from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="StellarMilk/t5-base-newsqa-qag-trained")

# model prediction
question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")

With transformers

python

from transformers import pipeline

pipe = pipeline("text2text-generation", "StellarMilk/t5-base-newsqa-qag-trained")
output = pipe("generate question and answer: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

Evaluation

Metric (Question & Answer Generation): raw metric file

Score	Type	Dataset

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: StellarMilk/newsqa
dataset_name: default
input_types: ['paragraph']
output_types: ['questions_answers']
prefix_types: ['qag']
model: t5-base
max_length: 512
max_length_output: 512
epoch: 14
batch: 2
lr: 0.0001
fp16: False
random_seed: 1
gradient_accumulation_steps: 2
label_smoothing: 0.0

The full configuration can be found at fine-tuning config file.

Citation

@inproceedings{ushio-etal-2022-generative,
    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
    author = "Ushio, Asahi  and
        Alva-Manchego, Fernando  and
        Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, U.A.E.",
    publisher = "Association for Computational Linguistics",
}

StellarMilk/t5-base-newsqa-qag-trained

Model Card of StellarMilk/t5-base-newsqa-qag-trained

Overview

Usage

Evaluation

Training hyperparameters

Citation

StellarMilk/
t5-base-newsqa-qag-trained

Model Card of `StellarMilk/t5-base-newsqa-qag-trained`