ---
  tags:
  - autotrain
  - text-classification
  language:
  - ar
  widget:
  - text: مطر غزير على شوارع العاصمة المقدسة.
  datasets:
  - MMars/autotrain-data-marbertv2_flodusta
  co2_eq_emissions:
    emissions: 0.026171515160105633
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  ---
  # Labels Mapping
  0 non event 
  
  1 flood 
  
  2 dust storm 
  
  3 traffic accident
  
  
  # Model Trained Using AutoTrain
  
  - Problem type: Multi-class Classification
  - Model ID: 2782682135
  - CO2 Emissions (in grams): 0.0262
  
  ## Validation Metrics
  
  - Loss: 0.157
  - Accuracy: 0.948
  - Macro F1: 0.945
  - Micro F1: 0.948
  - Weighted F1: 0.948
  - Macro Precision: 0.940
  - Micro Precision: 0.948
  - Weighted Precision: 0.949
  - Macro Recall: 0.952
  - Micro Recall: 0.948
  - Weighted Recall: 0.948
  
  
  ## 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/MMars/autotrain-marbertv2_flodusta-2782682135
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("MMars/marbertv2_flodusta", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("MMars/marbertv2_flodusta", use_auth_token=True)
  
  inputs = tokenizer("I love AutoTrain", return_tensors="pt")
  
  outputs = model(**inputs)
  ```