README.md

metadata

tags:
  - autotrain
  - text-classification
language:
  - ar
widget:
  - text: مطر غزير على شوارع العاصمة المقدسة.
datasets:
  - MMars/autotrain-data-camelbert-mix_flodusta
co2_eq_emissions:
  emissions: 0.010214592292905006
metrics:
  - accuracy
  - f1
  - precision
  - recall

Labels Mapping

0 non event

1 flood

2 dust storm

3 traffic accident

Model Trained Using AutoTrain

• Problem type: Multi-class Classification
• Model ID: 2783082152
• CO2 Emissions (in grams): 0.0102

Validation Metrics

• Loss: 0.149
• Accuracy: 0.949
• Macro F1: 0.946
• Micro F1: 0.949
• Weighted F1: 0.949
• Macro Precision: 0.942
• Micro Precision: 0.949
• Weighted Precision: 0.950
• Macro Recall: 0.951
• Micro Recall: 0.949
• Weighted Recall: 0.949

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-camelbert-mix_flodusta-2783082152

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("MMars/camelbert-mix_flodusta", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("MMars/camelbert-mix_flodusta", use_auth_token=True)

inputs = tokenizer("I love AutoTrain", return_tensors="pt")

outputs = model(**inputs)