metadata
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:
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?']