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--- |
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tags: |
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- autotrain |
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- text-classification |
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language: |
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- ar |
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widget: |
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- text: مطر غزير على شوارع العاصمة المقدسة. |
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datasets: |
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- MMars/autotrain-data-camelbert-mix_flodusta |
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co2_eq_emissions: |
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emissions: 0.010214592292905006 |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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--- |
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# Labels Mapping |
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0 non event |
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1 flood |
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2 dust storm |
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3 traffic accident |
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# Model Trained Using AutoTrain |
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- Problem type: Multi-class Classification |
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- Model ID: 2783082152 |
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- CO2 Emissions (in grams): 0.0102 |
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## Validation Metrics |
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- Loss: 0.149 |
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- Accuracy: 0.949 |
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- Macro F1: 0.946 |
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- Micro F1: 0.949 |
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- Weighted F1: 0.949 |
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- Macro Precision: 0.942 |
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- Micro Precision: 0.949 |
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- Weighted Precision: 0.950 |
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- Macro Recall: 0.951 |
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- Micro Recall: 0.949 |
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- Weighted Recall: 0.949 |
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## Usage |
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You can use cURL to access this model: |
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``` |
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$ 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 |
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``` |
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Or Python API: |
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``` |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("MMars/camelbert-mix_flodusta", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("MMars/camelbert-mix_flodusta", use_auth_token=True) |
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inputs = tokenizer("I love AutoTrain", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
