
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 614417501
- CO2 Emissions (in grams): 1.6912535041856878

## Validation Metrics

- Loss: 1.305419921875
- Accuracy: 0.5
- Macro F1: 0.3333333333333333
- Micro F1: 0.5
- Weighted F1: 0.4444444444444444
- Macro Precision: 0.375
- Micro Precision: 0.5
- Weighted Precision: 0.5
- Macro Recall: 0.375
- Micro Recall: 0.5
- Weighted Recall: 0.5


## 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 AutoNLP"}' https://api-inference.huggingface.co/models/billfrench/autonlp-cyberlandr-ai-4-614417501
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("billfrench/autonlp-cyberlandr-ai-4-614417501", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("billfrench/autonlp-cyberlandr-ai-4-614417501", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autonlp", "datasets": ["billfrench/autonlp-data-cyberlandr-ai-4"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.6912535041856878}
transformers pytorch bert text-classification autonlp en dataset:billfrench/autonlp-data-cyberlandr-ai-4 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-billfrench/autonlp-data-cyberlandr-ai-4 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 614417501
- CO2 Emissions (in grams): 1.6912535041856878

## Validation Metrics

- Loss: 1.305419921875
- Accuracy: 0.5
- Macro F1: 0.3333333333333333
- Micro F1: 0.5
- Weighted F1: 0.4444444444444444
- Macro Precision: 0.375
- Micro Precision: 0.5
- Weighted Precision: 0.5
- Macro Recall: 0.375
- Micro Recall: 0.5
- Weighted Recall: 0.5


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 614417501
- CO2 Emissions (in grams): 1.6912535041856878 ## Validation Metrics

- Loss: 1.305419921875
- Accuracy: 0.5
- Macro F1: 0.3333333333333333
- Micro F1: 0.5
- Weighted F1: 0.4444444444444444
- Macro Precision: 0.375
- Micro Precision: 0.5
- Weighted Precision: 0.5
- Macro Recall: 0.375
- Micro Recall: 0.5
- Weighted Recall: 0.5 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-billfrench/autonlp-data-cyberlandr-ai-4 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 614417501
- CO2 Emissions (in grams): 1.6912535041856878 ## Validation Metrics

- Loss: 1.305419921875
- Accuracy: 0.5
- Macro F1: 0.3333333333333333
- Micro F1: 0.5
- Weighted F1: 0.4444444444444444
- Macro Precision: 0.375
- Micro Precision: 0.5
- Weighted Precision: 0.5
- Macro Recall: 0.375
- Micro Recall: 0.5
- Weighted Recall: 0.5 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-billfrench/autonlp-data-cyberlandr-ai-4 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 614417501
- CO2 Emissions (in grams): 1.6912535041856878## Validation Metrics

- Loss: 1.305419921875
- Accuracy: 0.5
- Macro F1: 0.3333333333333333
- Micro F1: 0.5
- Weighted F1: 0.4444444444444444
- Macro Precision: 0.375
- Micro Precision: 0.5
- Weighted Precision: 0.5
- Macro Recall: 0.375
- Micro Recall: 0.5
- Weighted Recall: 0.5## Usage

You can use cURL to access this model:



Or Python API: