
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1035435583
- CO2 Emissions (in grams): 0.03608660562919794

## Validation Metrics

- Loss: 0.31551286578178406
- Accuracy: 0.8816629547141797
- Precision: 0.8965702036441586
- Recall: 0.8906042054830983
- AUC: 0.9449180200540812
- F1: 0.8935772466283884

## 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/AI-Prize-Challenges/autotrain-finetuned1-1035435583
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("AI-Prize-Challenges/autotrain-finetuned1-1035435583", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("AI-Prize-Challenges/autotrain-finetuned1-1035435583", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "zh", "tags": "autotrain", "datasets": ["AI-Prize-Challenges/autotrain-data-finetuned1"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.03608660562919794}
transformers pytorch albert text-classification autotrain zh dataset:AI-Prize-Challenges/autotrain-data-finetuned1 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #albert #text-classification #autotrain #zh #dataset-AI-Prize-Challenges/autotrain-data-finetuned1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1035435583
- CO2 Emissions (in grams): 0.03608660562919794

## Validation Metrics

- Loss: 0.31551286578178406
- Accuracy: 0.8816629547141797
- Precision: 0.8965702036441586
- Recall: 0.8906042054830983
- AUC: 0.9449180200540812
- F1: 0.8935772466283884

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1035435583
- CO2 Emissions (in grams): 0.03608660562919794 ## Validation Metrics

- Loss: 0.31551286578178406
- Accuracy: 0.8816629547141797
- Precision: 0.8965702036441586
- Recall: 0.8906042054830983
- AUC: 0.9449180200540812
- F1: 0.8935772466283884 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #albert #text-classification #autotrain #zh #dataset-AI-Prize-Challenges/autotrain-data-finetuned1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1035435583
- CO2 Emissions (in grams): 0.03608660562919794 ## Validation Metrics

- Loss: 0.31551286578178406
- Accuracy: 0.8816629547141797
- Precision: 0.8965702036441586
- Recall: 0.8906042054830983
- AUC: 0.9449180200540812
- F1: 0.8935772466283884 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #albert #text-classification #autotrain #zh #dataset-AI-Prize-Challenges/autotrain-data-finetuned1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1035435583
- CO2 Emissions (in grams): 0.03608660562919794## Validation Metrics

- Loss: 0.31551286578178406
- Accuracy: 0.8816629547141797
- Precision: 0.8965702036441586
- Recall: 0.8906042054830983
- AUC: 0.9449180200540812
- F1: 0.8935772466283884## Usage

You can use cURL to access this model:



Or Python API: