
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 966432120
- CO2 Emissions (in grams): 0.061185706621337065

## Validation Metrics

- Loss: 0.6066656112670898
- Accuracy: 0.724822695035461
- Macro F1: 0.7077087000886584
- Micro F1: 0.7248226950354609
- Weighted F1: 0.7077087000886584
- Macro Precision: 0.7143184427227084
- Micro Precision: 0.724822695035461
- Weighted Precision: 0.7143184427227083
- Macro Recall: 0.7248226950354609
- Micro Recall: 0.724822695035461
- Weighted Recall: 0.724822695035461


## 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/qualitydatalab/autotrain-car-review-project-966432120
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("qualitydatalab/autotrain-car-review-project-966432120", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("qualitydatalab/autotrain-car-review-project-966432120", 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 AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.061185706621337065}
transformers pytorch roberta text-classification autotrain en dataset:qualitydatalab/autotrain-data-car-review-project co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-qualitydatalab/autotrain-data-car-review-project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 966432120
- CO2 Emissions (in grams): 0.061185706621337065

## Validation Metrics

- Loss: 0.6066656112670898
- Accuracy: 0.724822695035461
- Macro F1: 0.7077087000886584
- Micro F1: 0.7248226950354609
- Weighted F1: 0.7077087000886584
- Macro Precision: 0.7143184427227084
- Micro Precision: 0.724822695035461
- Weighted Precision: 0.7143184427227083
- Macro Recall: 0.7248226950354609
- Micro Recall: 0.724822695035461
- Weighted Recall: 0.724822695035461


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 966432120
- CO2 Emissions (in grams): 0.061185706621337065 ## Validation Metrics

- Loss: 0.6066656112670898
- Accuracy: 0.724822695035461
- Macro F1: 0.7077087000886584
- Micro F1: 0.7248226950354609
- Weighted F1: 0.7077087000886584
- Macro Precision: 0.7143184427227084
- Micro Precision: 0.724822695035461
- Weighted Precision: 0.7143184427227083
- Macro Recall: 0.7248226950354609
- Micro Recall: 0.724822695035461
- Weighted Recall: 0.724822695035461 ## 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 #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 966432120
- CO2 Emissions (in grams): 0.061185706621337065 ## Validation Metrics

- Loss: 0.6066656112670898
- Accuracy: 0.724822695035461
- Macro F1: 0.7077087000886584
- Micro F1: 0.7248226950354609
- Weighted F1: 0.7077087000886584
- Macro Precision: 0.7143184427227084
- Micro Precision: 0.724822695035461
- Weighted Precision: 0.7143184427227083
- Macro Recall: 0.7248226950354609
- Micro Recall: 0.724822695035461
- Weighted Recall: 0.724822695035461 ## 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 #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 966432120
- CO2 Emissions (in grams): 0.061185706621337065## Validation Metrics

- Loss: 0.6066656112670898
- Accuracy: 0.724822695035461
- Macro F1: 0.7077087000886584
- Micro F1: 0.7248226950354609
- Weighted F1: 0.7077087000886584
- Macro Precision: 0.7143184427227084
- Micro Precision: 0.724822695035461
- Weighted Precision: 0.7143184427227083
- Macro Recall: 0.7248226950354609
- Micro Recall: 0.724822695035461
- Weighted Recall: 0.724822695035461## Usage

You can use cURL to access this model:



Or Python API: