
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 717221775
- CO2 Emissions (in grams): 5.080390550458655

## Validation Metrics

- Loss: 0.35279911756515503
- Accuracy: 0.9269102990033222
- Macro F1: 0.9261839948926327
- Micro F1: 0.9269102990033222
- Weighted F1: 0.9263981751760975
- Macro Precision: 0.9273912049203341
- Micro Precision: 0.9269102990033222
- Weighted Precision: 0.9280084437800646
- Macro Recall: 0.927250645380574
- Micro Recall: 0.9269102990033222
- Weighted Recall: 0.9269102990033222


## 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-717221775
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("palakagl/autotrain-PersonalAssitant-717221775", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("palakagl/autotrain-PersonalAssitant-717221775", 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": 5.080390550458655}
transformers pytorch bert text-classification autotrain en dataset:palakagl/autotrain-data-PersonalAssitant co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #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: 717221775
- CO2 Emissions (in grams): 5.080390550458655

## Validation Metrics

- Loss: 0.35279911756515503
- Accuracy: 0.9269102990033222
- Macro F1: 0.9261839948926327
- Micro F1: 0.9269102990033222
- Weighted F1: 0.9263981751760975
- Macro Precision: 0.9273912049203341
- Micro Precision: 0.9269102990033222
- Weighted Precision: 0.9280084437800646
- Macro Recall: 0.927250645380574
- Micro Recall: 0.9269102990033222
- Weighted Recall: 0.9269102990033222


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 717221775
- CO2 Emissions (in grams): 5.080390550458655 ## Validation Metrics

- Loss: 0.35279911756515503
- Accuracy: 0.9269102990033222
- Macro F1: 0.9261839948926327
- Micro F1: 0.9269102990033222
- Weighted F1: 0.9263981751760975
- Macro Precision: 0.9273912049203341
- Micro Precision: 0.9269102990033222
- Weighted Precision: 0.9280084437800646
- Macro Recall: 0.927250645380574
- Micro Recall: 0.9269102990033222
- Weighted Recall: 0.9269102990033222 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #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: 717221775
- CO2 Emissions (in grams): 5.080390550458655 ## Validation Metrics

- Loss: 0.35279911756515503
- Accuracy: 0.9269102990033222
- Macro F1: 0.9261839948926327
- Micro F1: 0.9269102990033222
- Weighted F1: 0.9263981751760975
- Macro Precision: 0.9273912049203341
- Micro Precision: 0.9269102990033222
- Weighted Precision: 0.9280084437800646
- Macro Recall: 0.927250645380574
- Micro Recall: 0.9269102990033222
- Weighted Recall: 0.9269102990033222 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #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: 717221775
- CO2 Emissions (in grams): 5.080390550458655## Validation Metrics

- Loss: 0.35279911756515503
- Accuracy: 0.9269102990033222
- Macro F1: 0.9261839948926327
- Micro F1: 0.9269102990033222
- Weighted F1: 0.9263981751760975
- Macro Precision: 0.9273912049203341
- Micro Precision: 0.9269102990033222
- Weighted Precision: 0.9280084437800646
- Macro Recall: 0.927250645380574
- Micro Recall: 0.9269102990033222
- Weighted Recall: 0.9269102990033222## Usage

You can use cURL to access this model:



Or Python API: