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---
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)
``` |