
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 722922024
- CO2 Emissions (in grams): 0.019299491458156143

## Validation Metrics

- Loss: 0.19609540700912476
- Accuracy: 0.9457627118644067
- Macro F1: 0.9404319054946133
- Micro F1: 0.9457627118644067
- Weighted F1: 0.9456037443251943
- Macro Precision: 0.9420917371721244
- Micro Precision: 0.9457627118644067
- Weighted Precision: 0.9457910238180336
- Macro Recall: 0.9391783746329772
- Micro Recall: 0.9457627118644067
- Weighted Recall: 0.9457627118644067


## 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/jicoc22578/autotrain-livedoor_news-722922024
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("jicoc22578/autotrain-livedoor_news-722922024", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("jicoc22578/autotrain-livedoor_news-722922024", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "ja", "tags": "autotrain", "datasets": ["jicoc22578/autotrain-data-livedoor_news"], "widget": [{"text": "Windows 11\u642d\u8f09PC\u3092\u8cb7\u3063\u305f\u3089\u6700\u4f4e\u9650\u3084\u3063\u3066\u304a\u304d\u305f\u3044\u3053\u3068"}, {"text": "3\u6708\u30c7\u30b9\u30af\u30c8\u30c3\u30d7OS\u30b7\u30a7\u30a2\u3001Windows\u304c\u5897\u52a0\u3057Mac\u304c\u6e1b\u5c11"}, {"text": "raytrek\u3001Core i7-12700H\u3068RTX 3070 Ti\u3092\u642d\u8f09\u3059\u308b\u30ce\u30fc\u30c8PC"}], "co2_eq_emissions": 0.019299491458156143}
transformers pytorch bert text-classification autotrain ja dataset:jicoc22578/autotrain-data-livedoor_news co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #ja #dataset-jicoc22578/autotrain-data-livedoor_news #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 722922024
- CO2 Emissions (in grams): 0.019299491458156143

## Validation Metrics

- Loss: 0.19609540700912476
- Accuracy: 0.9457627118644067
- Macro F1: 0.9404319054946133
- Micro F1: 0.9457627118644067
- Weighted F1: 0.9456037443251943
- Macro Precision: 0.9420917371721244
- Micro Precision: 0.9457627118644067
- Weighted Precision: 0.9457910238180336
- Macro Recall: 0.9391783746329772
- Micro Recall: 0.9457627118644067
- Weighted Recall: 0.9457627118644067


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 722922024
- CO2 Emissions (in grams): 0.019299491458156143 ## Validation Metrics

- Loss: 0.19609540700912476
- Accuracy: 0.9457627118644067
- Macro F1: 0.9404319054946133
- Micro F1: 0.9457627118644067
- Weighted F1: 0.9456037443251943
- Macro Precision: 0.9420917371721244
- Micro Precision: 0.9457627118644067
- Weighted Precision: 0.9457910238180336
- Macro Recall: 0.9391783746329772
- Micro Recall: 0.9457627118644067
- Weighted Recall: 0.9457627118644067 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #ja #dataset-jicoc22578/autotrain-data-livedoor_news #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 722922024
- CO2 Emissions (in grams): 0.019299491458156143 ## Validation Metrics

- Loss: 0.19609540700912476
- Accuracy: 0.9457627118644067
- Macro F1: 0.9404319054946133
- Micro F1: 0.9457627118644067
- Weighted F1: 0.9456037443251943
- Macro Precision: 0.9420917371721244
- Micro Precision: 0.9457627118644067
- Weighted Precision: 0.9457910238180336
- Macro Recall: 0.9391783746329772
- Micro Recall: 0.9457627118644067
- Weighted Recall: 0.9457627118644067 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #ja #dataset-jicoc22578/autotrain-data-livedoor_news #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 722922024
- CO2 Emissions (in grams): 0.019299491458156143## Validation Metrics

- Loss: 0.19609540700912476
- Accuracy: 0.9457627118644067
- Macro F1: 0.9404319054946133
- Micro F1: 0.9457627118644067
- Weighted F1: 0.9456037443251943
- Macro Precision: 0.9420917371721244
- Micro Precision: 0.9457627118644067
- Weighted Precision: 0.9457910238180336
- Macro Recall: 0.9391783746329772
- Micro Recall: 0.9457627118644067
- Weighted Recall: 0.9457627118644067## Usage

You can use cURL to access this model:



Or Python API: