readme: ---
  tags: autotrain
  language: ja
  widget:
  - text: "RustでWebAssemblyインタプリタを作った話+webassembly+rust"
  - text: "Goのロギングライブラリ 2021年冬 golang library logging go"
  - text: "VimとTUIツールをなめらかに切り替える ranger tig git vim"
  datasets:
  - vabadeh213/autotrain-data-iine_classification10
  co2_eq_emissions: 7.351885824089346
  ---
  
  # Model Trained Using AutoTrain
  
  - Problem type: Binary Classification
  - Model ID: 737422470
  - CO2 Emissions (in grams): 7.351885824089346
  
  ## Validation Metrics
  
  - Loss: 0.39456263184547424
  - Accuracy: 0.8279088689991864
  - Precision: 0.6869806094182825
  - Recall: 0.17663817663817663
  - AUC: 0.7937892215111646
  - F1: 0.2810198300283286
  
  ## 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/vabadeh213/autotrain-iine_classification10-737422470
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("vabadeh213/autotrain-iine_classification10-737422470", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("vabadeh213/autotrain-iine_classification10-737422470", use_auth_token=True)
  
  inputs = tokenizer("I love AutoTrain", return_tensors="pt")
  
  outputs = model(**inputs)
  ```