# Labels Mapping
0 non event 

1 flood 

2 dust storm 

3 traffic accident


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 2782582128
- CO2 Emissions (in grams): 3.6263

## Validation Metrics

- Loss: 0.144
- Accuracy: 0.953
- Macro F1: 0.951
- Micro F1: 0.953
- Weighted F1: 0.953
- Macro Precision: 0.951
- Micro Precision: 0.953
- Weighted Precision: 0.953
- Macro Recall: 0.952
- Micro Recall: 0.953
- Weighted Recall: 0.953


## 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-arabertv2_flodusta-2782582128
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("MMars/arabertv2_flodusta", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("MMars/arabertv2_flodusta", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["MMars/autotrain-data-arabertv2_flodusta"], "widget": [{"text": "\u0645\u0637\u0631 \u063a\u0632\u064a\u0631 \u0639\u0644\u0649 \u0634\u0648\u0627\u0631\u0639 \u0627\u0644\u0639\u0627\u0635\u0645\u0629 \u0627\u0644\u0645\u0642\u062f\u0633\u0629."}], "co2_eq_emissions": {"emissions": 3.6263155149619304}}
transformers pytorch bert text-classification autotrain unk dataset:MMars/autotrain-data-arabertv2_flodusta co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-MMars/autotrain-data-arabertv2_flodusta #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 

# Labels Mapping
0 non event 

1 flood 

2 dust storm 

3 traffic accident


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 2782582128
- CO2 Emissions (in grams): 3.6263

## Validation Metrics

- Loss: 0.144
- Accuracy: 0.953
- Macro F1: 0.951
- Micro F1: 0.953
- Weighted F1: 0.953
- Macro Precision: 0.951
- Micro Precision: 0.953
- Weighted Precision: 0.953
- Macro Recall: 0.952
- Micro Recall: 0.953
- Weighted Recall: 0.953


## Usage

You can use cURL to access this model:



Or Python API:


# Labels Mapping
0 non event 

1 flood 

2 dust storm 

3 traffic accident # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 2782582128
- CO2 Emissions (in grams): 3.6263 ## Validation Metrics

- Loss: 0.144
- Accuracy: 0.953
- Macro F1: 0.951
- Micro F1: 0.953
- Weighted F1: 0.953
- Macro Precision: 0.951
- Micro Precision: 0.953
- Weighted Precision: 0.953
- Macro Recall: 0.952
- Micro Recall: 0.953
- Weighted Recall: 0.953 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-MMars/autotrain-data-arabertv2_flodusta #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Labels Mapping
0 non event 

1 flood 

2 dust storm 

3 traffic accident # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 2782582128
- CO2 Emissions (in grams): 3.6263 ## Validation Metrics

- Loss: 0.144
- Accuracy: 0.953
- Macro F1: 0.951
- Micro F1: 0.953
- Weighted F1: 0.953
- Macro Precision: 0.951
- Micro Precision: 0.953
- Weighted Precision: 0.953
- Macro Recall: 0.952
- Micro Recall: 0.953
- Weighted Recall: 0.953 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-MMars/autotrain-data-arabertv2_flodusta #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Labels Mapping
0 non event 

1 flood 

2 dust storm 

3 traffic accident# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 2782582128
- CO2 Emissions (in grams): 3.6263## Validation Metrics

- Loss: 0.144
- Accuracy: 0.953
- Macro F1: 0.951
- Micro F1: 0.953
- Weighted F1: 0.953
- Macro Precision: 0.951
- Micro Precision: 0.953
- Weighted Precision: 0.953
- Macro Recall: 0.952
- Micro Recall: 0.953
- Weighted Recall: 0.953## Usage

You can use cURL to access this model:



Or Python API: