A question generation model trained on `SQuAD` and `Spoken-SQuAD`

Example usage:

```py
from transformers import BartConfig, BartForConditionalGeneration, BartTokenizer

model_name = "alinet/bart-base-spoken-squad-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 a reading comprehension dataset?']
```
{"language": ["en"], "datasets": ["squad", "alinet/spoken_squad"], "model-index": [{"name": "alinet/bart-base-spoken-squad-qg", "results": [{"task": {"type": "text2text-generation", "name": "Question Generation"}, "dataset": {"name": "MRQA", "type": "mrqa"}, "metrics": [{"type": "bertscore", "value": 0.6817703436309667, "name": "BERTScore F1"}, {"type": "bertscore", "value": 0.6905492821454426, "name": "BERTScore Precision"}, {"type": "bertscore", "value": 0.676456374645377, "name": "BERTScore Recall"}]}, {"task": {"type": "text2text-generation", "name": "Question Generation"}, "dataset": {"name": "Spoken-SQuAD", "type": "alinet/spoken_squad"}, "metrics": [{"type": "bertscore", "value": 0.6375612532393318, "name": "BERTScore F1"}, {"type": "bertscore", "value": 0.6397380229210538, "name": "BERTScore Precision"}, {"type": "bertscore", "value": 0.6385392911981904, "name": "BERTScore Recall"}]}]}]}
transformers pytorch bart text2text-generation en dataset:squad dataset:alinet/spoken_squad model-index autotrain_compatible endpoints_compatible region:us