The Question Generator takes only a document as input and outputs questions that it thinks can be
answered by this document. In our current implementation, input texts are split into chunks of 50 words
with a 10 word overlap. This is because the default model
valhalla/t5-base-e2e-qg seems to generate only
about 3 questions per passage regardless of length. Our approach prioritizes the creation of more questions
over processing efficiency (T5 is able to digest much more than 50 words at once). The returned questions
generally come in an order dictated by the order of their answers i.e. early questions in the list generally
come from earlier in the document.
def __init__(model_name_or_path="valhalla/t5-base-e2e-qg", model_version=None, num_beams=4, max_length=256, no_repeat_ngram_size=3, length_penalty=1.5, early_stopping=True, split_length=50, split_overlap=10, use_gpu=True, prompt="generate questions:", batch_size: Optional[int] = None)
Uses the valhalla/t5-base-e2e-qg model by default. This class supports any question generation model that is
implemented as a Seq2SeqLM in HuggingFace Transformers. Note that this style of question generation (where the only input is a document) is sometimes referred to as end-to-end question generation. Answer-supervised question generation is not currently supported.
model_name_or_path: Directory of a saved model or the name of a public model e.g. "valhalla/t5-base-e2e-qg". See https://huggingface.co/models for full list of available models.
model_version: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
use_gpu: Whether to use GPU or the CPU. Falls back on CPU if no GPU is available.
batch_size: Number of documents to process at a time.
def generate_batch(texts: Union[List[str], List[List[str]]], batch_size: Optional[int] = None) -> Union[List[List[str]], List[List[List[str]]]]
Generates questions for a list of strings or a list of lists of strings.
texts: List of str or list of list of str.
batch_size: Number of texts to process at a time.