
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 39629103274
- CO2 Emissions (in grams): 0.0199

## Validation Metrics

- Loss: 1.545
- Accuracy: 0.346
- Macro F1: 0.103
- Micro F1: 0.346
- Weighted F1: 0.178
- Macro Precision: 0.069
- Micro Precision: 0.346
- Weighted Precision: 0.119
- Macro Recall: 0.200
- Micro Recall: 0.346
- Weighted Recall: 0.346


## 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/yzx123/autotrain-aa-39629103274
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("yzx123/autotrain-aa-39629103274", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("yzx123/autotrain-aa-39629103274", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["zh"], "tags": ["autotrain", "text-classification"], "datasets": ["yzx123/autotrain-data-aa"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.019862830888571947}}
transformers pytorch electra text-classification autotrain zh dataset:yzx123/autotrain-data-aa co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #electra #text-classification #autotrain #zh #dataset-yzx123/autotrain-data-aa #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 39629103274
- CO2 Emissions (in grams): 0.0199

## Validation Metrics

- Loss: 1.545
- Accuracy: 0.346
- Macro F1: 0.103
- Micro F1: 0.346
- Weighted F1: 0.178
- Macro Precision: 0.069
- Micro Precision: 0.346
- Weighted Precision: 0.119
- Macro Recall: 0.200
- Micro Recall: 0.346
- Weighted Recall: 0.346


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 39629103274
- CO2 Emissions (in grams): 0.0199 ## Validation Metrics

- Loss: 1.545
- Accuracy: 0.346
- Macro F1: 0.103
- Micro F1: 0.346
- Weighted F1: 0.178
- Macro Precision: 0.069
- Micro Precision: 0.346
- Weighted Precision: 0.119
- Macro Recall: 0.200
- Micro Recall: 0.346
- Weighted Recall: 0.346 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #electra #text-classification #autotrain #zh #dataset-yzx123/autotrain-data-aa #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 39629103274
- CO2 Emissions (in grams): 0.0199 ## Validation Metrics

- Loss: 1.545
- Accuracy: 0.346
- Macro F1: 0.103
- Micro F1: 0.346
- Weighted F1: 0.178
- Macro Precision: 0.069
- Micro Precision: 0.346
- Weighted Precision: 0.119
- Macro Recall: 0.200
- Micro Recall: 0.346
- Weighted Recall: 0.346 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #electra #text-classification #autotrain #zh #dataset-yzx123/autotrain-data-aa #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 39629103274
- CO2 Emissions (in grams): 0.0199## Validation Metrics

- Loss: 1.545
- Accuracy: 0.346
- Macro F1: 0.103
- Micro F1: 0.346
- Weighted F1: 0.178
- Macro Precision: 0.069
- Micro Precision: 0.346
- Weighted Precision: 0.119
- Macro Recall: 0.200
- Micro Recall: 0.346
- Weighted Recall: 0.346## Usage

You can use cURL to access this model:



Or Python API: