
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 37433101195
- CO2 Emissions (in grams): 0.0125

## Validation Metrics

- Loss: 0.204
- Accuracy: 0.941
- Precision: 0.975
- Recall: 0.905
- AUC: 0.992
- F1: 0.939

## 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/Sarwar242/autotrain-fake-reviews-labelling-37433101195
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Sarwar242/autotrain-fake-reviews-labelling-37433101195", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Sarwar242/autotrain-fake-reviews-labelling-37433101195", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["Sarwar242/autotrain-data-fake-reviews-labelling"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.012510004345691475}}
transformers pytorch deberta-v2 text-classification autotrain en dataset:Sarwar242/autotrain-data-fake-reviews-labelling co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #en #dataset-Sarwar242/autotrain-data-fake-reviews-labelling #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 37433101195
- CO2 Emissions (in grams): 0.0125

## Validation Metrics

- Loss: 0.204
- Accuracy: 0.941
- Precision: 0.975
- Recall: 0.905
- AUC: 0.992
- F1: 0.939

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 37433101195
- CO2 Emissions (in grams): 0.0125 ## Validation Metrics

- Loss: 0.204
- Accuracy: 0.941
- Precision: 0.975
- Recall: 0.905
- AUC: 0.992
- F1: 0.939 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #en #dataset-Sarwar242/autotrain-data-fake-reviews-labelling #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 37433101195
- CO2 Emissions (in grams): 0.0125 ## Validation Metrics

- Loss: 0.204
- Accuracy: 0.941
- Precision: 0.975
- Recall: 0.905
- AUC: 0.992
- F1: 0.939 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #en #dataset-Sarwar242/autotrain-data-fake-reviews-labelling #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 37433101195
- CO2 Emissions (in grams): 0.0125## Validation Metrics

- Loss: 0.204
- Accuracy: 0.941
- Precision: 0.975
- Recall: 0.905
- AUC: 0.992
- F1: 0.939## Usage

You can use cURL to access this model:



Or Python API: