
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 23995359
- CO2 Emissions (in grams): 0.6503024714880831

## Validation Metrics

- Loss: 0.49598395824432373
- Accuracy: 0.7907801418439716
- Precision: 0.7841726618705036
- Recall: 0.7898550724637681
- AUC: 0.8774154589371981
- F1: 0.7870036101083032

## 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/stop_the_steal_relevancy_analysis-binary
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("sefaozalpadl/stop_the_steal_relevancy_analysis-binary", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("sefaozalpadl/stop_the_steal_relevancy_analysis-binary", use_auth_token=True)

inputs = tokenizer("take our country back. Stop the steal! #trump2020", return_tensors="pt")

outputs = model(**inputs)
```
{"language": "en", "tags": "coe", "datasets": ["sefaozalpadl/autonlp-data-stop_the_steal_relevancy_analysis"], "widget": [{"text": "take our country back. Stop the steal! #trump2020"}], "co2_eq_emissions": 0.6503024714880831}
transformers pytorch roberta text-classification coe en dataset:sefaozalpadl/autonlp-data-stop_the_steal_relevancy_analysis co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #coe #en #dataset-sefaozalpadl/autonlp-data-stop_the_steal_relevancy_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 23995359
- CO2 Emissions (in grams): 0.6503024714880831

## Validation Metrics

- Loss: 0.49598395824432373
- Accuracy: 0.7907801418439716
- Precision: 0.7841726618705036
- Recall: 0.7898550724637681
- AUC: 0.8774154589371981
- F1: 0.7870036101083032

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 23995359
- CO2 Emissions (in grams): 0.6503024714880831 ## Validation Metrics

- Loss: 0.49598395824432373
- Accuracy: 0.7907801418439716
- Precision: 0.7841726618705036
- Recall: 0.7898550724637681
- AUC: 0.8774154589371981
- F1: 0.7870036101083032 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #coe #en #dataset-sefaozalpadl/autonlp-data-stop_the_steal_relevancy_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 23995359
- CO2 Emissions (in grams): 0.6503024714880831 ## Validation Metrics

- Loss: 0.49598395824432373
- Accuracy: 0.7907801418439716
- Precision: 0.7841726618705036
- Recall: 0.7898550724637681
- AUC: 0.8774154589371981
- F1: 0.7870036101083032 ## 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-stop_the_steal_relevancy_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 23995359
- CO2 Emissions (in grams): 0.6503024714880831## Validation Metrics

- Loss: 0.49598395824432373
- Accuracy: 0.7907801418439716
- Precision: 0.7841726618705036
- Recall: 0.7898550724637681
- AUC: 0.8774154589371981
- F1: 0.7870036101083032## Usage

You can use cURL to access this model:



Or Python API: