from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="lmqg/t5-base-tweetqa-qag")

# 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", "lmqg/t5-base-tweetqa-qag")
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
BERTScore	90.55	default	lmqg/qag_tweetqa
Bleu_1	39.29	default	lmqg/qag_tweetqa
Bleu_2	26.69	default	lmqg/qag_tweetqa
Bleu_3	18.4	default	lmqg/qag_tweetqa
Bleu_4	12.93	default	lmqg/qag_tweetqa
METEOR	30.35	default	lmqg/qag_tweetqa
MoverScore	61.82	default	lmqg/qag_tweetqa
QAAlignedF1Score (BERTScore)	92.37	default	lmqg/qag_tweetqa
QAAlignedF1Score (MoverScore)	64.63	default	lmqg/qag_tweetqa
QAAlignedPrecision (BERTScore)	92.75	default	lmqg/qag_tweetqa
QAAlignedPrecision (MoverScore)	65.5	default	lmqg/qag_tweetqa
QAAlignedRecall (BERTScore)	92.01	default	lmqg/qag_tweetqa
QAAlignedRecall (MoverScore)	63.85	default	lmqg/qag_tweetqa
ROUGE_L	36.54	default	lmqg/qag_tweetqa

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qag_tweetqa
dataset_name: default
input_types: ['paragraph']
output_types: ['questions_answers']
prefix_types: ['qag']
model: t5-base
max_length: 256
max_length_output: 128
epoch: 15
batch: 32
lr: 0.0001
fp16: False
random_seed: 1
gradient_accumulation_steps: 2
label_smoothing: 0.15

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",
}

lmqg/t5-base-tweetqa-qag

Model Card of lmqg/t5-base-tweetqa-qag

Overview

Usage

Evaluation

Training hyperparameters

Citation

lmqg/
t5-base-tweetqa-qag

Model Card of `lmqg/t5-base-tweetqa-qag`