
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3409393337
- CO2 Emissions (in grams): 3.6739

## Validation Metrics

- Loss: 0.244
- Accuracy: 0.909
- Precision: 0.922
- Recall: 0.875
- AUC: 0.953
- F1: 0.898

## 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/paulkm/autotrain-lottery_prod_v3-3409393337
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("paulkm/autotrain-lottery_prod_v3-3409393337", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("paulkm/autotrain-lottery_prod_v3-3409393337", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["zh"], "tags": ["autotrain", "text-classification"], "datasets": ["paulkm/autotrain-data-lottery_prod_v3"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 3.67386840637788}}
transformers pytorch bert text-classification autotrain zh dataset:paulkm/autotrain-data-lottery_prod_v3 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #zh #dataset-paulkm/autotrain-data-lottery_prod_v3 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3409393337
- CO2 Emissions (in grams): 3.6739

## Validation Metrics

- Loss: 0.244
- Accuracy: 0.909
- Precision: 0.922
- Recall: 0.875
- AUC: 0.953
- F1: 0.898

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3409393337
- CO2 Emissions (in grams): 3.6739 ## Validation Metrics

- Loss: 0.244
- Accuracy: 0.909
- Precision: 0.922
- Recall: 0.875
- AUC: 0.953
- F1: 0.898 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #zh #dataset-paulkm/autotrain-data-lottery_prod_v3 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3409393337
- CO2 Emissions (in grams): 3.6739 ## Validation Metrics

- Loss: 0.244
- Accuracy: 0.909
- Precision: 0.922
- Recall: 0.875
- AUC: 0.953
- F1: 0.898 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #zh #dataset-paulkm/autotrain-data-lottery_prod_v3 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3409393337
- CO2 Emissions (in grams): 3.6739## Validation Metrics

- Loss: 0.244
- Accuracy: 0.909
- Precision: 0.922
- Recall: 0.875
- AUC: 0.953
- F1: 0.898## Usage

You can use cURL to access this model:



Or Python API: