
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 886428460
- CO2 Emissions (in grams): 14.294320632050567

## Validation Metrics

- Loss: 0.051413487643003464
- Accuracy: 0.9894490035169988
- Precision: 1.0
- Recall: 0.9862174578866769
- AUC: 0.9989318529862175
- F1: 0.9930609097918273

## 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/Vmuaddib/autotrain-gudel-department-classifier-clean-886428460
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Vmuaddib/autotrain-gudel-department-classifier-clean-886428460", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Vmuaddib/autotrain-gudel-department-classifier-clean-886428460", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "de", "tags": ["autotrain"], "datasets": ["Vmuaddib/autotrain-data-gudel-department-classifier-clean"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 14.294320632050567}
transformers pytorch electra text-classification autotrain de dataset:Vmuaddib/autotrain-data-gudel-department-classifier-clean co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #electra #text-classification #autotrain #de #dataset-Vmuaddib/autotrain-data-gudel-department-classifier-clean #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 886428460
- CO2 Emissions (in grams): 14.294320632050567

## Validation Metrics

- Loss: 0.051413487643003464
- Accuracy: 0.9894490035169988
- Precision: 1.0
- Recall: 0.9862174578866769
- AUC: 0.9989318529862175
- F1: 0.9930609097918273

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 886428460
- CO2 Emissions (in grams): 14.294320632050567 ## Validation Metrics

- Loss: 0.051413487643003464
- Accuracy: 0.9894490035169988
- Precision: 1.0
- Recall: 0.9862174578866769
- AUC: 0.9989318529862175
- F1: 0.9930609097918273 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #electra #text-classification #autotrain #de #dataset-Vmuaddib/autotrain-data-gudel-department-classifier-clean #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 886428460
- CO2 Emissions (in grams): 14.294320632050567 ## Validation Metrics

- Loss: 0.051413487643003464
- Accuracy: 0.9894490035169988
- Precision: 1.0
- Recall: 0.9862174578866769
- AUC: 0.9989318529862175
- F1: 0.9930609097918273 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #electra #text-classification #autotrain #de #dataset-Vmuaddib/autotrain-data-gudel-department-classifier-clean #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 886428460
- CO2 Emissions (in grams): 14.294320632050567## Validation Metrics

- Loss: 0.051413487643003464
- Accuracy: 0.9894490035169988
- Precision: 1.0
- Recall: 0.9862174578866769
- AUC: 0.9989318529862175
- F1: 0.9930609097918273## Usage

You can use cURL to access this model:



Or Python API: