
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 980432459
- CO2 Emissions (in grams): 0.012147398577917884

## Validation Metrics

- Loss: 0.0469294898211956
- Accuracy: 0.9917355371900827
- Precision: 0.9936708860759493
- Recall: 0.9936708860759493
- AUC: 0.9990958408679927
- F1: 0.9936708860759493

## 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/Jerimee/autotrain-dontknowwhatImdoing-980432459
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Jerimee/autotrain-dontknowwhatImdoing-980432459", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Jerimee/autotrain-dontknowwhatImdoing-980432459", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["Jerimee/autotrain-data-dontknowwhatImdoing"], "widget": [{"text": "Jerimee", "example_title": "a weird human name"}, {"text": "Curtastica", "example_title": "a goblin name"}, {"text": "Fatima", "example_title": "a common human name"}], "co2_eq_emissions": 0.012147398577917884}
transformers pytorch bert text-classification autotrain en dataset:Jerimee/autotrain-data-dontknowwhatImdoing co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-Jerimee/autotrain-data-dontknowwhatImdoing #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 980432459
- CO2 Emissions (in grams): 0.012147398577917884

## Validation Metrics

- Loss: 0.0469294898211956
- Accuracy: 0.9917355371900827
- Precision: 0.9936708860759493
- Recall: 0.9936708860759493
- AUC: 0.9990958408679927
- F1: 0.9936708860759493

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 980432459
- CO2 Emissions (in grams): 0.012147398577917884 ## Validation Metrics

- Loss: 0.0469294898211956
- Accuracy: 0.9917355371900827
- Precision: 0.9936708860759493
- Recall: 0.9936708860759493
- AUC: 0.9990958408679927
- F1: 0.9936708860759493 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-Jerimee/autotrain-data-dontknowwhatImdoing #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 980432459
- CO2 Emissions (in grams): 0.012147398577917884 ## Validation Metrics

- Loss: 0.0469294898211956
- Accuracy: 0.9917355371900827
- Precision: 0.9936708860759493
- Recall: 0.9936708860759493
- AUC: 0.9990958408679927
- F1: 0.9936708860759493 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-Jerimee/autotrain-data-dontknowwhatImdoing #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 980432459
- CO2 Emissions (in grams): 0.012147398577917884## Validation Metrics

- Loss: 0.0469294898211956
- Accuracy: 0.9917355371900827
- Precision: 0.9936708860759493
- Recall: 0.9936708860759493
- AUC: 0.9990958408679927
- F1: 0.9936708860759493## Usage

You can use cURL to access this model:



Or Python API: