
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3621496854
- CO2 Emissions (in grams): 7.9378

## Validation Metrics

- Loss: 0.353
- Accuracy: 0.905
- Macro F1: 0.714
- Micro F1: 0.905
- Weighted F1: 0.890
- Macro Precision: 0.712
- Micro Precision: 0.905
- Weighted Precision: 0.887
- Macro Recall: 0.743
- Micro Recall: 0.905
- Weighted Recall: 0.905


## 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/gjbooth2/autotrain-glenn_ntsa_1-3621496854
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("gjbooth2/autotrain-glenn_ntsa_1-3621496854", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("gjbooth2/autotrain-glenn_ntsa_1-3621496854", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["gjbooth2/autotrain-data-glenn_ntsa_1"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 7.937797482362119}}
transformers pytorch bert text-classification autotrain en dataset:gjbooth2/autotrain-data-glenn_ntsa_1 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-gjbooth2/autotrain-data-glenn_ntsa_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3621496854
- CO2 Emissions (in grams): 7.9378

## Validation Metrics

- Loss: 0.353
- Accuracy: 0.905
- Macro F1: 0.714
- Micro F1: 0.905
- Weighted F1: 0.890
- Macro Precision: 0.712
- Micro Precision: 0.905
- Weighted Precision: 0.887
- Macro Recall: 0.743
- Micro Recall: 0.905
- Weighted Recall: 0.905


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3621496854
- CO2 Emissions (in grams): 7.9378 ## Validation Metrics

- Loss: 0.353
- Accuracy: 0.905
- Macro F1: 0.714
- Micro F1: 0.905
- Weighted F1: 0.890
- Macro Precision: 0.712
- Micro Precision: 0.905
- Weighted Precision: 0.887
- Macro Recall: 0.743
- Micro Recall: 0.905
- Weighted Recall: 0.905 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-gjbooth2/autotrain-data-glenn_ntsa_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3621496854
- CO2 Emissions (in grams): 7.9378 ## Validation Metrics

- Loss: 0.353
- Accuracy: 0.905
- Macro F1: 0.714
- Micro F1: 0.905
- Weighted F1: 0.890
- Macro Precision: 0.712
- Micro Precision: 0.905
- Weighted Precision: 0.887
- Macro Recall: 0.743
- Micro Recall: 0.905
- Weighted Recall: 0.905 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-gjbooth2/autotrain-data-glenn_ntsa_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3621496854
- CO2 Emissions (in grams): 7.9378## Validation Metrics

- Loss: 0.353
- Accuracy: 0.905
- Macro F1: 0.714
- Micro F1: 0.905
- Weighted F1: 0.890
- Macro Precision: 0.712
- Micro Precision: 0.905
- Weighted Precision: 0.887
- Macro Recall: 0.743
- Micro Recall: 0.905
- Weighted Recall: 0.905## Usage

You can use cURL to access this model:



Or Python API: