
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3631097041
- CO2 Emissions (in grams): 6.2190

## Validation Metrics

- Loss: 1.549
- Accuracy: 0.563
- Macro F1: 0.175
- Micro F1: 0.563
- Weighted F1: 0.498
- Macro Precision: 0.179
- Micro Precision: 0.563
- Weighted Precision: 0.456
- Macro Recall: 0.181
- Micro Recall: 0.563
- Weighted Recall: 0.563


## 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-reklam-filtered-3631097041
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("fathyshalab/autotrain-reklam-filtered-3631097041", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("fathyshalab/autotrain-reklam-filtered-3631097041", 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-reklam-filtered"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 6.219004242367904}}
transformers pytorch electra text-classification autotrain de dataset:fathyshalab/autotrain-data-reklam-filtered co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #electra #text-classification #autotrain #de #dataset-fathyshalab/autotrain-data-reklam-filtered #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3631097041
- CO2 Emissions (in grams): 6.2190

## Validation Metrics

- Loss: 1.549
- Accuracy: 0.563
- Macro F1: 0.175
- Micro F1: 0.563
- Weighted F1: 0.498
- Macro Precision: 0.179
- Micro Precision: 0.563
- Weighted Precision: 0.456
- Macro Recall: 0.181
- Micro Recall: 0.563
- Weighted Recall: 0.563


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3631097041
- CO2 Emissions (in grams): 6.2190 ## Validation Metrics

- Loss: 1.549
- Accuracy: 0.563
- Macro F1: 0.175
- Micro F1: 0.563
- Weighted F1: 0.498
- Macro Precision: 0.179
- Micro Precision: 0.563
- Weighted Precision: 0.456
- Macro Recall: 0.181
- Micro Recall: 0.563
- Weighted Recall: 0.563 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 3631097041
- CO2 Emissions (in grams): 6.2190 ## Validation Metrics

- Loss: 1.549
- Accuracy: 0.563
- Macro F1: 0.175
- Micro F1: 0.563
- Weighted F1: 0.498
- Macro Precision: 0.179
- Micro Precision: 0.563
- Weighted Precision: 0.456
- Macro Recall: 0.181
- Micro Recall: 0.563
- Weighted Recall: 0.563 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 3631097041
- CO2 Emissions (in grams): 6.2190## Validation Metrics

- Loss: 1.549
- Accuracy: 0.563
- Macro F1: 0.175
- Micro F1: 0.563
- Weighted F1: 0.498
- Macro Precision: 0.179
- Micro Precision: 0.563
- Weighted Precision: 0.456
- Macro Recall: 0.181
- Micro Recall: 0.563
- Weighted Recall: 0.563## Usage

You can use cURL to access this model:



Or Python API: