
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 48023117161
- CO2 Emissions (in grams): 0.4467

## Validation Metrics

- Loss: 0.300
- Accuracy: 0.922
- Macro F1: 0.921
- Micro F1: 0.922
- Weighted F1: 0.922
- Macro Precision: 0.921
- Micro Precision: 0.922
- Weighted Precision: 0.922
- Macro Recall: 0.921
- Micro Recall: 0.922
- Weighted Recall: 0.922


## 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/billster45/autotrain-agnews-48023117161
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("billster45/autotrain-agnews-48023117161", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("billster45/autotrain-agnews-48023117161", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["billster45/autotrain-data-agnews"], "widget": [{"text": "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\band of ultra-cynics, are seeing green again."}], "co2_eq_emissions": {"emissions": 0.44669471536805566}}
transformers pytorch bert text-classification autotrain en dataset:billster45/autotrain-data-agnews co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-billster45/autotrain-data-agnews #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 48023117161
- CO2 Emissions (in grams): 0.4467

## Validation Metrics

- Loss: 0.300
- Accuracy: 0.922
- Macro F1: 0.921
- Micro F1: 0.922
- Weighted F1: 0.922
- Macro Precision: 0.921
- Micro Precision: 0.922
- Weighted Precision: 0.922
- Macro Recall: 0.921
- Micro Recall: 0.922
- Weighted Recall: 0.922


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 48023117161
- CO2 Emissions (in grams): 0.4467 ## Validation Metrics

- Loss: 0.300
- Accuracy: 0.922
- Macro F1: 0.921
- Micro F1: 0.922
- Weighted F1: 0.922
- Macro Precision: 0.921
- Micro Precision: 0.922
- Weighted Precision: 0.922
- Macro Recall: 0.921
- Micro Recall: 0.922
- Weighted Recall: 0.922 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-billster45/autotrain-data-agnews #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 48023117161
- CO2 Emissions (in grams): 0.4467 ## Validation Metrics

- Loss: 0.300
- Accuracy: 0.922
- Macro F1: 0.921
- Micro F1: 0.922
- Weighted F1: 0.922
- Macro Precision: 0.921
- Micro Precision: 0.922
- Weighted Precision: 0.922
- Macro Recall: 0.921
- Micro Recall: 0.922
- Weighted Recall: 0.922 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-billster45/autotrain-data-agnews #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 48023117161
- CO2 Emissions (in grams): 0.4467## Validation Metrics

- Loss: 0.300
- Accuracy: 0.922
- Macro F1: 0.921
- Micro F1: 0.922
- Weighted F1: 0.922
- Macro Precision: 0.921
- Micro Precision: 0.922
- Weighted Precision: 0.922
- Macro Recall: 0.921
- Micro Recall: 0.922
- Weighted Recall: 0.922## Usage

You can use cURL to access this model:



Or Python API: