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research-backup/
t5-large-squad-qg-no-paragraph

Name: t5-large-squad-qg-no-paragraph
Availability: InStock

text-generation-inference

endpoints_compatible

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Model Card of `research-backup/t5-large-squad-qg-no-paragraph`

This model is fine-tuned version of t5-large for question generation task on the lmqg/qg_squad (dataset_name: default) via lmqg. This model is fine-tuned without pargraph information but only the sentence that contains the answer.

Overview

Language model: t5-large
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-large-squad-qg-no-paragraph")

# 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-large-squad-qg-no-paragraph")
output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

Evaluation

Metric (Question Generation): raw metric file

	Score	Type	Dataset
BERTScore	90.88	default	lmqg/qg_squad
Bleu_1	57.49	default	lmqg/qg_squad
Bleu_2	41.59	default	lmqg/qg_squad
Bleu_3	32.1	default	lmqg/qg_squad
Bleu_4	25.36	default	lmqg/qg_squad
METEOR	26.28	default	lmqg/qg_squad
MoverScore	64.44	default	lmqg/qg_squad
ROUGE_L	52.53	default	lmqg/qg_squad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qg_squad
dataset_name: default
input_types: ['sentence_answer']
output_types: ['question']
prefix_types: ['qg']
model: t5-large
max_length: 128
max_length_output: 32
epoch: 6
batch: 16
lr: 5e-05
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-large-squad-qg-no-paragraph

Model Card of research-backup/t5-large-squad-qg-no-paragraph