
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 38671101799
- CO2 Emissions (in grams): 0.4167

## Validation Metrics

- Loss: 1.484
- Accuracy: 0.593
- Macro F1: 0.136
- Micro F1: 0.593
- Weighted F1: 0.534
- Macro Precision: 0.125
- Micro Precision: 0.593
- Weighted Precision: 0.487
- Macro Recall: 0.150
- Micro Recall: 0.593
- Weighted Recall: 0.593


## 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-reklambox2-38671101799
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("fathyshalab/autotrain-reklambox2-38671101799", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("fathyshalab/autotrain-reklambox2-38671101799", 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-reklambox2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.41672710603977325}}
transformers pytorch bert text-classification autotrain de dataset:fathyshalab/autotrain-data-reklambox2 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #de #dataset-fathyshalab/autotrain-data-reklambox2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 38671101799
- CO2 Emissions (in grams): 0.4167

## Validation Metrics

- Loss: 1.484
- Accuracy: 0.593
- Macro F1: 0.136
- Micro F1: 0.593
- Weighted F1: 0.534
- Macro Precision: 0.125
- Micro Precision: 0.593
- Weighted Precision: 0.487
- Macro Recall: 0.150
- Micro Recall: 0.593
- Weighted Recall: 0.593


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 38671101799
- CO2 Emissions (in grams): 0.4167 ## Validation Metrics

- Loss: 1.484
- Accuracy: 0.593
- Macro F1: 0.136
- Micro F1: 0.593
- Weighted F1: 0.534
- Macro Precision: 0.125
- Micro Precision: 0.593
- Weighted Precision: 0.487
- Macro Recall: 0.150
- Micro Recall: 0.593
- Weighted Recall: 0.593 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #de #dataset-fathyshalab/autotrain-data-reklambox2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 38671101799
- CO2 Emissions (in grams): 0.4167 ## Validation Metrics

- Loss: 1.484
- Accuracy: 0.593
- Macro F1: 0.136
- Micro F1: 0.593
- Weighted F1: 0.534
- Macro Precision: 0.125
- Micro Precision: 0.593
- Weighted Precision: 0.487
- Macro Recall: 0.150
- Micro Recall: 0.593
- Weighted Recall: 0.593 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #de #dataset-fathyshalab/autotrain-data-reklambox2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 38671101799
- CO2 Emissions (in grams): 0.4167## Validation Metrics

- Loss: 1.484
- Accuracy: 0.593
- Macro F1: 0.136
- Micro F1: 0.593
- Weighted F1: 0.534
- Macro Precision: 0.125
- Micro Precision: 0.593
- Weighted Precision: 0.487
- Macro Recall: 0.150
- Micro Recall: 0.593
- Weighted Recall: 0.593## Usage

You can use cURL to access this model:



Or Python API: