Model Card of lmqg/mbart-large-cc25-dequad-qag

This model is fine-tuned version of facebook/mbart-large-cc25 for question & answer pair generation task on the lmqg/qag_dequad (dataset_name: default) via lmqg.

Overview

• Language model: facebook/mbart-large-cc25
• Language: de
• Training data: lmqg/qag_dequad (default)
• Online Demo: https://autoqg.net/
• Repository: https://github.com/asahi417/lm-question-generation
• Paper: https://arxiv.org/abs/2210.03992

Usage

• With lmqg

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="de", model="lmqg/mbart-large-cc25-dequad-qag")

# model prediction
question_answer_pairs = model.generate_qa("das erste weltweit errichtete Hermann Brehmer 1855 im niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen).")

• With transformers

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-dequad-qag")
output = pipe("Empfangs- und Sendeantenne sollen in ihrer Polarisation übereinstimmen, andernfalls wird die Signalübertragung stark gedämpft. ")

Evaluation

• Metric (Question & Answer Generation): raw metric file

	Score	Type	Dataset
QAAlignedF1Score (BERTScore)	69.25	default	lmqg/qag_dequad
QAAlignedF1Score (MoverScore)	50.71	default	lmqg/qag_dequad
QAAlignedPrecision (BERTScore)	70.69	default	lmqg/qag_dequad
QAAlignedPrecision (MoverScore)	51.81	default	lmqg/qag_dequad
QAAlignedRecall (BERTScore)	68.05	default	lmqg/qag_dequad
QAAlignedRecall (MoverScore)	49.78	default	lmqg/qag_dequad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

• dataset_path: lmqg/qag_dequad
• dataset_name: default
• input_types: ['paragraph']
• output_types: ['questions_answers']
• prefix_types: None
• model: facebook/mbart-large-cc25
• max_length: 512
• max_length_output: 256
• epoch: 6
• batch: 2
• lr: 0.0001
• fp16: False
• random_seed: 1
• gradient_accumulation_steps: 32
• 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",
}