
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 672119797
- CO2 Emissions (in grams): 1019.0229633198007

## Validation Metrics

- Loss: 0.9898674488067627
- Accuracy: 0.5688083333333334
- Macro F1: 0.5640966271895913
- Micro F1: 0.5688083333333334
- Weighted F1: 0.5640966271895913
- Macro Precision: 0.5673737438011194
- Micro Precision: 0.5688083333333334
- Weighted Precision: 0.5673737438011194
- Macro Recall: 0.5688083333333334
- Micro Recall: 0.5688083333333334
- Weighted Recall: 0.5688083333333334


## 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/YXHugging/autotrain-xlm-roberta-base-reviews-672119797
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("YXHugging/autotrain-xlm-roberta-base-reviews-672119797", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("YXHugging/autotrain-xlm-roberta-base-reviews-672119797", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["YXHugging/autotrain-data-xlm-roberta-base-reviews"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 1019.0229633198007}
transformers pytorch xlm-roberta text-classification autotrain unk dataset:YXHugging/autotrain-data-xlm-roberta-base-reviews co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-YXHugging/autotrain-data-xlm-roberta-base-reviews #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 672119797
- CO2 Emissions (in grams): 1019.0229633198007

## Validation Metrics

- Loss: 0.9898674488067627
- Accuracy: 0.5688083333333334
- Macro F1: 0.5640966271895913
- Micro F1: 0.5688083333333334
- Weighted F1: 0.5640966271895913
- Macro Precision: 0.5673737438011194
- Micro Precision: 0.5688083333333334
- Weighted Precision: 0.5673737438011194
- Macro Recall: 0.5688083333333334
- Micro Recall: 0.5688083333333334
- Weighted Recall: 0.5688083333333334


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 672119797
- CO2 Emissions (in grams): 1019.0229633198007 ## Validation Metrics

- Loss: 0.9898674488067627
- Accuracy: 0.5688083333333334
- Macro F1: 0.5640966271895913
- Micro F1: 0.5688083333333334
- Weighted F1: 0.5640966271895913
- Macro Precision: 0.5673737438011194
- Micro Precision: 0.5688083333333334
- Weighted Precision: 0.5673737438011194
- Macro Recall: 0.5688083333333334
- Micro Recall: 0.5688083333333334
- Weighted Recall: 0.5688083333333334 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 672119797
- CO2 Emissions (in grams): 1019.0229633198007 ## Validation Metrics

- Loss: 0.9898674488067627
- Accuracy: 0.5688083333333334
- Macro F1: 0.5640966271895913
- Micro F1: 0.5688083333333334
- Weighted F1: 0.5640966271895913
- Macro Precision: 0.5673737438011194
- Micro Precision: 0.5688083333333334
- Weighted Precision: 0.5673737438011194
- Macro Recall: 0.5688083333333334
- Micro Recall: 0.5688083333333334
- Weighted Recall: 0.5688083333333334 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 672119797
- CO2 Emissions (in grams): 1019.0229633198007## Validation Metrics

- Loss: 0.9898674488067627
- Accuracy: 0.5688083333333334
- Macro F1: 0.5640966271895913
- Micro F1: 0.5688083333333334
- Weighted F1: 0.5640966271895913
- Macro Precision: 0.5673737438011194
- Micro Precision: 0.5688083333333334
- Weighted Precision: 0.5673737438011194
- Macro Recall: 0.5688083333333334
- Micro Recall: 0.5688083333333334
- Weighted Recall: 0.5688083333333334## Usage

You can use cURL to access this model:



Or Python API: