README.md

metadata

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)