
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 696121179
- CO2 Emissions (in grams): 4.355285184457145

## Validation Metrics

- Loss: 0.34467628598213196
- Accuracy: 0.8544333807491702
- Precision: 0.9014251781472684
- Recall: 0.7721261444557477
- AUC: 0.9422766967397805
- F1: 0.8317808219178082

## 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/unjustify/autotrain-commonsense_1-696121179
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("unjustify/autotrain-commonsense_1-696121179", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("unjustify/autotrain-commonsense_1-696121179", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["unjustify/autotrain-data-commonsense_1"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 4.355285184457145}
transformers pytorch roberta text-classification autotrain en dataset:unjustify/autotrain-data-commonsense_1 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-unjustify/autotrain-data-commonsense_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 696121179
- CO2 Emissions (in grams): 4.355285184457145

## Validation Metrics

- Loss: 0.34467628598213196
- Accuracy: 0.8544333807491702
- Precision: 0.9014251781472684
- Recall: 0.7721261444557477
- AUC: 0.9422766967397805
- F1: 0.8317808219178082

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 696121179
- CO2 Emissions (in grams): 4.355285184457145 ## Validation Metrics

- Loss: 0.34467628598213196
- Accuracy: 0.8544333807491702
- Precision: 0.9014251781472684
- Recall: 0.7721261444557477
- AUC: 0.9422766967397805
- F1: 0.8317808219178082 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-unjustify/autotrain-data-commonsense_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 696121179
- CO2 Emissions (in grams): 4.355285184457145 ## Validation Metrics

- Loss: 0.34467628598213196
- Accuracy: 0.8544333807491702
- Precision: 0.9014251781472684
- Recall: 0.7721261444557477
- AUC: 0.9422766967397805
- F1: 0.8317808219178082 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-unjustify/autotrain-data-commonsense_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 696121179
- CO2 Emissions (in grams): 4.355285184457145## Validation Metrics

- Loss: 0.34467628598213196
- Accuracy: 0.8544333807491702
- Precision: 0.9014251781472684
- Recall: 0.7721261444557477
- AUC: 0.9422766967397805
- F1: 0.8317808219178082## Usage

You can use cURL to access this model:



Or Python API: