
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 672119800
- CO2 Emissions (in grams): 2011.6528745969179

## Validation Metrics

- Loss: 0.9570887088775635
- Accuracy: 0.5830708333333333
- Macro F1: 0.5789149828346194
- Micro F1: 0.5830708333333333
- Weighted F1: 0.5789149828346193
- Macro Precision: 0.5808338093704437
- Micro Precision: 0.5830708333333333
- Weighted Precision: 0.5808338093704437
- Macro Recall: 0.5830708333333334
- Micro Recall: 0.5830708333333333
- Weighted Recall: 0.5830708333333333


## 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-672119800
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("YXHugging/autotrain-xlm-roberta-base-reviews-672119800", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("YXHugging/autotrain-xlm-roberta-base-reviews-672119800", 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": 2011.6528745969179}
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: 672119800
- CO2 Emissions (in grams): 2011.6528745969179

## Validation Metrics

- Loss: 0.9570887088775635
- Accuracy: 0.5830708333333333
- Macro F1: 0.5789149828346194
- Micro F1: 0.5830708333333333
- Weighted F1: 0.5789149828346193
- Macro Precision: 0.5808338093704437
- Micro Precision: 0.5830708333333333
- Weighted Precision: 0.5808338093704437
- Macro Recall: 0.5830708333333334
- Micro Recall: 0.5830708333333333
- Weighted Recall: 0.5830708333333333


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 672119800
- CO2 Emissions (in grams): 2011.6528745969179 ## Validation Metrics

- Loss: 0.9570887088775635
- Accuracy: 0.5830708333333333
- Macro F1: 0.5789149828346194
- Micro F1: 0.5830708333333333
- Weighted F1: 0.5789149828346193
- Macro Precision: 0.5808338093704437
- Micro Precision: 0.5830708333333333
- Weighted Precision: 0.5808338093704437
- Macro Recall: 0.5830708333333334
- Micro Recall: 0.5830708333333333
- Weighted Recall: 0.5830708333333333 ## 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: 672119800
- CO2 Emissions (in grams): 2011.6528745969179 ## Validation Metrics

- Loss: 0.9570887088775635
- Accuracy: 0.5830708333333333
- Macro F1: 0.5789149828346194
- Micro F1: 0.5830708333333333
- Weighted F1: 0.5789149828346193
- Macro Precision: 0.5808338093704437
- Micro Precision: 0.5830708333333333
- Weighted Precision: 0.5808338093704437
- Macro Recall: 0.5830708333333334
- Micro Recall: 0.5830708333333333
- Weighted Recall: 0.5830708333333333 ## 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: 672119800
- CO2 Emissions (in grams): 2011.6528745969179## Validation Metrics

- Loss: 0.9570887088775635
- Accuracy: 0.5830708333333333
- Macro F1: 0.5789149828346194
- Micro F1: 0.5830708333333333
- Weighted F1: 0.5789149828346193
- Macro Precision: 0.5808338093704437
- Micro Precision: 0.5830708333333333
- Weighted Precision: 0.5808338093704437
- Macro Recall: 0.5830708333333334
- Micro Recall: 0.5830708333333333
- Weighted Recall: 0.5830708333333333## Usage

You can use cURL to access this model:



Or Python API: