File size: 1,430 Bytes
6a54784 da7ba36 6a54784 c173ced 6a54784 da7ba36 6a54784 2318908 5b53f71 2318908 5b53f71 2318908 5b53f71 2318908 6a54784 5b53f71 6a54784 c173ced 6a54784 c173ced 6a54784 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
---
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)
``` |