readme: ---
  language: ko
  tags:
  - intent-classification
  datasets:
  - kor_3i4k
  license: cc-by-nc-4.0
  ---
  
  ## Finetuning
  - Pretrain Model : [klue/roberta-small](https://github.com/KLUE-benchmark/KLUE)
  - Dataset for fine-tuning : [3i4k](https://github.com/warnikchow/3i4k) 
    - Train : 46,863
    - Validation : 8,271 (15% of Train)
    - Test : 6,121
  - Label info 
    - 0: "fragment",
    - 1: "statement",
    - 2: "question",
    - 3: "command",
    - 4: "rhetorical question",
    - 5: "rhetorical command",
    - 6: "intonation-dependent utterance"
  - Parameters of Training
  ```
  {
      "epochs": 3 (setting 10 but early stopped),
      "batch_size":32,
      "optimizer_class": "<keras.optimizer_v2.adam.Adam'>",
      "optimizer_params": {
          "lr": 5e-05
      },
      "min_delta": 0.01
  }
  ```
  
  ## Usage
  ``` python
  from transformers import RobertaTokenizerFast, RobertaForSequenceClassification, TextClassificationPipeline
  
  # Load fine-tuned MRC model by HuggingFace Model Hub
  HUGGINGFACE_MODEL_PATH = "bespin-global/klue-roberta-small-3i4k-intent-classification"
  loaded_tokenizer = RobertaTokenizerFast.from_pretrained(HUGGINGFACE_MODEL_PATH )
  loaded_model = RobertaForSequenceClassification.from_pretrained(HUGGINGFACE_MODEL_PATH )
  
  # using Pipeline
  text_classifier = TextClassificationPipeline(
      tokenizer=loaded_tokenizer, 
      model=loaded_model, 
      return_all_scores=True
  )
  
  # predict
  text = "your text"
  
  preds_list = text_classifier(text)
  best_pred = preds_list[0]
  print(f"Label of Best Intentatioin: {best_pred['label']}")
  print(f"Score of Best Intentatioin: {best_pred['score']}")
  ```
  
  ## Evaluation
  ```
                                 precision    recall  f1-score   support
  
                        command       0.89      0.92      0.90      1296
                       fragment       0.98      0.96      0.97       600
  intonation-depedent utterance       0.71      0.69      0.70       327
                       question       0.95      0.97      0.96      1786
             rhetorical command       0.87      0.64      0.74       108
            rhetorical question       0.61      0.63      0.62       174
                      statement       0.91      0.89      0.90      1830
  
                       accuracy                           0.90      6121
                      macro avg       0.85      0.81      0.83      6121
                   weighted avg       0.90      0.90      0.90      6121
  ```
  
  
  ## Citing & Authors
  <!--- Describe where people can find more information -->
  [Jaehyeong](https://huggingface.co/jaehyeong) at [Bespin Global](https://www.bespinglobal.com/)