
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 879428192
- CO2 Emissions (in grams): 13.170344687762716

## Validation Metrics

- Loss: 0.06465228646993637
- Accuracy: 0.9796652588768966
- Precision: 0.9843385538153949
- Recall: 0.993943472409152
- AUC: 0.9855992605071237
- F1: 0.9891176963000168

## 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/nthanhha26/autotrain-test-project-879428192
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("nthanhha26/autotrain-test-project-879428192", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("nthanhha26/autotrain-test-project-879428192", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["nthanhha26/autotrain-data-test-project"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 13.170344687762716}
transformers pytorch roberta text-classification autotrain unk dataset:nthanhha26/autotrain-data-test-project co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-nthanhha26/autotrain-data-test-project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 879428192
- CO2 Emissions (in grams): 13.170344687762716

## Validation Metrics

- Loss: 0.06465228646993637
- Accuracy: 0.9796652588768966
- Precision: 0.9843385538153949
- Recall: 0.993943472409152
- AUC: 0.9855992605071237
- F1: 0.9891176963000168

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 879428192
- CO2 Emissions (in grams): 13.170344687762716 ## Validation Metrics

- Loss: 0.06465228646993637
- Accuracy: 0.9796652588768966
- Precision: 0.9843385538153949
- Recall: 0.993943472409152
- AUC: 0.9855992605071237
- F1: 0.9891176963000168 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-nthanhha26/autotrain-data-test-project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 879428192
- CO2 Emissions (in grams): 13.170344687762716 ## Validation Metrics

- Loss: 0.06465228646993637
- Accuracy: 0.9796652588768966
- Precision: 0.9843385538153949
- Recall: 0.993943472409152
- AUC: 0.9855992605071237
- F1: 0.9891176963000168 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-nthanhha26/autotrain-data-test-project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 879428192
- CO2 Emissions (in grams): 13.170344687762716## Validation Metrics

- Loss: 0.06465228646993637
- Accuracy: 0.9796652588768966
- Precision: 0.9843385538153949
- Recall: 0.993943472409152
- AUC: 0.9855992605071237
- F1: 0.9891176963000168## Usage

You can use cURL to access this model:



Or Python API: