
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 755323156
- CO2 Emissions (in grams): 2.4120667129093043

## Validation Metrics

- Loss: 0.17826060950756073
- Accuracy: 0.9550898203592815
- Macro F1: 0.8880388927888968
- Micro F1: 0.9550898203592815
- Weighted F1: 0.9528256324309916
- Macro Precision: 0.9093073732635162
- Micro Precision: 0.9550898203592815
- Weighted Precision: 0.9533674643333371
- Macro Recall: 0.8872729481745715
- Micro Recall: 0.9550898203592815
- Weighted Recall: 0.9550898203592815


## 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/xInsignia/autotrain-Online_orders-755323156
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("xInsignia/autotrain-Online_orders-755323156", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("xInsignia/autotrain-Online_orders-755323156", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["xInsignia/autotrain-data-Online_orders-5cf92320"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 2.4120667129093043}
transformers pytorch distilbert text-classification autotrain en dataset:xInsignia/autotrain-data-Online_orders-5cf92320 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-xInsignia/autotrain-data-Online_orders-5cf92320 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 755323156
- CO2 Emissions (in grams): 2.4120667129093043

## Validation Metrics

- Loss: 0.17826060950756073
- Accuracy: 0.9550898203592815
- Macro F1: 0.8880388927888968
- Micro F1: 0.9550898203592815
- Weighted F1: 0.9528256324309916
- Macro Precision: 0.9093073732635162
- Micro Precision: 0.9550898203592815
- Weighted Precision: 0.9533674643333371
- Macro Recall: 0.8872729481745715
- Micro Recall: 0.9550898203592815
- Weighted Recall: 0.9550898203592815


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 755323156
- CO2 Emissions (in grams): 2.4120667129093043 ## Validation Metrics

- Loss: 0.17826060950756073
- Accuracy: 0.9550898203592815
- Macro F1: 0.8880388927888968
- Micro F1: 0.9550898203592815
- Weighted F1: 0.9528256324309916
- Macro Precision: 0.9093073732635162
- Micro Precision: 0.9550898203592815
- Weighted Precision: 0.9533674643333371
- Macro Recall: 0.8872729481745715
- Micro Recall: 0.9550898203592815
- Weighted Recall: 0.9550898203592815 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-xInsignia/autotrain-data-Online_orders-5cf92320 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 755323156
- CO2 Emissions (in grams): 2.4120667129093043 ## Validation Metrics

- Loss: 0.17826060950756073
- Accuracy: 0.9550898203592815
- Macro F1: 0.8880388927888968
- Micro F1: 0.9550898203592815
- Weighted F1: 0.9528256324309916
- Macro Precision: 0.9093073732635162
- Micro Precision: 0.9550898203592815
- Weighted Precision: 0.9533674643333371
- Macro Recall: 0.8872729481745715
- Micro Recall: 0.9550898203592815
- Weighted Recall: 0.9550898203592815 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-xInsignia/autotrain-data-Online_orders-5cf92320 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 755323156
- CO2 Emissions (in grams): 2.4120667129093043## Validation Metrics

- Loss: 0.17826060950756073
- Accuracy: 0.9550898203592815
- Macro F1: 0.8880388927888968
- Micro F1: 0.9550898203592815
- Weighted F1: 0.9528256324309916
- Macro Precision: 0.9093073732635162
- Micro Precision: 0.9550898203592815
- Weighted Precision: 0.9533674643333371
- Macro Recall: 0.8872729481745715
- Micro Recall: 0.9550898203592815
- Weighted Recall: 0.9550898203592815## Usage

You can use cURL to access this model:



Or Python API: