
# Election Fraud Binary Classifier

- Problem type: Binary Classification
- Model ID: 23315155
- CO2 Emissions (in grams): 1.3248523193990855

## Validation Metrics

- Loss: 0.4240806996822357
- Accuracy: 0.8173913043478261
- Precision: 0.8837209302325582
- Recall: 0.8085106382978723
- AUC: 0.8882580285281696
- F1: 0.8444444444444444

## 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 AutoNLP"}' https://api-inference.huggingface.co/models/sefaozalpadl/election_relevancy_best
```

Or Python API:

```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
  
tokenizer = AutoTokenizer.from_pretrained("sefaozalpadl/election_relevancy_best")

model = AutoModelForSequenceClassification.from_pretrained("sefaozalpadl/election_relevancy_best")

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

outputs = model(**inputs)
```
{"language": "en", "tags": "coe", "datasets": ["sefaozalpadl/autonlp-data-election_relevancy_analysis"], "widget": [{"text": "@PressSec Response to Putin is laughable. He has Biden's number. He knows Biden can't hold up in a live debate, and the Chinese did a number on the U.S. too. Biden is making US the laughing stock of the world. We pay the price for a stolen election"}], "co2_eq_emissions": 1.3248523193990855}
transformers pytorch roberta text-classification coe en dataset:sefaozalpadl/autonlp-data-election_relevancy_analysis co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #coe #en #dataset-sefaozalpadl/autonlp-data-election_relevancy_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Election Fraud Binary Classifier

- Problem type: Binary Classification
- Model ID: 23315155
- CO2 Emissions (in grams): 1.3248523193990855

## Validation Metrics

- Loss: 0.4240806996822357
- Accuracy: 0.8173913043478261
- Precision: 0.8837209302325582
- Recall: 0.8085106382978723
- AUC: 0.8882580285281696
- F1: 0.8444444444444444

## Usage

You can use cURL to access this model:



Or Python API:


# Election Fraud Binary Classifier

- Problem type: Binary Classification
- Model ID: 23315155
- CO2 Emissions (in grams): 1.3248523193990855 ## Validation Metrics

- Loss: 0.4240806996822357
- Accuracy: 0.8173913043478261
- Precision: 0.8837209302325582
- Recall: 0.8085106382978723
- AUC: 0.8882580285281696
- F1: 0.8444444444444444 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #coe #en #dataset-sefaozalpadl/autonlp-data-election_relevancy_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Election Fraud Binary Classifier

- Problem type: Binary Classification
- Model ID: 23315155
- CO2 Emissions (in grams): 1.3248523193990855 ## Validation Metrics

- Loss: 0.4240806996822357
- Accuracy: 0.8173913043478261
- Precision: 0.8837209302325582
- Recall: 0.8085106382978723
- AUC: 0.8882580285281696
- F1: 0.8444444444444444 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #coe #en #dataset-sefaozalpadl/autonlp-data-election_relevancy_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Election Fraud Binary Classifier

- Problem type: Binary Classification
- Model ID: 23315155
- CO2 Emissions (in grams): 1.3248523193990855## Validation Metrics

- Loss: 0.4240806996822357
- Accuracy: 0.8173913043478261
- Precision: 0.8837209302325582
- Recall: 0.8085106382978723
- AUC: 0.8882580285281696
- F1: 0.8444444444444444## Usage

You can use cURL to access this model:



Or Python API: