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vocabtrimmer/
mt5-small-trimmed-en-60000-squad-qg

Name: mt5-small-trimmed-en-60000-squad-qg
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Model Card of `vocabtrimmer/mt5-small-trimmed-en-60000-squad-qg`

This model is fine-tuned version of ckpts/mt5-small-trimmed-en-60000 for question generation task on the lmqg/qg_squad (dataset_name: default) via lmqg.

Overview

Language model: ckpts/mt5-small-trimmed-en-60000
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="vocabtrimmer/mt5-small-trimmed-en-60000-squad-qg")

# 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", "vocabtrimmer/mt5-small-trimmed-en-60000-squad-qg")
output = pipe("<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	90.05	default	lmqg/qg_squad
Bleu_1	54.46	default	lmqg/qg_squad
Bleu_2	38.13	default	lmqg/qg_squad
Bleu_3	28.68	default	lmqg/qg_squad
Bleu_4	22.2	default	lmqg/qg_squad
METEOR	24.16	default	lmqg/qg_squad
MoverScore	62.89	default	lmqg/qg_squad
ROUGE_L	49.3	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_answer
output_types: question
prefix_types: None
model: ckpts/mt5-small-trimmed-en-60000
max_length: 512
max_length_output: 32
epoch: 16
batch: 16
lr: 0.0005
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",
}

vocabtrimmer/mt5-small-trimmed-en-60000-squad-qg

Model Card of vocabtrimmer/mt5-small-trimmed-en-60000-squad-qg