
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 688020754
- CO2 Emissions (in grams): 3.1151249696839685

## Validation Metrics

- Loss: 0.2810373902320862
- Accuracy: 0.8928571428571429
- Precision: 0.9272727272727272
- Recall: 0.8869565217391304
- AUC: 0.9500805152979066
- F1: 0.9066666666666666

## 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/vlsb/autotrain-security-texts-classification-roberta-688020754
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("vlsb/autotrain-security-texts-classification-roberta-688020754", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("vlsb/autotrain-security-texts-classification-roberta-688020754", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["vlsb/autotrain-data-security-texts-classification-roberta"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 3.1151249696839685}
transformers pytorch roberta text-classification autotrain unk dataset:vlsb/autotrain-data-security-texts-classification-roberta co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-vlsb/autotrain-data-security-texts-classification-roberta #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 688020754
- CO2 Emissions (in grams): 3.1151249696839685

## Validation Metrics

- Loss: 0.2810373902320862
- Accuracy: 0.8928571428571429
- Precision: 0.9272727272727272
- Recall: 0.8869565217391304
- AUC: 0.9500805152979066
- F1: 0.9066666666666666

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 688020754
- CO2 Emissions (in grams): 3.1151249696839685 ## Validation Metrics

- Loss: 0.2810373902320862
- Accuracy: 0.8928571428571429
- Precision: 0.9272727272727272
- Recall: 0.8869565217391304
- AUC: 0.9500805152979066
- F1: 0.9066666666666666 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 688020754
- CO2 Emissions (in grams): 3.1151249696839685 ## Validation Metrics

- Loss: 0.2810373902320862
- Accuracy: 0.8928571428571429
- Precision: 0.9272727272727272
- Recall: 0.8869565217391304
- AUC: 0.9500805152979066
- F1: 0.9066666666666666 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 688020754
- CO2 Emissions (in grams): 3.1151249696839685## Validation Metrics

- Loss: 0.2810373902320862
- Accuracy: 0.8928571428571429
- Precision: 0.9272727272727272
- Recall: 0.8869565217391304
- AUC: 0.9500805152979066
- F1: 0.9066666666666666## Usage

You can use cURL to access this model:



Or Python API: