
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 828926405
- CO2 Emissions (in grams): 0.00258669198292644

## Validation Metrics

- Loss: 0.1797131597995758
- Accuracy: 0.9318181818181818
- Precision: 0.9047619047619048
- Recall: 0.95
- AUC: 0.9875
- F1: 0.9268292682926829

## 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/EAST/autotrain-maysix-828926405
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("EAST/autotrain-maysix-828926405", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("EAST/autotrain-maysix-828926405", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "zh", "tags": "autotrain", "datasets": ["EAST/autotrain-data-maysix"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.00258669198292644}
transformers pytorch bert text-classification autotrain zh dataset:EAST/autotrain-data-maysix co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #zh #dataset-EAST/autotrain-data-maysix #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 828926405
- CO2 Emissions (in grams): 0.00258669198292644

## Validation Metrics

- Loss: 0.1797131597995758
- Accuracy: 0.9318181818181818
- Precision: 0.9047619047619048
- Recall: 0.95
- AUC: 0.9875
- F1: 0.9268292682926829

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 828926405
- CO2 Emissions (in grams): 0.00258669198292644 ## Validation Metrics

- Loss: 0.1797131597995758
- Accuracy: 0.9318181818181818
- Precision: 0.9047619047619048
- Recall: 0.95
- AUC: 0.9875
- F1: 0.9268292682926829 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 828926405
- CO2 Emissions (in grams): 0.00258669198292644 ## Validation Metrics

- Loss: 0.1797131597995758
- Accuracy: 0.9318181818181818
- Precision: 0.9047619047619048
- Recall: 0.95
- AUC: 0.9875
- F1: 0.9268292682926829 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 828926405
- CO2 Emissions (in grams): 0.00258669198292644## Validation Metrics

- Loss: 0.1797131597995758
- Accuracy: 0.9318181818181818
- Precision: 0.9047619047619048
- Recall: 0.95
- AUC: 0.9875
- F1: 0.9268292682926829## Usage

You can use cURL to access this model:



Or Python API: