
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1148842296
- CO2 Emissions (in grams): 0.027846282970913613

## Validation Metrics

- Loss: 0.4816772937774658
- Accuracy: 0.864
- Macro F1: 0.865050349743783
- Micro F1: 0.864
- Weighted F1: 0.865050349743783
- Macro Precision: 0.8706266090178479
- Micro Precision: 0.864
- Weighted Precision: 0.8706266090178482
- Macro Recall: 0.864
- Micro Recall: 0.864
- Weighted Recall: 0.864


## 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/erixxdp/autotrain-gsemodel-1148842296
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("erixxdp/autotrain-gsemodel-1148842296", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("erixxdp/autotrain-gsemodel-1148842296", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "es", "tags": "xerox", "datasets": ["erixxdp/autotrain-data-gsemodel"], "widget": [{"text": "Debo de levantarme temprano para hacer ejercicio"}], "co2_eq_emissions": 0.027846282970913613}
transformers pytorch roberta text-classification xerox es dataset:erixxdp/autotrain-data-gsemodel co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #xerox #es #dataset-erixxdp/autotrain-data-gsemodel #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1148842296
- CO2 Emissions (in grams): 0.027846282970913613

## Validation Metrics

- Loss: 0.4816772937774658
- Accuracy: 0.864
- Macro F1: 0.865050349743783
- Micro F1: 0.864
- Weighted F1: 0.865050349743783
- Macro Precision: 0.8706266090178479
- Micro Precision: 0.864
- Weighted Precision: 0.8706266090178482
- Macro Recall: 0.864
- Micro Recall: 0.864
- Weighted Recall: 0.864


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1148842296
- CO2 Emissions (in grams): 0.027846282970913613 ## Validation Metrics

- Loss: 0.4816772937774658
- Accuracy: 0.864
- Macro F1: 0.865050349743783
- Micro F1: 0.864
- Weighted F1: 0.865050349743783
- Macro Precision: 0.8706266090178479
- Micro Precision: 0.864
- Weighted Precision: 0.8706266090178482
- Macro Recall: 0.864
- Micro Recall: 0.864
- Weighted Recall: 0.864 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #xerox #es #dataset-erixxdp/autotrain-data-gsemodel #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1148842296
- CO2 Emissions (in grams): 0.027846282970913613 ## Validation Metrics

- Loss: 0.4816772937774658
- Accuracy: 0.864
- Macro F1: 0.865050349743783
- Micro F1: 0.864
- Weighted F1: 0.865050349743783
- Macro Precision: 0.8706266090178479
- Micro Precision: 0.864
- Weighted Precision: 0.8706266090178482
- Macro Recall: 0.864
- Micro Recall: 0.864
- Weighted Recall: 0.864 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #xerox #es #dataset-erixxdp/autotrain-data-gsemodel #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1148842296
- CO2 Emissions (in grams): 0.027846282970913613## Validation Metrics

- Loss: 0.4816772937774658
- Accuracy: 0.864
- Macro F1: 0.865050349743783
- Micro F1: 0.864
- Weighted F1: 0.865050349743783
- Macro Precision: 0.8706266090178479
- Micro Precision: 0.864
- Weighted Precision: 0.8706266090178482
- Macro Recall: 0.864
- Micro Recall: 0.864
- Weighted Recall: 0.864## Usage

You can use cURL to access this model:



Or Python API: