
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 46079114807
- CO2 Emissions (in grams): 0.4102

## Validation Metrics

- Loss: 0.305
- Accuracy: 0.891
- Macro F1: 0.887
- Micro F1: 0.891
- Weighted F1: 0.891
- Macro Precision: 0.890
- Micro Precision: 0.891
- Weighted Precision: 0.891
- Macro Recall: 0.885
- Micro Recall: 0.891
- Weighted Recall: 0.891


## 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/dvilasuero/autotrain-alpaca-bs-detector-46079114807
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("dvilasuero/autotrain-alpaca-bs-detector-46079114807", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("dvilasuero/autotrain-alpaca-bs-detector-46079114807", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["dvilasuero/autotrain-data-alpaca-bs-detector"], "widget": [{"text": "INSTRUCTION:\nReview the given chart and find the outlier.\nINPUT:\nData Series A: 0, 5, 8, 10, 11, 10, 9\nOUTPUT:\nThe outlier of the given data series is 11, as it is numerically greater than the rest of the numbers in the series.\n"}], "co2_eq_emissions": {"emissions": 0.4102361717910936}}
transformers pytorch deberta-v2 text-classification autotrain en dataset:dvilasuero/autotrain-data-alpaca-bs-detector co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #en #dataset-dvilasuero/autotrain-data-alpaca-bs-detector #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 46079114807
- CO2 Emissions (in grams): 0.4102

## Validation Metrics

- Loss: 0.305
- Accuracy: 0.891
- Macro F1: 0.887
- Micro F1: 0.891
- Weighted F1: 0.891
- Macro Precision: 0.890
- Micro Precision: 0.891
- Weighted Precision: 0.891
- Macro Recall: 0.885
- Micro Recall: 0.891
- Weighted Recall: 0.891


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 46079114807
- CO2 Emissions (in grams): 0.4102 ## Validation Metrics

- Loss: 0.305
- Accuracy: 0.891
- Macro F1: 0.887
- Micro F1: 0.891
- Weighted F1: 0.891
- Macro Precision: 0.890
- Micro Precision: 0.891
- Weighted Precision: 0.891
- Macro Recall: 0.885
- Micro Recall: 0.891
- Weighted Recall: 0.891 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #en #dataset-dvilasuero/autotrain-data-alpaca-bs-detector #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 46079114807
- CO2 Emissions (in grams): 0.4102 ## Validation Metrics

- Loss: 0.305
- Accuracy: 0.891
- Macro F1: 0.887
- Micro F1: 0.891
- Weighted F1: 0.891
- Macro Precision: 0.890
- Micro Precision: 0.891
- Weighted Precision: 0.891
- Macro Recall: 0.885
- Micro Recall: 0.891
- Weighted Recall: 0.891 ## Usage

You can use cURL to access this model:



Or Python API: