
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 688320769
- CO2 Emissions (in grams): 3.670416179055797

## Validation Metrics

- Loss: 0.3046899139881134
- Accuracy: 0.8826530612244898
- Precision: 0.9181818181818182
- Recall: 0.8782608695652174
- AUC: 0.9423510466988727
- F1: 0.8977777777777778

## 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-text-classification-albert-688320769
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("vlsb/autotrain-security-text-classification-albert-688320769", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("vlsb/autotrain-security-text-classification-albert-688320769", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["vlsb/autotrain-data-security-text-classification-albert"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 3.670416179055797}
transformers pytorch albert text-classification autotrain unk dataset:vlsb/autotrain-data-security-text-classification-albert co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #albert #text-classification #autotrain #unk #dataset-vlsb/autotrain-data-security-text-classification-albert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 688320769
- CO2 Emissions (in grams): 3.670416179055797

## Validation Metrics

- Loss: 0.3046899139881134
- Accuracy: 0.8826530612244898
- Precision: 0.9181818181818182
- Recall: 0.8782608695652174
- AUC: 0.9423510466988727
- F1: 0.8977777777777778

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 688320769
- CO2 Emissions (in grams): 3.670416179055797 ## Validation Metrics

- Loss: 0.3046899139881134
- Accuracy: 0.8826530612244898
- Precision: 0.9181818181818182
- Recall: 0.8782608695652174
- AUC: 0.9423510466988727
- F1: 0.8977777777777778 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 688320769
- CO2 Emissions (in grams): 3.670416179055797 ## Validation Metrics

- Loss: 0.3046899139881134
- Accuracy: 0.8826530612244898
- Precision: 0.9181818181818182
- Recall: 0.8782608695652174
- AUC: 0.9423510466988727
- F1: 0.8977777777777778 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 688320769
- CO2 Emissions (in grams): 3.670416179055797## Validation Metrics

- Loss: 0.3046899139881134
- Accuracy: 0.8826530612244898
- Precision: 0.9181818181818182
- Recall: 0.8782608695652174
- AUC: 0.9423510466988727
- F1: 0.8977777777777778## Usage

You can use cURL to access this model:



Or Python API: