
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 717221781
- CO2 Emissions (in grams): 2.258363491829382

## Validation Metrics

- Loss: 0.38660314679145813
- Accuracy: 0.9042081949058693
- Macro F1: 0.9079200295131094
- Micro F1: 0.9042081949058692
- Weighted F1: 0.9052766730963512
- Macro Precision: 0.9116101664087508
- Micro Precision: 0.9042081949058693
- Weighted Precision: 0.9097680514456175
- Macro Recall: 0.9080246002936301
- Micro Recall: 0.9042081949058693
- Weighted Recall: 0.9042081949058693


## 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/palakagl/autotrain-PersonalAssitant-717221781
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("palakagl/autotrain-PersonalAssitant-717221781", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("palakagl/autotrain-PersonalAssitant-717221781", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["palakagl/autotrain-data-PersonalAssitant"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 2.258363491829382}
transformers pytorch distilbert text-classification autotrain en dataset:palakagl/autotrain-data-PersonalAssitant co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-palakagl/autotrain-data-PersonalAssitant #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 717221781
- CO2 Emissions (in grams): 2.258363491829382

## Validation Metrics

- Loss: 0.38660314679145813
- Accuracy: 0.9042081949058693
- Macro F1: 0.9079200295131094
- Micro F1: 0.9042081949058692
- Weighted F1: 0.9052766730963512
- Macro Precision: 0.9116101664087508
- Micro Precision: 0.9042081949058693
- Weighted Precision: 0.9097680514456175
- Macro Recall: 0.9080246002936301
- Micro Recall: 0.9042081949058693
- Weighted Recall: 0.9042081949058693


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 717221781
- CO2 Emissions (in grams): 2.258363491829382 ## Validation Metrics

- Loss: 0.38660314679145813
- Accuracy: 0.9042081949058693
- Macro F1: 0.9079200295131094
- Micro F1: 0.9042081949058692
- Weighted F1: 0.9052766730963512
- Macro Precision: 0.9116101664087508
- Micro Precision: 0.9042081949058693
- Weighted Precision: 0.9097680514456175
- Macro Recall: 0.9080246002936301
- Micro Recall: 0.9042081949058693
- Weighted Recall: 0.9042081949058693 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 717221781
- CO2 Emissions (in grams): 2.258363491829382 ## Validation Metrics

- Loss: 0.38660314679145813
- Accuracy: 0.9042081949058693
- Macro F1: 0.9079200295131094
- Micro F1: 0.9042081949058692
- Weighted F1: 0.9052766730963512
- Macro Precision: 0.9116101664087508
- Micro Precision: 0.9042081949058693
- Weighted Precision: 0.9097680514456175
- Macro Recall: 0.9080246002936301
- Micro Recall: 0.9042081949058693
- Weighted Recall: 0.9042081949058693 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 717221781
- CO2 Emissions (in grams): 2.258363491829382## Validation Metrics

- Loss: 0.38660314679145813
- Accuracy: 0.9042081949058693
- Macro F1: 0.9079200295131094
- Micro F1: 0.9042081949058692
- Weighted F1: 0.9052766730963512
- Macro Precision: 0.9116101664087508
- Micro Precision: 0.9042081949058693
- Weighted Precision: 0.9097680514456175
- Macro Recall: 0.9080246002936301
- Micro Recall: 0.9042081949058693
- Weighted Recall: 0.9042081949058693## Usage

You can use cURL to access this model:



Or Python API: