
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3527295358
- CO2 Emissions (in grams): 3.5347

## Validation Metrics

- Loss: 1.428
- Accuracy: 0.572
- Macro F1: 0.209
- Micro F1: 0.572
- Weighted F1: 0.513
- Macro Precision: 0.206
- Micro Precision: 0.572
- Weighted Precision: 0.469
- Macro Recall: 0.220
- Micro Recall: 0.572
- Weighted Recall: 0.572


## 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/fathyshalab/autotrain-reklambox-3527295358
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("fathyshalab/autotrain-reklambox-3527295358", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("fathyshalab/autotrain-reklambox-3527295358", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["de"], "tags": ["autotrain", "text-classification"], "datasets": ["fathyshalab/autotrain-data-reklambox"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 3.5346662598120697}}
transformers pytorch roberta text-classification autotrain de dataset:fathyshalab/autotrain-data-reklambox co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #de #dataset-fathyshalab/autotrain-data-reklambox #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3527295358
- CO2 Emissions (in grams): 3.5347

## Validation Metrics

- Loss: 1.428
- Accuracy: 0.572
- Macro F1: 0.209
- Micro F1: 0.572
- Weighted F1: 0.513
- Macro Precision: 0.206
- Micro Precision: 0.572
- Weighted Precision: 0.469
- Macro Recall: 0.220
- Micro Recall: 0.572
- Weighted Recall: 0.572


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3527295358
- CO2 Emissions (in grams): 3.5347 ## Validation Metrics

- Loss: 1.428
- Accuracy: 0.572
- Macro F1: 0.209
- Micro F1: 0.572
- Weighted F1: 0.513
- Macro Precision: 0.206
- Micro Precision: 0.572
- Weighted Precision: 0.469
- Macro Recall: 0.220
- Micro Recall: 0.572
- Weighted Recall: 0.572 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #de #dataset-fathyshalab/autotrain-data-reklambox #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3527295358
- CO2 Emissions (in grams): 3.5347 ## Validation Metrics

- Loss: 1.428
- Accuracy: 0.572
- Macro F1: 0.209
- Micro F1: 0.572
- Weighted F1: 0.513
- Macro Precision: 0.206
- Micro Precision: 0.572
- Weighted Precision: 0.469
- Macro Recall: 0.220
- Micro Recall: 0.572
- Weighted Recall: 0.572 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #de #dataset-fathyshalab/autotrain-data-reklambox #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3527295358
- CO2 Emissions (in grams): 3.5347## Validation Metrics

- Loss: 1.428
- Accuracy: 0.572
- Macro F1: 0.209
- Micro F1: 0.572
- Weighted F1: 0.513
- Macro Precision: 0.206
- Micro Precision: 0.572
- Weighted Precision: 0.469
- Macro Recall: 0.220
- Micro Recall: 0.572
- Weighted Recall: 0.572## Usage

You can use cURL to access this model:



Or Python API: