
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1154042511
- CO2 Emissions (in grams): 50.942111222257715

## Validation Metrics

- Loss: 0.7391096353530884
- Accuracy: 0.672331747110809
- Macro F1: 0.5302988889038903
- Micro F1: 0.672331747110809
- Weighted F1: 0.628333699928783
- Macro Precision: 0.6916740835116003
- Micro Precision: 0.672331747110809
- Weighted Precision: 0.6810625595246631
- Macro Recall: 0.5264405918447705
- Micro Recall: 0.672331747110809
- Weighted Recall: 0.672331747110809


## 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/snap/autotrain-argument-feedback-1154042511
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("snap/autotrain-argument-feedback-1154042511", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("snap/autotrain-argument-feedback-1154042511", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["snap/autotrain-data-argument-feedback"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 50.942111222257715}
transformers pytorch bert text-classification autotrain en dataset:snap/autotrain-data-argument-feedback co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-snap/autotrain-data-argument-feedback #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1154042511
- CO2 Emissions (in grams): 50.942111222257715

## Validation Metrics

- Loss: 0.7391096353530884
- Accuracy: 0.672331747110809
- Macro F1: 0.5302988889038903
- Micro F1: 0.672331747110809
- Weighted F1: 0.628333699928783
- Macro Precision: 0.6916740835116003
- Micro Precision: 0.672331747110809
- Weighted Precision: 0.6810625595246631
- Macro Recall: 0.5264405918447705
- Micro Recall: 0.672331747110809
- Weighted Recall: 0.672331747110809


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1154042511
- CO2 Emissions (in grams): 50.942111222257715 ## Validation Metrics

- Loss: 0.7391096353530884
- Accuracy: 0.672331747110809
- Macro F1: 0.5302988889038903
- Micro F1: 0.672331747110809
- Weighted F1: 0.628333699928783
- Macro Precision: 0.6916740835116003
- Micro Precision: 0.672331747110809
- Weighted Precision: 0.6810625595246631
- Macro Recall: 0.5264405918447705
- Micro Recall: 0.672331747110809
- Weighted Recall: 0.672331747110809 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 1154042511
- CO2 Emissions (in grams): 50.942111222257715 ## Validation Metrics

- Loss: 0.7391096353530884
- Accuracy: 0.672331747110809
- Macro F1: 0.5302988889038903
- Micro F1: 0.672331747110809
- Weighted F1: 0.628333699928783
- Macro Precision: 0.6916740835116003
- Micro Precision: 0.672331747110809
- Weighted Precision: 0.6810625595246631
- Macro Recall: 0.5264405918447705
- Micro Recall: 0.672331747110809
- Weighted Recall: 0.672331747110809 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 1154042511
- CO2 Emissions (in grams): 50.942111222257715## Validation Metrics

- Loss: 0.7391096353530884
- Accuracy: 0.672331747110809
- Macro F1: 0.5302988889038903
- Micro F1: 0.672331747110809
- Weighted F1: 0.628333699928783
- Macro Precision: 0.6916740835116003
- Micro Precision: 0.672331747110809
- Weighted Precision: 0.6810625595246631
- Macro Recall: 0.5264405918447705
- Micro Recall: 0.672331747110809
- Weighted Recall: 0.672331747110809## Usage

You can use cURL to access this model:



Or Python API: