
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 441411446
- CO2 Emissions (in grams): 0.4362732160754736

## Validation Metrics

- Loss: 0.7598486542701721
- Accuracy: 0.8222222222222222
- Macro F1: 0.2912091747693842
- Micro F1: 0.8222222222222222
- Weighted F1: 0.7707160863181806
- Macro Precision: 0.29631463146314635
- Micro Precision: 0.8222222222222222
- Weighted Precision: 0.7341339689524508
- Macro Recall: 0.30174603174603176
- Micro Recall: 0.8222222222222222
- Weighted Recall: 0.8222222222222222


## 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/alecmullen/autonlp-group-classification-441411446
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("alecmullen/autonlp-group-classification-441411446", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("alecmullen/autonlp-group-classification-441411446", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autonlp", "datasets": ["alecmullen/autonlp-data-group-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 0.4362732160754736}
transformers pytorch roberta text-classification autonlp en dataset:alecmullen/autonlp-data-group-classification co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-alecmullen/autonlp-data-group-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 441411446
- CO2 Emissions (in grams): 0.4362732160754736

## Validation Metrics

- Loss: 0.7598486542701721
- Accuracy: 0.8222222222222222
- Macro F1: 0.2912091747693842
- Micro F1: 0.8222222222222222
- Weighted F1: 0.7707160863181806
- Macro Precision: 0.29631463146314635
- Micro Precision: 0.8222222222222222
- Weighted Precision: 0.7341339689524508
- Macro Recall: 0.30174603174603176
- Micro Recall: 0.8222222222222222
- Weighted Recall: 0.8222222222222222


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 441411446
- CO2 Emissions (in grams): 0.4362732160754736 ## Validation Metrics

- Loss: 0.7598486542701721
- Accuracy: 0.8222222222222222
- Macro F1: 0.2912091747693842
- Micro F1: 0.8222222222222222
- Weighted F1: 0.7707160863181806
- Macro Precision: 0.29631463146314635
- Micro Precision: 0.8222222222222222
- Weighted Precision: 0.7341339689524508
- Macro Recall: 0.30174603174603176
- Micro Recall: 0.8222222222222222
- Weighted Recall: 0.8222222222222222 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-alecmullen/autonlp-data-group-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 441411446
- CO2 Emissions (in grams): 0.4362732160754736 ## Validation Metrics

- Loss: 0.7598486542701721
- Accuracy: 0.8222222222222222
- Macro F1: 0.2912091747693842
- Micro F1: 0.8222222222222222
- Weighted F1: 0.7707160863181806
- Macro Precision: 0.29631463146314635
- Micro Precision: 0.8222222222222222
- Weighted Precision: 0.7341339689524508
- Macro Recall: 0.30174603174603176
- Micro Recall: 0.8222222222222222
- Weighted Recall: 0.8222222222222222 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-alecmullen/autonlp-data-group-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 441411446
- CO2 Emissions (in grams): 0.4362732160754736## Validation Metrics

- Loss: 0.7598486542701721
- Accuracy: 0.8222222222222222
- Macro F1: 0.2912091747693842
- Micro F1: 0.8222222222222222
- Weighted F1: 0.7707160863181806
- Macro Precision: 0.29631463146314635
- Micro Precision: 0.8222222222222222
- Weighted Precision: 0.7341339689524508
- Macro Recall: 0.30174603174603176
- Micro Recall: 0.8222222222222222
- Weighted Recall: 0.8222222222222222## Usage

You can use cURL to access this model:



Or Python API: