This model is used to determine if a domain name is malicious or not.
It should be used only as a suggestion as it does have some false positives, especialy on domains
from other countries.









---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Cubicz/autotrain-data-domain-filer-2
co2_eq_emissions:
  emissions: 2.854698438703003
---

# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3436193686
- CO2 Emissions (in grams): 2.8547

## Validation Metrics

- Loss: 0.124
- Accuracy: 0.967
- Precision: 0.963
- Recall: 0.963
- AUC: 0.991
- F1: 0.963

## 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/Cubicz/autotrain-domain-filer-2-3436193686
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Cubicz/autotrain-domain-filer-2-3436193686", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Cubicz/autotrain-domain-filer-2-3436193686", use_auth_token=True)

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

outputs = model(**inputs)
```
{}
transformers pytorch deberta-v2 text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain_compatible #endpoints_compatible #region-us 

This model is used to determine if a domain name is malicious or not.
It should be used only as a suggestion as it does have some false positives, especialy on domains
from other countries.









---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain "
datasets:
- Cubicz/autotrain-data-domain-filer-2
co2_eq_emissions:
  emissions: 2.854698438703003
---

# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3436193686
- CO2 Emissions (in grams): 2.8547

## Validation Metrics

- Loss: 0.124
- Accuracy: 0.967
- Precision: 0.963
- Recall: 0.963
- AUC: 0.991
- F1: 0.963

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3436193686
- CO2 Emissions (in grams): 2.8547 ## Validation Metrics

- Loss: 0.124
- Accuracy: 0.967
- Precision: 0.963
- Recall: 0.963
- AUC: 0.991
- F1: 0.963 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3436193686
- CO2 Emissions (in grams): 2.8547 ## Validation Metrics

- Loss: 0.124
- Accuracy: 0.967
- Precision: 0.963
- Recall: 0.963
- AUC: 0.991
- F1: 0.963 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3436193686
- CO2 Emissions (in grams): 2.8547## Validation Metrics

- Loss: 0.124
- Accuracy: 0.967
- Precision: 0.963
- Recall: 0.963
- AUC: 0.991
- F1: 0.963## Usage

You can use cURL to access this model:



Or Python API: