
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1013333726
- CO2 Emissions (in grams): 33.183779535405364

## Validation Metrics

- Loss: 0.1998898833990097
- Accuracy: 0.9226923076923077
- Precision: 0.9269808389435525
- Recall: 0.9177134068187645
- AUC: 0.9785380985232148
- F1: 0.9223238438747907

## 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/deepesh0x/autotrain-mlsec-1013333726
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("deepesh0x/autotrain-mlsec-1013333726", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("deepesh0x/autotrain-mlsec-1013333726", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["deepesh0x/autotrain-data-mlsec"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 33.183779535405364}
transformers pytorch julien text-classification autotrain en dataset:deepesh0x/autotrain-data-mlsec co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #julien #text-classification #autotrain #en #dataset-deepesh0x/autotrain-data-mlsec #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1013333726
- CO2 Emissions (in grams): 33.183779535405364

## Validation Metrics

- Loss: 0.1998898833990097
- Accuracy: 0.9226923076923077
- Precision: 0.9269808389435525
- Recall: 0.9177134068187645
- AUC: 0.9785380985232148
- F1: 0.9223238438747907

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1013333726
- CO2 Emissions (in grams): 33.183779535405364 ## Validation Metrics

- Loss: 0.1998898833990097
- Accuracy: 0.9226923076923077
- Precision: 0.9269808389435525
- Recall: 0.9177134068187645
- AUC: 0.9785380985232148
- F1: 0.9223238438747907 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #julien #text-classification #autotrain #en #dataset-deepesh0x/autotrain-data-mlsec #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1013333726
- CO2 Emissions (in grams): 33.183779535405364 ## Validation Metrics

- Loss: 0.1998898833990097
- Accuracy: 0.9226923076923077
- Precision: 0.9269808389435525
- Recall: 0.9177134068187645
- AUC: 0.9785380985232148
- F1: 0.9223238438747907 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #julien #text-classification #autotrain #en #dataset-deepesh0x/autotrain-data-mlsec #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1013333726
- CO2 Emissions (in grams): 33.183779535405364## Validation Metrics

- Loss: 0.1998898833990097
- Accuracy: 0.9226923076923077
- Precision: 0.9269808389435525
- Recall: 0.9177134068187645
- AUC: 0.9785380985232148
- F1: 0.9223238438747907## Usage

You can use cURL to access this model:



Or Python API: