readme: ---
  language: 
    - ko
  pipeline_tag: sentence-similarity
  tags:
  - sentence-similarity
  - transformers
  license: cc-by-4.0
  datasets:
  - korsts
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  ---
  
  
  # Similarity between two sentences (fine-tuning with KoELECTRA-Small-v3 model and KorSTS 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_korsts.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
  tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/koelectra-small-v3-korsts")
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  
  
  # Huggingface pre-trained model: 'monologg/koelectra-small-v3-discriminator'
  def model_fn(model_path):
      ####
      # 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)
      model = ElectraForSequenceClassification.from_pretrained('daekeun-ml/koelectra-small-v3-korsts')
      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"]
          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)
  
          prediction_dict = {}
          prediction_dict['score'] = output[0].squeeze().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_korsts import model_fn, input_fn, predict_fn, output_fn
  >>> with open('./samples/korsts.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_korsts.py:44} INFO - input text: ['맛있는 라면을 먹고 싶어요', '후루룩 쩝쩝 후루룩 쩝쩝 맛좋은 라면']
  [{inference_korsts.py:44} INFO - input text: ['뽀로로는 내친구', '머신러닝은 러닝머신이 아닙니다.']
  [{inference_korsts.py:71} INFO - jsonline: {"score": 4.786738872528076}
  [{inference_korsts.py:71} INFO - jsonline: {"score": 0.2319069355726242}
  {"score": 4.786738872528076}
  {"score": 0.2319069355726242}
  ```
  
  ### Sample data (samples/korsts.txt)
  ```
  {"text": ["맛있는 라면을 먹고 싶어요", "후루룩 쩝쩝 후루룩 쩝쩝 맛좋은 라면"]}
  {"text": ["뽀로로는 내친구", "머신러닝은 러닝머신이 아닙니다."]}
  ```
  
  ## References
  - KoELECTRA: https://github.com/monologg/KoELECTRA
  - KorNLI and KorSTS Dataset: https://github.com/kakaobrain/KorNLUDatasets