readme: ---
  language: 
    - ko
  tags:
  - classification
  license: mit
  datasets:
  - nsmc
  widget:
  - text: "불후의 명작입니다! 이렇게 감동적인 내용은 처음이에요"
    example_title: "Positive"
  - text: "시간이 정말 아깝습니다. 10점 만점에 1점도 아까워요.."
    example_title: "Negative"
  metrics:
  - accuracy
  - f1
  - precision
  - recall- accuracy
  ---
  
  # Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus dataset)
  
  ## Usage (Amazon SageMaker inference applicable)
  It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.
  
  ### inference_nsmc.py
  
  ```python
  import json
  import sys
  import logging
  import torch
  from torch import nn
  from transformers import ElectraConfig
  from transformers import ElectraModel, AutoTokenizer, ElectraTokenizer, ElectraForSequenceClassification
  
  logging.basicConfig(
      level=logging.INFO, 
      format='[{%(filename)s:%(lineno)d} %(levelname)s - %(message)s',
      handlers=[
          logging.FileHandler(filename='tmp.log'),
          logging.StreamHandler(sys.stdout)
      ]
  )
  logger = logging.getLogger(__name__)
  
  max_seq_length = 128
  classes = ['Neg', 'Pos']
  
  tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/koelectra-small-v3-nsmc")
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  
  
  def model_fn(model_path=None):
      ####
      # If you have your own trained model
      # Huggingface pre-trained model: 'monologg/koelectra-small-v3-discriminator'
      ####
      #config = ElectraConfig.from_json_file(f'{model_path}/config.json')
      #model = ElectraForSequenceClassification.from_pretrained(f'{model_path}/model.pth', config=config)
      
      # Download model from the Huggingface hub
      model = ElectraForSequenceClassification.from_pretrained('daekeun-ml/koelectra-small-v3-nsmc')   
      model.to(device)
      return model
  
  
  def input_fn(input_data, content_type="application/jsonlines"): 
      data_str = input_data.decode("utf-8")
      jsonlines = data_str.split("\n")
      transformed_inputs = []
  
      for jsonline in jsonlines:
          text = json.loads(jsonline)["text"][0]
          logger.info("input text: {}".format(text))          
          encode_plus_token = tokenizer.encode_plus(
              text,
              max_length=max_seq_length,
              add_special_tokens=True,
              return_token_type_ids=False,
              padding="max_length",
              return_attention_mask=True,
              return_tensors="pt",
              truncation=True,
          )
          transformed_inputs.append(encode_plus_token)
          
      return transformed_inputs
  
  
  def predict_fn(transformed_inputs, model):
      predicted_classes = []
      
      for data in transformed_inputs:
          data = data.to(device)
          output = model(**data)
  
          softmax_fn = nn.Softmax(dim=1)
          softmax_output = softmax_fn(output[0])
          _, prediction = torch.max(softmax_output, dim=1)
  
          predicted_class_idx = prediction.item()
          predicted_class = classes[predicted_class_idx]
          score = softmax_output[0][predicted_class_idx]
          logger.info("predicted_class: {}".format(predicted_class))
  
          prediction_dict = {}
          prediction_dict["predicted_label"] = predicted_class
          prediction_dict['score'] = score.cpu().detach().numpy().tolist()
  
          jsonline = json.dumps(prediction_dict)
          logger.info("jsonline: {}".format(jsonline))        
          predicted_classes.append(jsonline)
  
      predicted_classes_jsonlines = "\n".join(predicted_classes)
      return predicted_classes_jsonlines
  
  
  def output_fn(outputs, accept="application/jsonlines"):
      return outputs, accept
  ```    
  
  ### test.py 
  ```python
  >>> from inference_nsmc import model_fn, input_fn, predict_fn, output_fn
  >>> with open('samples/nsmc.txt', mode='rb') as file:
  >>>     model_input_data = file.read()
  >>> model = model_fn()
  >>> transformed_inputs = input_fn(model_input_data)
  >>> predicted_classes_jsonlines = predict_fn(transformed_inputs, model)
  >>> model_outputs = output_fn(predicted_classes_jsonlines)
  >>> print(model_outputs[0])    
     
  [{inference_nsmc.py:47} INFO - input text: 이 영화는 최고의 영화입니다
  [{inference_nsmc.py:47} INFO - input text: 최악이에요. 배우의 연기력도 좋지 않고 내용도 너무 허접합니다
  [{inference_nsmc.py:77} INFO - predicted_class: Pos
  [{inference_nsmc.py:84} INFO - jsonline: {"predicted_label": "Pos", "score": 0.9619030952453613}
  [{inference_nsmc.py:77} INFO - predicted_class: Neg
  [{inference_nsmc.py:84} INFO - jsonline: {"predicted_label": "Neg", "score": 0.9994170665740967}
  {"predicted_label": "Pos", "score": 0.9619030952453613}
  {"predicted_label": "Neg", "score": 0.9994170665740967}
  ```
  
  ### Sample data (samples/nsmc.txt)
  ```
  {"text": ["이 영화는 최고의 영화입니다"]}
  {"text": ["최악이에요. 배우의 연기력도 좋지 않고 내용도 너무 허접합니다"]}
  ```
  
  ## References
  - KoELECTRA: https://github.com/monologg/KoELECTRA
  - Naver Sentiment Movie Corpus Dataset: https://github.com/e9t/nsmc