
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1241547318
- CO2 Emissions (in grams): 151.9730

## Validation Metrics

- Loss: 0.512
- Accuracy: 0.862
- Macro F1: 0.862
- Micro F1: 0.862
- Weighted F1: 0.862
- Macro Precision: 0.863
- Micro Precision: 0.862
- Weighted Precision: 0.863
- Macro Recall: 0.862
- Micro Recall: 0.862
- Weighted Recall: 0.862


## 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/yuan1729/autotrain-YuAN-lawthone-CL_facts_backTrans-1241547318
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("yuan1729/autotrain-YuAN-lawthone-CL_facts_backTrans-1241547318", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("yuan1729/autotrain-YuAN-lawthone-CL_facts_backTrans-1241547318", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["zh"], "tags": ["autotrain", "text-classification"], "datasets": ["yuan1729/autotrain-data-YuAN-lawthone-CL_facts_backTrans"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 151.97297148175758}}
transformers pytorch bert text-classification autotrain zh dataset:yuan1729/autotrain-data-YuAN-lawthone-CL_facts_backTrans co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #zh #dataset-yuan1729/autotrain-data-YuAN-lawthone-CL_facts_backTrans #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1241547318
- CO2 Emissions (in grams): 151.9730

## Validation Metrics

- Loss: 0.512
- Accuracy: 0.862
- Macro F1: 0.862
- Micro F1: 0.862
- Weighted F1: 0.862
- Macro Precision: 0.863
- Micro Precision: 0.862
- Weighted Precision: 0.863
- Macro Recall: 0.862
- Micro Recall: 0.862
- Weighted Recall: 0.862


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1241547318
- CO2 Emissions (in grams): 151.9730 ## Validation Metrics

- Loss: 0.512
- Accuracy: 0.862
- Macro F1: 0.862
- Micro F1: 0.862
- Weighted F1: 0.862
- Macro Precision: 0.863
- Micro Precision: 0.862
- Weighted Precision: 0.863
- Macro Recall: 0.862
- Micro Recall: 0.862
- Weighted Recall: 0.862 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #zh #dataset-yuan1729/autotrain-data-YuAN-lawthone-CL_facts_backTrans #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1241547318
- CO2 Emissions (in grams): 151.9730 ## Validation Metrics

- Loss: 0.512
- Accuracy: 0.862
- Macro F1: 0.862
- Micro F1: 0.862
- Weighted F1: 0.862
- Macro Precision: 0.863
- Micro Precision: 0.862
- Weighted Precision: 0.863
- Macro Recall: 0.862
- Micro Recall: 0.862
- Weighted Recall: 0.862 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #zh #dataset-yuan1729/autotrain-data-YuAN-lawthone-CL_facts_backTrans #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1241547318
- CO2 Emissions (in grams): 151.9730## Validation Metrics

- Loss: 0.512
- Accuracy: 0.862
- Macro F1: 0.862
- Micro F1: 0.862
- Weighted F1: 0.862
- Macro Precision: 0.863
- Micro Precision: 0.862
- Weighted Precision: 0.863
- Macro Recall: 0.862
- Micro Recall: 0.862
- Weighted Recall: 0.862## Usage

You can use cURL to access this model:



Or Python API: