
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 813825547
- CO2 Emissions (in grams): 0.056186258092819436

## Validation Metrics

- Loss: 0.15057753026485443
- Accuracy: 0.9738805970149254
- Precision: 0.9469026548672567
- Recall: 0.9304347826086956
- AUC: 0.9891149437157905
- F1: 0.9385964912280702

## 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/tristantristantristan/autotrain-rumour_detection-813825547
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("tristantristantristan/autotrain-rumour_detection-813825547", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("tristantristantristan/autotrain-rumour_detection-813825547", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["tristantristantristan/autotrain-data-rumour_detection"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.056186258092819436}
transformers pytorch bert text-classification autotrain en dataset:tristantristantristan/autotrain-data-rumour_detection co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-tristantristantristan/autotrain-data-rumour_detection #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 813825547
- CO2 Emissions (in grams): 0.056186258092819436

## Validation Metrics

- Loss: 0.15057753026485443
- Accuracy: 0.9738805970149254
- Precision: 0.9469026548672567
- Recall: 0.9304347826086956
- AUC: 0.9891149437157905
- F1: 0.9385964912280702

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 813825547
- CO2 Emissions (in grams): 0.056186258092819436 ## Validation Metrics

- Loss: 0.15057753026485443
- Accuracy: 0.9738805970149254
- Precision: 0.9469026548672567
- Recall: 0.9304347826086956
- AUC: 0.9891149437157905
- F1: 0.9385964912280702 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 813825547
- CO2 Emissions (in grams): 0.056186258092819436 ## Validation Metrics

- Loss: 0.15057753026485443
- Accuracy: 0.9738805970149254
- Precision: 0.9469026548672567
- Recall: 0.9304347826086956
- AUC: 0.9891149437157905
- F1: 0.9385964912280702 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 813825547
- CO2 Emissions (in grams): 0.056186258092819436## Validation Metrics

- Loss: 0.15057753026485443
- Accuracy: 0.9738805970149254
- Precision: 0.9469026548672567
- Recall: 0.9304347826086956
- AUC: 0.9891149437157905
- F1: 0.9385964912280702## Usage

You can use cURL to access this model:



Or Python API: