readme: ---
  language: en
  tags:
  - generative qa
  datasets:
  - eli5
  - stackexchange(pets, cooking, gardening, diy, crafts)
  ---
  
  Work by [Frederico Vicente](https://huggingface.co/mrvicente) & [Diogo Tavares](https://huggingface.co/d-c-t). We finetuned BART Large for the task of generative question answering. It was trained on eli5, askScience and stackexchange using the following forums: pets, cooking, gardening, diy, crafts. 
  
  Check demo: https://huggingface.co/spaces/unlisboa/bart_qa_assistant
  
  ### Usage
  
  ```python
  
  from transformers import (
        BartForConditionalGeneration,
        BartTokenizer
  )
  import torch
  import json
  
  def read_json_file_2_dict(filename, store_dir='.'):
      with open(f'{store_dir}/{filename}', 'r', encoding='utf-8') as file:
          return json.load(file)
  
  def get_device():
      # If there's a GPU available...
      if torch.cuda.is_available():
          device = torch.device("cuda")
          n_gpus = torch.cuda.device_count()
          first_gpu = torch.cuda.get_device_name(0)
  
          print(f'There are {n_gpus} GPU(s) available.')
          print(f'GPU gonna be used: {first_gpu}')
      else:
          print('No GPU available, using the CPU instead.')
          device = torch.device("cpu")
      return device
  
  model_name = 'unlisboa/bart_qa_assistant'
  tokenizer = BartTokenizer.from_pretrained(model_name)
  device = get_device()
  model = BartForConditionalGeneration.from_pretrained(model_name).to(device)
  model.eval()
                                                                                                                                                            
  model_input = tokenizer(question, truncation=True, padding=True, return_tensors="pt")
  generated_answers_encoded = model.generate(input_ids=model_input["input_ids"].to(device),attention_mask=model_input["attention_mask"].to(device),
                                                                                        force_words_ids=None,
                                                                                        min_length=1,
                                                                                        max_length=100,
                                                                                        do_sample=True,
                                                                                        early_stopping=True,
                                                                                        num_beams=4,
                                                                                        temperature=1.0,
                                                                                        top_k=None,
                                                                                        top_p=None,
                                                                                        # eos_token_id=tokenizer.eos_token_id,
                                                                                        no_repeat_ngram_size=2,
                                                                                        num_return_sequences=1,
                                                                                        return_dict_in_generate=True,
                                                                                        output_scores=True)
  response = tokenizer.batch_decode(generated_answers_encoded['sequences'], skip_special_tokens=True,clean_up_tokenization_spaces=True)    
  print(response)
  
  ```
  
  Have fun!