bart-base-balanced-ra-qg

A question generation model trained on alinet/balanced_qg dataset (resolved_augmented subset).

Example usage:

from transformers import BartConfig, BartForConditionalGeneration, BartTokenizer

model_name = "alinet/bart-base-balanced-ra-qg"

tokenizer = BartTokenizer.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name) 

def run_model(input_string, **generator_args):
  input_ids = tokenizer.encode(input_string, return_tensors="pt")
  res = model.generate(input_ids, **generator_args)
  output = tokenizer.batch_decode(res, skip_special_tokens=True)
  print(output)

run_model("Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.", max_length=32, num_beams=4)
# ['What is the term for a reading comprehension dataset consisting of questions posed by crowdworkers?']