Model Card of lmqg/mbart-large-cc25-ruquad-qag
This model is fine-tuned version of facebook/mbart-large-cc25 for question & answer pair generation task on the lmqg/qag_ruquad (dataset_name: default) via lmqg
.
Overview
- Language model: facebook/mbart-large-cc25
- Language: ru
- Training data: lmqg/qag_ruquad (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="ru", model="lmqg/mbart-large-cc25-ruquad-qag")
# model prediction
question_answer_pairs = model.generate_qa("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.")
- With
transformers
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-ruquad-qag")
output = pipe("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.")
Evaluation
- Metric (Question & Answer Generation): raw metric file
Score | Type | Dataset | |
---|---|---|---|
QAAlignedF1Score (BERTScore) | 77.36 | default | lmqg/qag_ruquad |
QAAlignedF1Score (MoverScore) | 56.1 | default | lmqg/qag_ruquad |
QAAlignedPrecision (BERTScore) | 74.97 | default | lmqg/qag_ruquad |
QAAlignedPrecision (MoverScore) | 54.4 | default | lmqg/qag_ruquad |
QAAlignedRecall (BERTScore) | 80.05 | default | lmqg/qag_ruquad |
QAAlignedRecall (MoverScore) | 58.11 | default | lmqg/qag_ruquad |
Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qag_ruquad
- 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",
}
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Dataset used to train research-backup/mbart-large-cc25-ruquad-qag
Evaluation results
- QAAlignedF1Score-BERTScore (Question & Answer Generation) on lmqg/qag_ruquadself-reported77.360
- QAAlignedRecall-BERTScore (Question & Answer Generation) on lmqg/qag_ruquadself-reported80.050
- QAAlignedPrecision-BERTScore (Question & Answer Generation) on lmqg/qag_ruquadself-reported74.970
- QAAlignedF1Score-MoverScore (Question & Answer Generation) on lmqg/qag_ruquadself-reported56.100
- QAAlignedRecall-MoverScore (Question & Answer Generation) on lmqg/qag_ruquadself-reported58.110
- QAAlignedPrecision-MoverScore (Question & Answer Generation) on lmqg/qag_ruquadself-reported54.400