
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 872827783
- CO2 Emissions (in grams): 0.020162211418903533

## Validation Metrics

- Loss: 0.25198695063591003
- Accuracy: 0.9325714285714286
- Macro F1: 0.9254931094274171
- Micro F1: 0.9325714285714286
- Weighted F1: 0.9323540959391766
- Macro Precision: 0.9286720054236212
- Micro Precision: 0.9325714285714286
- Weighted Precision: 0.9324375609546055
- Macro Recall: 0.9227549386201338
- Micro Recall: 0.9325714285714286
- Weighted Recall: 0.9325714285714286


## 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/Yarn007/autotrain-Napkin-872827783
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Yarn007/autotrain-Napkin-872827783", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Yarn007/autotrain-Napkin-872827783", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["Yarn007/autotrain-data-Napkin"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.020162211418903533}
transformers pytorch bert text-classification autotrain unk dataset:Yarn007/autotrain-data-Napkin co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-Yarn007/autotrain-data-Napkin #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 872827783
- CO2 Emissions (in grams): 0.020162211418903533

## Validation Metrics

- Loss: 0.25198695063591003
- Accuracy: 0.9325714285714286
- Macro F1: 0.9254931094274171
- Micro F1: 0.9325714285714286
- Weighted F1: 0.9323540959391766
- Macro Precision: 0.9286720054236212
- Micro Precision: 0.9325714285714286
- Weighted Precision: 0.9324375609546055
- Macro Recall: 0.9227549386201338
- Micro Recall: 0.9325714285714286
- Weighted Recall: 0.9325714285714286


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 872827783
- CO2 Emissions (in grams): 0.020162211418903533 ## Validation Metrics

- Loss: 0.25198695063591003
- Accuracy: 0.9325714285714286
- Macro F1: 0.9254931094274171
- Micro F1: 0.9325714285714286
- Weighted F1: 0.9323540959391766
- Macro Precision: 0.9286720054236212
- Micro Precision: 0.9325714285714286
- Weighted Precision: 0.9324375609546055
- Macro Recall: 0.9227549386201338
- Micro Recall: 0.9325714285714286
- Weighted Recall: 0.9325714285714286 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-Yarn007/autotrain-data-Napkin #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 872827783
- CO2 Emissions (in grams): 0.020162211418903533 ## Validation Metrics

- Loss: 0.25198695063591003
- Accuracy: 0.9325714285714286
- Macro F1: 0.9254931094274171
- Micro F1: 0.9325714285714286
- Weighted F1: 0.9323540959391766
- Macro Precision: 0.9286720054236212
- Micro Precision: 0.9325714285714286
- Weighted Precision: 0.9324375609546055
- Macro Recall: 0.9227549386201338
- Micro Recall: 0.9325714285714286
- Weighted Recall: 0.9325714285714286 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-Yarn007/autotrain-data-Napkin #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 872827783
- CO2 Emissions (in grams): 0.020162211418903533## Validation Metrics

- Loss: 0.25198695063591003
- Accuracy: 0.9325714285714286
- Macro F1: 0.9254931094274171
- Micro F1: 0.9325714285714286
- Weighted F1: 0.9323540959391766
- Macro Precision: 0.9286720054236212
- Micro Precision: 0.9325714285714286
- Weighted Precision: 0.9324375609546055
- Macro Recall: 0.9227549386201338
- Micro Recall: 0.9325714285714286
- Weighted Recall: 0.9325714285714286## Usage

You can use cURL to access this model:



Or Python API: