
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 51372121776
- CO2 Emissions (in grams): 1.1892

## Validation Metrics

- Loss: 1.324
- Accuracy: 0.570
- Macro F1: 0.474
- Micro F1: 0.570
- Weighted F1: 0.561
- Macro Precision: 0.506
- Micro Precision: 0.570
- Weighted Precision: 0.564
- Macro Recall: 0.462
- Micro Recall: 0.570
- Weighted Recall: 0.570


## 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/claudio-cyberg0n/autotrain-cve-sa-numeriarrotondati-51372121776
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("claudio-cyberg0n/autotrain-cve-sa-numeriarrotondati-51372121776", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("claudio-cyberg0n/autotrain-cve-sa-numeriarrotondati-51372121776", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["claudio-cyberg0n/autotrain-data-cve-sa-numeriarrotondati"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 1.189216119158559}}
transformers pytorch bert text-classification autotrain en dataset:claudio-cyberg0n/autotrain-data-cve-sa-numeriarrotondati co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-claudio-cyberg0n/autotrain-data-cve-sa-numeriarrotondati #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 51372121776
- CO2 Emissions (in grams): 1.1892

## Validation Metrics

- Loss: 1.324
- Accuracy: 0.570
- Macro F1: 0.474
- Micro F1: 0.570
- Weighted F1: 0.561
- Macro Precision: 0.506
- Micro Precision: 0.570
- Weighted Precision: 0.564
- Macro Recall: 0.462
- Micro Recall: 0.570
- Weighted Recall: 0.570


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 51372121776
- CO2 Emissions (in grams): 1.1892 ## Validation Metrics

- Loss: 1.324
- Accuracy: 0.570
- Macro F1: 0.474
- Micro F1: 0.570
- Weighted F1: 0.561
- Macro Precision: 0.506
- Micro Precision: 0.570
- Weighted Precision: 0.564
- Macro Recall: 0.462
- Micro Recall: 0.570
- Weighted Recall: 0.570 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-claudio-cyberg0n/autotrain-data-cve-sa-numeriarrotondati #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 51372121776
- CO2 Emissions (in grams): 1.1892 ## Validation Metrics

- Loss: 1.324
- Accuracy: 0.570
- Macro F1: 0.474
- Micro F1: 0.570
- Weighted F1: 0.561
- Macro Precision: 0.506
- Micro Precision: 0.570
- Weighted Precision: 0.564
- Macro Recall: 0.462
- Micro Recall: 0.570
- Weighted Recall: 0.570 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-claudio-cyberg0n/autotrain-data-cve-sa-numeriarrotondati #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 51372121776
- CO2 Emissions (in grams): 1.1892## Validation Metrics

- Loss: 1.324
- Accuracy: 0.570
- Macro F1: 0.474
- Micro F1: 0.570
- Weighted F1: 0.561
- Macro Precision: 0.506
- Micro Precision: 0.570
- Weighted Precision: 0.564
- Macro Recall: 0.462
- Micro Recall: 0.570
- Weighted Recall: 0.570## Usage

You can use cURL to access this model:



Or Python API: