
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 966432121
- CO2 Emissions (in grams): 0.21529888368377176

## Validation Metrics

- Loss: 0.6013365983963013
- Accuracy: 0.737791286727457
- Macro F1: 0.729171012281939
- Micro F1: 0.737791286727457
- Weighted F1: 0.729171012281939
- Macro Precision: 0.7313770127538427
- Micro Precision: 0.737791286727457
- Weighted Precision: 0.7313770127538428
- Macro Recall: 0.737791286727457
- Micro Recall: 0.737791286727457
- Weighted Recall: 0.737791286727457


## 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 driving this car"}' https://api-inference.huggingface.co/models/qualitydatalab/autotrain-car-review-project-966432121
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("qualitydatalab/autotrain-car-review-project-966432121", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("qualitydatalab/autotrain-car-review-project-966432121", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["qualitydatalab/autotrain-data-car-review-project"], "widget": [{"text": "I love driving this car"}], "co2_eq_emissions": 0.21529888368377176}
transformers pytorch roberta text-classification autotrain en dataset:qualitydatalab/autotrain-data-car-review-project co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-qualitydatalab/autotrain-data-car-review-project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 966432121
- CO2 Emissions (in grams): 0.21529888368377176

## Validation Metrics

- Loss: 0.6013365983963013
- Accuracy: 0.737791286727457
- Macro F1: 0.729171012281939
- Micro F1: 0.737791286727457
- Weighted F1: 0.729171012281939
- Macro Precision: 0.7313770127538427
- Micro Precision: 0.737791286727457
- Weighted Precision: 0.7313770127538428
- Macro Recall: 0.737791286727457
- Micro Recall: 0.737791286727457
- Weighted Recall: 0.737791286727457


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 966432121
- CO2 Emissions (in grams): 0.21529888368377176 ## Validation Metrics

- Loss: 0.6013365983963013
- Accuracy: 0.737791286727457
- Macro F1: 0.729171012281939
- Micro F1: 0.737791286727457
- Weighted F1: 0.729171012281939
- Macro Precision: 0.7313770127538427
- Micro Precision: 0.737791286727457
- Weighted Precision: 0.7313770127538428
- Macro Recall: 0.737791286727457
- Micro Recall: 0.737791286727457
- Weighted Recall: 0.737791286727457 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-qualitydatalab/autotrain-data-car-review-project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 966432121
- CO2 Emissions (in grams): 0.21529888368377176 ## Validation Metrics

- Loss: 0.6013365983963013
- Accuracy: 0.737791286727457
- Macro F1: 0.729171012281939
- Micro F1: 0.737791286727457
- Weighted F1: 0.729171012281939
- Macro Precision: 0.7313770127538427
- Micro Precision: 0.737791286727457
- Weighted Precision: 0.7313770127538428
- Macro Recall: 0.737791286727457
- Micro Recall: 0.737791286727457
- Weighted Recall: 0.737791286727457 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-qualitydatalab/autotrain-data-car-review-project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 966432121
- CO2 Emissions (in grams): 0.21529888368377176## Validation Metrics

- Loss: 0.6013365983963013
- Accuracy: 0.737791286727457
- Macro F1: 0.729171012281939
- Micro F1: 0.737791286727457
- Weighted F1: 0.729171012281939
- Macro Precision: 0.7313770127538427
- Micro Precision: 0.737791286727457
- Weighted Precision: 0.7313770127538428
- Macro Recall: 0.737791286727457
- Micro Recall: 0.737791286727457
- Weighted Recall: 0.737791286727457## Usage

You can use cURL to access this model:



Or Python API: