
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1009033469
- CO2 Emissions (in grams): 0.1287915253247826

## Validation Metrics

- Loss: 0.4084862470626831
- Accuracy: 0.8722054859679721
- Macro F1: 0.6340608446004876
- Micro F1: 0.8722054859679722
- Weighted F1: 0.8679846554644491
- Macro Precision: 0.645023001823007
- Micro Precision: 0.8722054859679721
- Weighted Precision: 0.8656545967138464
- Macro Recall: 0.6283763558287574
- Micro Recall: 0.8722054859679721
- Weighted Recall: 0.8722054859679721


## 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/Siddish/autotrain-yes-or-no-classifier-on-circa-1009033469
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Siddish/autotrain-yes-or-no-classifier-on-circa-1009033469", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Siddish/autotrain-yes-or-no-classifier-on-circa-1009033469", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["Siddish/autotrain-data-yes-or-no-classifier-on-circa"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.1287915253247826}
transformers pytorch roberta text-classification autotrain en dataset:Siddish/autotrain-data-yes-or-no-classifier-on-circa co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-Siddish/autotrain-data-yes-or-no-classifier-on-circa #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1009033469
- CO2 Emissions (in grams): 0.1287915253247826

## Validation Metrics

- Loss: 0.4084862470626831
- Accuracy: 0.8722054859679721
- Macro F1: 0.6340608446004876
- Micro F1: 0.8722054859679722
- Weighted F1: 0.8679846554644491
- Macro Precision: 0.645023001823007
- Micro Precision: 0.8722054859679721
- Weighted Precision: 0.8656545967138464
- Macro Recall: 0.6283763558287574
- Micro Recall: 0.8722054859679721
- Weighted Recall: 0.8722054859679721


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1009033469
- CO2 Emissions (in grams): 0.1287915253247826 ## Validation Metrics

- Loss: 0.4084862470626831
- Accuracy: 0.8722054859679721
- Macro F1: 0.6340608446004876
- Micro F1: 0.8722054859679722
- Weighted F1: 0.8679846554644491
- Macro Precision: 0.645023001823007
- Micro Precision: 0.8722054859679721
- Weighted Precision: 0.8656545967138464
- Macro Recall: 0.6283763558287574
- Micro Recall: 0.8722054859679721
- Weighted Recall: 0.8722054859679721 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-Siddish/autotrain-data-yes-or-no-classifier-on-circa #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1009033469
- CO2 Emissions (in grams): 0.1287915253247826 ## Validation Metrics

- Loss: 0.4084862470626831
- Accuracy: 0.8722054859679721
- Macro F1: 0.6340608446004876
- Micro F1: 0.8722054859679722
- Weighted F1: 0.8679846554644491
- Macro Precision: 0.645023001823007
- Micro Precision: 0.8722054859679721
- Weighted Precision: 0.8656545967138464
- Macro Recall: 0.6283763558287574
- Micro Recall: 0.8722054859679721
- Weighted Recall: 0.8722054859679721 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-Siddish/autotrain-data-yes-or-no-classifier-on-circa #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1009033469
- CO2 Emissions (in grams): 0.1287915253247826## Validation Metrics

- Loss: 0.4084862470626831
- Accuracy: 0.8722054859679721
- Macro F1: 0.6340608446004876
- Micro F1: 0.8722054859679722
- Weighted F1: 0.8679846554644491
- Macro Precision: 0.645023001823007
- Micro Precision: 0.8722054859679721
- Weighted Precision: 0.8656545967138464
- Macro Recall: 0.6283763558287574
- Micro Recall: 0.8722054859679721
- Weighted Recall: 0.8722054859679721## Usage

You can use cURL to access this model:



Or Python API: