
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3647697496
- CO2 Emissions (in grams): 1.1197

## Validation Metrics

- Loss: 1.379
- Accuracy: 0.875
- Macro F1: 0.867
- Micro F1: 0.875
- Weighted F1: 0.867
- Macro Precision: 0.917
- Micro Precision: 0.875
- Weighted Precision: 0.917
- Macro Recall: 0.875
- Micro Recall: 0.875
- Weighted Recall: 0.875


## 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/harshasurampudi/autotrain-intent-classification-roberta-3647697496
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("harshasurampudi/autotrain-intent-classification-roberta-3647697496", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("harshasurampudi/autotrain-intent-classification-roberta-3647697496", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["harshasurampudi/autotrain-data-intent-classification-roberta"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 1.1197250732252992}}
transformers pytorch safetensors roberta text-classification autotrain unk dataset:harshasurampudi/autotrain-data-intent-classification-roberta co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #autotrain #unk #dataset-harshasurampudi/autotrain-data-intent-classification-roberta #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3647697496
- CO2 Emissions (in grams): 1.1197

## Validation Metrics

- Loss: 1.379
- Accuracy: 0.875
- Macro F1: 0.867
- Micro F1: 0.875
- Weighted F1: 0.867
- Macro Precision: 0.917
- Micro Precision: 0.875
- Weighted Precision: 0.917
- Macro Recall: 0.875
- Micro Recall: 0.875
- Weighted Recall: 0.875


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3647697496
- CO2 Emissions (in grams): 1.1197 ## Validation Metrics

- Loss: 1.379
- Accuracy: 0.875
- Macro F1: 0.867
- Micro F1: 0.875
- Weighted F1: 0.867
- Macro Precision: 0.917
- Micro Precision: 0.875
- Weighted Precision: 0.917
- Macro Recall: 0.875
- Micro Recall: 0.875
- Weighted Recall: 0.875 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #autotrain #unk #dataset-harshasurampudi/autotrain-data-intent-classification-roberta #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3647697496
- CO2 Emissions (in grams): 1.1197 ## Validation Metrics

- Loss: 1.379
- Accuracy: 0.875
- Macro F1: 0.867
- Micro F1: 0.875
- Weighted F1: 0.867
- Macro Precision: 0.917
- Micro Precision: 0.875
- Weighted Precision: 0.917
- Macro Recall: 0.875
- Micro Recall: 0.875
- Weighted Recall: 0.875 ## Usage

You can use cURL to access this model:



Or Python API: