
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 49241119078
- CO2 Emissions (in grams): 0.4690

## Validation Metrics

- Loss: 1.676
- Accuracy: 0.800
- Macro F1: 0.733
- Micro F1: 0.800
- Weighted F1: 0.733
- Macro Precision: 0.700
- Micro Precision: 0.800
- Weighted Precision: 0.700
- Macro Recall: 0.800
- Micro Recall: 0.800
- Weighted Recall: 0.800


## Usage

Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("tradero/distilbert-user-intent", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("tradero/distilbert-user-intent", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["tradero/autotrain-data-user-intent"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.46902644633558377}}
transformers pytorch deberta text-classification autotrain en dataset:tradero/autotrain-data-user-intent co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta #text-classification #autotrain #en #dataset-tradero/autotrain-data-user-intent #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 49241119078
- CO2 Emissions (in grams): 0.4690

## Validation Metrics

- Loss: 1.676
- Accuracy: 0.800
- Macro F1: 0.733
- Micro F1: 0.800
- Weighted F1: 0.733
- Macro Precision: 0.700
- Micro Precision: 0.800
- Weighted Precision: 0.700
- Macro Recall: 0.800
- Micro Recall: 0.800
- Weighted Recall: 0.800


## Usage

Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 49241119078
- CO2 Emissions (in grams): 0.4690 ## Validation Metrics

- Loss: 1.676
- Accuracy: 0.800
- Macro F1: 0.733
- Micro F1: 0.800
- Weighted F1: 0.733
- Macro Precision: 0.700
- Micro Precision: 0.800
- Weighted Precision: 0.700
- Macro Recall: 0.800
- Micro Recall: 0.800
- Weighted Recall: 0.800 ## Usage

Python API:
TAGS
#transformers #pytorch #deberta #text-classification #autotrain #en #dataset-tradero/autotrain-data-user-intent #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 49241119078
- CO2 Emissions (in grams): 0.4690 ## Validation Metrics

- Loss: 1.676
- Accuracy: 0.800
- Macro F1: 0.733
- Micro F1: 0.800
- Weighted F1: 0.733
- Macro Precision: 0.700
- Micro Precision: 0.800
- Weighted Precision: 0.700
- Macro Recall: 0.800
- Micro Recall: 0.800
- Weighted Recall: 0.800 ## Usage

Python API:
TAGS
#transformers #pytorch #deberta #text-classification #autotrain #en #dataset-tradero/autotrain-data-user-intent #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 49241119078
- CO2 Emissions (in grams): 0.4690## Validation Metrics

- Loss: 1.676
- Accuracy: 0.800
- Macro F1: 0.733
- Micro F1: 0.800
- Weighted F1: 0.733
- Macro Precision: 0.700
- Micro Precision: 0.800
- Weighted Precision: 0.700
- Macro Recall: 0.800
- Micro Recall: 0.800
- Weighted Recall: 0.800## Usage

Python API: