
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 732022289
- CO2 Emissions (in grams): 0.02886635131127639

## Validation Metrics

- Loss: 0.19849611818790436
- Accuracy: 0.9471186440677966
- Macro F1: 0.9441816841379956
- Micro F1: 0.9471186440677966
- Weighted F1: 0.9470801715002611
- Macro Precision: 0.945983665608131
- Micro Precision: 0.9471186440677966
- Weighted Precision: 0.9475574732458715
- Macro Recall: 0.9429694962141204
- Micro Recall: 0.9471186440677966
- Weighted Recall: 0.9471186440677966


## 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/jurader/autotrain-livedoor_news-732022289
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("jurader/autotrain-livedoor_news-732022289", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("jurader/autotrain-livedoor_news-732022289", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "ja", "tags": "autotrain", "datasets": ["jurader/autotrain-data-livedoor_news"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.02886635131127639}
transformers pytorch bert text-classification autotrain ja dataset:jurader/autotrain-data-livedoor_news co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #ja #dataset-jurader/autotrain-data-livedoor_news #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 732022289
- CO2 Emissions (in grams): 0.02886635131127639

## Validation Metrics

- Loss: 0.19849611818790436
- Accuracy: 0.9471186440677966
- Macro F1: 0.9441816841379956
- Micro F1: 0.9471186440677966
- Weighted F1: 0.9470801715002611
- Macro Precision: 0.945983665608131
- Micro Precision: 0.9471186440677966
- Weighted Precision: 0.9475574732458715
- Macro Recall: 0.9429694962141204
- Micro Recall: 0.9471186440677966
- Weighted Recall: 0.9471186440677966


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 732022289
- CO2 Emissions (in grams): 0.02886635131127639 ## Validation Metrics

- Loss: 0.19849611818790436
- Accuracy: 0.9471186440677966
- Macro F1: 0.9441816841379956
- Micro F1: 0.9471186440677966
- Weighted F1: 0.9470801715002611
- Macro Precision: 0.945983665608131
- Micro Precision: 0.9471186440677966
- Weighted Precision: 0.9475574732458715
- Macro Recall: 0.9429694962141204
- Micro Recall: 0.9471186440677966
- Weighted Recall: 0.9471186440677966 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 732022289
- CO2 Emissions (in grams): 0.02886635131127639 ## Validation Metrics

- Loss: 0.19849611818790436
- Accuracy: 0.9471186440677966
- Macro F1: 0.9441816841379956
- Micro F1: 0.9471186440677966
- Weighted F1: 0.9470801715002611
- Macro Precision: 0.945983665608131
- Micro Precision: 0.9471186440677966
- Weighted Precision: 0.9475574732458715
- Macro Recall: 0.9429694962141204
- Micro Recall: 0.9471186440677966
- Weighted Recall: 0.9471186440677966 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 732022289
- CO2 Emissions (in grams): 0.02886635131127639## Validation Metrics

- Loss: 0.19849611818790436
- Accuracy: 0.9471186440677966
- Macro F1: 0.9441816841379956
- Micro F1: 0.9471186440677966
- Weighted F1: 0.9470801715002611
- Macro Precision: 0.945983665608131
- Micro Precision: 0.9471186440677966
- Weighted Precision: 0.9475574732458715
- Macro Recall: 0.9429694962141204
- Micro Recall: 0.9471186440677966
- Weighted Recall: 0.9471186440677966## Usage

You can use cURL to access this model:



Or Python API: