language:
- en
tags:
- semantic-role-labeling
- question-answer generation
- pytorch
datasets:
- kleinay/qanom
A Seq2Seq model for QANom parsing
This is a t5-small pretrained model, fine-tuned on the task of generating QANom QAs.
"QANom" stands for "QASRL for Nominalizations", which is an adaptation of QASRL (Question-Answer driven Semantic Role Labeling) for the nominal predicates domain. See the QANom paper for details about the task. The QANom Dataset official site is a Google drive, but we also wrapped it into a Huggingface Dataset, which is easier to plug-and-play with (check out our HF profile for other related datasets, such as QASRL, QAMR, QADiscourse, and QA-Align).
Demo
Visit our demo for interactively exploring our model!
Usage
The model and tokenizer can be downloaded as simply as running:
import transformers
model = transformers.AutoModelForSeq2SeqLM.from_pretrained("kleinay/qanom-seq2seq-model-baseline")
tokenizer = transformers.AutoTokenizer.from_pretrained("kleinay/qanom-seq2seq-model-baseline")
However, the model fine-tuning procedure involves input preprocessing (marking the predicate in the sentence, T5's "task prefix", incorporating the predicate type and/or the verbal for of the nominalization) and output postprocessing (parsing the sequence into a list of QASRL-formatted QAs).
In order to use the model for QANom parsing easily, we suggest downloading the pipeline.py file from this repository, and then use the QASRL_Pipeline class:
from pipeline import QASRL_Pipeline
pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-baseline")
pipe("The student was interested in Luke 's <predicate> research about see animals .", verb_form="research", predicate_type="nominal")
Which will output:
[{'generated_text': 'who _ _ researched something _ _ ?<extra_id_7> Luke',
'QAs': [{'question': 'who researched something ?', 'answers': ['Luke']}]}]
Notice that you need to specify which word in the sentence is the predicate, about which the question will interrogate. By default, you should precede the predicate with the <predicate> symbol, but you can also specify your own predicate marker:
pipe("The student was interested in Luke 's <PRED> research about see animals .", verb_form="research", predicate_type="nominal", predicate_marker="<PRED>")
In addition, you can specify additional kwargs for controling the model's decoding algorithm:
pipe("The student was interested in Luke 's <predicate> research about see animals .", verb_form="research", predicate_type="nominal", num_beams=3)