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research-backup/
t5-small-squad-qg-no-answer

Name: t5-small-squad-qg-no-answer
Availability: InStock

text-generation-inference

endpoints_compatible

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Model Card of `research-backup/t5-small-squad-qg-no-answer`

This model is fine-tuned version of t5-small for question generation task on the lmqg/qg_squad (dataset_name: default) via lmqg. This model is fine-tuned without answer information, i.e. generate a question only given a paragraph (note that normal model is fine-tuned to generate a question given a pargraph and an associated answer in the paragraph).

Overview

Language model: t5-small
Language: en
Training data: lmqg/qg_squad (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-squad-qg-no-answer")

# model prediction
questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")

With transformers

python

from transformers import pipeline

pipe = pipeline("text2text-generation", "research-backup/t5-small-squad-qg-no-answer")
output = pipe("generate question: <hl>  Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>")

Evaluation

Metric (Question Generation): raw metric file

	Score	Type	Dataset
BERTScore	89.64	default	lmqg/qg_squad
Bleu_1	53.37	default	lmqg/qg_squad
Bleu_2	36.67	default	lmqg/qg_squad
Bleu_3	27.4	default	lmqg/qg_squad
Bleu_4	21.12	default	lmqg/qg_squad
METEOR	23.38	default	lmqg/qg_squad
MoverScore	62.07	default	lmqg/qg_squad
ROUGE_L	47.47	default	lmqg/qg_squad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qg_squad
dataset_name: default
input_types: ['paragraph_sentence']
output_types: ['question']
prefix_types: ['qg']
model: t5-small
max_length: 512
max_length_output: 32
epoch: 7
batch: 64
lr: 0.0001
fp16: False
random_seed: 1
gradient_accumulation_steps: 4
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-squad-qg-no-answer

Model Card of research-backup/t5-small-squad-qg-no-answer