---
language:
- en
datasets:
- alinet/balanced_qg
model-index:
- name: alinet/bart-base-balanced-qg
  results:
  - task:
      type: text2text-generation
      name: Question Generation
    dataset:
      name: MRQA
      type: mrqa
    metrics:
    - type: bertscore
      value: 0.6579994835741414
      name: BERTScore F1
    - type: bertscore
      value: 0.6617731395187654
      name: BERTScore Precision
    - type: bertscore
      value: 0.6576008430831539
      name: BERTScore Recall
  - task:
      type: text2text-generation
      name: Question Generation
    dataset:
      name: Spoken-SQuAD
      type: alinet/spoken_squad
    metrics:
    - type: bertscore
      value: 0.6005104740534271
      name: BERTScore F1
    - type: bertscore
      value: 0.5973629577263946
      name: BERTScore Precision
    - type: bertscore
      value: 0.6071276199638798
      name: BERTScore Recall
---
A question generation model trained on `alinet/balanced_qg` dataset.

Example usage:

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

model_name = "alinet/bart-base-balanced-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 Stanford Question Answering Dataset?']
```