
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 874027878
- CO2 Emissions (in grams): 9.123490454955585

## Validation Metrics

- Loss: 0.35724225640296936
- Accuracy: 0.8571428571428571
- Precision: 0.7637362637362637
- Recall: 0.8910256410256411
- AUC: 0.9267555361305361
- F1: 0.8224852071005917

## 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/Amalq/autotrain-smm4h_large_roberta_clean-874027878
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Amalq/autotrain-smm4h_large_roberta_clean-874027878", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Amalq/autotrain-smm4h_large_roberta_clean-874027878", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["Amalq/autotrain-data-smm4h_large_roberta_clean"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 9.123490454955585}
transformers pytorch roberta text-classification autotrain unk dataset:Amalq/autotrain-data-smm4h_large_roberta_clean co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-Amalq/autotrain-data-smm4h_large_roberta_clean #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 874027878
- CO2 Emissions (in grams): 9.123490454955585

## Validation Metrics

- Loss: 0.35724225640296936
- Accuracy: 0.8571428571428571
- Precision: 0.7637362637362637
- Recall: 0.8910256410256411
- AUC: 0.9267555361305361
- F1: 0.8224852071005917

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 874027878
- CO2 Emissions (in grams): 9.123490454955585 ## Validation Metrics

- Loss: 0.35724225640296936
- Accuracy: 0.8571428571428571
- Precision: 0.7637362637362637
- Recall: 0.8910256410256411
- AUC: 0.9267555361305361
- F1: 0.8224852071005917 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-Amalq/autotrain-data-smm4h_large_roberta_clean #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 874027878
- CO2 Emissions (in grams): 9.123490454955585 ## Validation Metrics

- Loss: 0.35724225640296936
- Accuracy: 0.8571428571428571
- Precision: 0.7637362637362637
- Recall: 0.8910256410256411
- AUC: 0.9267555361305361
- F1: 0.8224852071005917 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-Amalq/autotrain-data-smm4h_large_roberta_clean #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 874027878
- CO2 Emissions (in grams): 9.123490454955585## Validation Metrics

- Loss: 0.35724225640296936
- Accuracy: 0.8571428571428571
- Precision: 0.7637362637362637
- Recall: 0.8910256410256411
- AUC: 0.9267555361305361
- F1: 0.8224852071005917## Usage

You can use cURL to access this model:



Or Python API: