readme: # ehdwns1516/bert-base-uncased_SWAG
  
  * This model has been trained as a [SWAG dataset](https://huggingface.co/ehdwns1516/bert-base-uncased_SWAG).
  
  * Sentence Inference Multiple Choice DEMO: [Ainize DEMO](https://main-sentence-inference-multiple-choice-ehdwns1516.endpoint.ainize.ai/)
  
  * Sentence Inference Multiple Choice API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/sentence_inference_multiple_choice)
  
  ## Overview
  
  Language model: [bert-base-uncased](https://huggingface.co/bert-base-uncased)
  
  Language: English
  
  Training data: [SWAG dataset](https://huggingface.co/datasets/swag)
  
  Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/Multiple_choice_SWAG_finetunning)
  
  ## Usage
  ## In Transformers
  
  ```
  from transformers import AutoTokenizer, AutoModelForMultipleChoice
    
  tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/bert-base-uncased_SWAG")
  
  model = AutoModelForMultipleChoice.from_pretrained("ehdwns1516/bert-base-uncased_SWAG")
  
  def run_model(candicates_count, context: str, candicates: list[str]):
      assert len(candicates) == candicates_count, "you need " + candicates_count + " candidates"
      choices_inputs = []
      for c in candicates:
          text_a = ""  # empty context
          text_b = context + " " + c
          inputs = tokenizer(
              text_a,
              text_b,
              add_special_tokens=True,
              max_length=128,
              padding="max_length",
              truncation=True,
              return_overflowing_tokens=True,
          )
          choices_inputs.append(inputs)
  
      input_ids = torch.LongTensor([x["input_ids"] for x in choices_inputs])
      output = model(input_ids=input_ids)
  
      return {"result": candicates[torch.argmax(output.logits).item()]}
  
  items = list()
  count = 4 # candicates count
  context = "your context"
  for i in range(int(count)):
      items.append("sentence")
  
  result = run_model(count, context, items)
  ```
  