
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 467612250
- CO2 Emissions (in grams): 73.72876780772296

## Validation Metrics

- Loss: 0.18261319398880005
- Accuracy: 0.9541659567217584
- Precision: 0.9530625832223701
- Recall: 0.9572049481778669
- AUC: 0.9901737875196123
- F1: 0.9551292743953294

## 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/rexxar96/autonlp-roberta-large-finetuned-467612250
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("rexxar96/autonlp-roberta-large-finetuned-467612250", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("rexxar96/autonlp-roberta-large-finetuned-467612250", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autonlp", "datasets": ["rexxar96/autonlp-data-roberta-large-finetuned"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 73.72876780772296}
transformers pytorch roberta text-classification autonlp unk dataset:rexxar96/autonlp-data-roberta-large-finetuned co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-rexxar96/autonlp-data-roberta-large-finetuned #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 467612250
- CO2 Emissions (in grams): 73.72876780772296

## Validation Metrics

- Loss: 0.18261319398880005
- Accuracy: 0.9541659567217584
- Precision: 0.9530625832223701
- Recall: 0.9572049481778669
- AUC: 0.9901737875196123
- F1: 0.9551292743953294

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 467612250
- CO2 Emissions (in grams): 73.72876780772296 ## Validation Metrics

- Loss: 0.18261319398880005
- Accuracy: 0.9541659567217584
- Precision: 0.9530625832223701
- Recall: 0.9572049481778669
- AUC: 0.9901737875196123
- F1: 0.9551292743953294 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-rexxar96/autonlp-data-roberta-large-finetuned #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 467612250
- CO2 Emissions (in grams): 73.72876780772296 ## Validation Metrics

- Loss: 0.18261319398880005
- Accuracy: 0.9541659567217584
- Precision: 0.9530625832223701
- Recall: 0.9572049481778669
- AUC: 0.9901737875196123
- F1: 0.9551292743953294 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-rexxar96/autonlp-data-roberta-large-finetuned #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 467612250
- CO2 Emissions (in grams): 73.72876780772296## Validation Metrics

- Loss: 0.18261319398880005
- Accuracy: 0.9541659567217584
- Precision: 0.9530625832223701
- Recall: 0.9572049481778669
- AUC: 0.9901737875196123
- F1: 0.9551292743953294## Usage

You can use cURL to access this model:



Or Python API: