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research-backup/
t5-small-tweetqa-qag-np

Name: t5-small-tweetqa-qag-np
Availability: InStock

transformers

pytorch

text2text-generation

questions and answers generation

text-generation-inference

endpoints_compatible

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Model Card of `research-backup/t5-small-tweetqa-qag-np`

This model is fine-tuned version of t5-small for question & answer pair generation task on the lmqg/qag_tweetqa (dataset_name: default) via lmqg. This model is fine-tuned without a task prefix.

Overview

Language model: t5-small
Language: en
Training data: lmqg/qag_tweetqa (default)
Online Demo: https://autoqg.net/
Repository: https://github.com/asahi417/lm-question-generation
Paper: https://arxiv.org/abs/2210.03992

Usage

With lmqg

python

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="research-backup/t5-small-tweetqa-qag-np")

# 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", "research-backup/t5-small-tweetqa-qag-np")
output = pipe("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	89.48	default	lmqg/qag_tweetqa
Bleu_1	35.61	default	lmqg/qag_tweetqa
Bleu_2	23.38	default	lmqg/qag_tweetqa
Bleu_3	15.73	default	lmqg/qag_tweetqa
Bleu_4	10.71	default	lmqg/qag_tweetqa
METEOR	27.8	default	lmqg/qag_tweetqa
MoverScore	60.53	default	lmqg/qag_tweetqa
QAAlignedF1Score (BERTScore)	90.7	default	lmqg/qag_tweetqa
QAAlignedF1Score (MoverScore)	62.94	default	lmqg/qag_tweetqa
QAAlignedPrecision (BERTScore)	91.19	default	lmqg/qag_tweetqa
QAAlignedPrecision (MoverScore)	64.1	default	lmqg/qag_tweetqa
QAAlignedRecall (BERTScore)	90.23	default	lmqg/qag_tweetqa
QAAlignedRecall (MoverScore)	61.9	default	lmqg/qag_tweetqa
ROUGE_L	34.77	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: None
model: t5-small
max_length: 256
max_length_output: 128
epoch: 16
batch: 64
lr: 0.0001
fp16: False
random_seed: 1
gradient_accumulation_steps: 1
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",
}

research-backup/t5-small-tweetqa-qag-np

Model Card of research-backup/t5-small-tweetqa-qag-np