readme: ---
  tags: autotrain
  language: ja
  widget:
  - text: "I love AutoTrain 🤗"
  datasets:
  - jurader/autotrain-data-livedoor_news
  co2_eq_emissions: 0.02886635131127639
  ---
  
  # 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)
  ```