
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 51657122297
- CO2 Emissions (in grams): 0.0136

## Validation Metrics

- Loss: 0.426
- Accuracy: 0.828
- Macro F1: 0.826
- Micro F1: 0.828
- Weighted F1: 0.827
- Macro Precision: 0.827
- Micro Precision: 0.828
- Weighted Precision: 0.828
- Macro Recall: 0.825
- Micro Recall: 0.828
- Weighted Recall: 0.828


## 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/Muhsabrys/autotrain-xlmroblarge-pretweet-51657122297
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Muhsabrys/autotrain-xlmroblarge-pretweet-51657122297", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Muhsabrys/autotrain-xlmroblarge-pretweet-51657122297", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["Muhsabrys/autotrain-data-xlmroblarge-pretweet"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.013614585185795386}}
transformers pytorch xlm-roberta text-classification autotrain unk dataset:Muhsabrys/autotrain-data-xlmroblarge-pretweet co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-Muhsabrys/autotrain-data-xlmroblarge-pretweet #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 51657122297
- CO2 Emissions (in grams): 0.0136

## Validation Metrics

- Loss: 0.426
- Accuracy: 0.828
- Macro F1: 0.826
- Micro F1: 0.828
- Weighted F1: 0.827
- Macro Precision: 0.827
- Micro Precision: 0.828
- Weighted Precision: 0.828
- Macro Recall: 0.825
- Micro Recall: 0.828
- Weighted Recall: 0.828


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 51657122297
- CO2 Emissions (in grams): 0.0136 ## Validation Metrics

- Loss: 0.426
- Accuracy: 0.828
- Macro F1: 0.826
- Micro F1: 0.828
- Weighted F1: 0.827
- Macro Precision: 0.827
- Micro Precision: 0.828
- Weighted Precision: 0.828
- Macro Recall: 0.825
- Micro Recall: 0.828
- Weighted Recall: 0.828 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 51657122297
- CO2 Emissions (in grams): 0.0136 ## Validation Metrics

- Loss: 0.426
- Accuracy: 0.828
- Macro F1: 0.826
- Micro F1: 0.828
- Weighted F1: 0.827
- Macro Precision: 0.827
- Micro Precision: 0.828
- Weighted Precision: 0.828
- Macro Recall: 0.825
- Micro Recall: 0.828
- Weighted Recall: 0.828 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 51657122297
- CO2 Emissions (in grams): 0.0136## Validation Metrics

- Loss: 0.426
- Accuracy: 0.828
- Macro F1: 0.826
- Micro F1: 0.828
- Weighted F1: 0.827
- Macro Precision: 0.827
- Micro Precision: 0.828
- Weighted Precision: 0.828
- Macro Recall: 0.825
- Micro Recall: 0.828
- Weighted Recall: 0.828## Usage

You can use cURL to access this model:



Or Python API: