Model Card of lmqg/mbart-large-cc25-dequad-qg
This model is fine-tuned version of facebook/mbart-large-cc25 for question generation task on the lmqg/qg_dequad (dataset_name: default) via lmqg
.
Overview
- Language model: facebook/mbart-large-cc25
- Language: de
- Training data: lmqg/qg_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-qg")
# model prediction
questions = model.generate_q(list_context="das erste weltweit errichtete Hermann Brehmer 1855 im niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen).", list_answer="1855")
- With
transformers
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-dequad-qg")
output = pipe("Empfangs- und Sendeantenne sollen in ihrer Polarisation übereinstimmen, andernfalls <hl> wird die Signalübertragung stark gedämpft. <hl>")
Evaluation
- Metric (Question Generation): raw metric file
Score | Type | Dataset | |
---|---|---|---|
BERTScore | 80.77 | default | lmqg/qg_dequad |
Bleu_1 | 10.96 | default | lmqg/qg_dequad |
Bleu_2 | 4.48 | default | lmqg/qg_dequad |
Bleu_3 | 1.91 | default | lmqg/qg_dequad |
Bleu_4 | 0.75 | default | lmqg/qg_dequad |
METEOR | 13.71 | default | lmqg/qg_dequad |
MoverScore | 55.88 | default | lmqg/qg_dequad |
ROUGE_L | 11.19 | default | lmqg/qg_dequad |
- Metric (Question & Answer Generation, Reference Answer): Each question is generated from the gold answer. raw metric file
Score | Type | Dataset | |
---|---|---|---|
QAAlignedF1Score (BERTScore) | 90.66 | default | lmqg/qg_dequad |
QAAlignedF1Score (MoverScore) | 65.36 | default | lmqg/qg_dequad |
QAAlignedPrecision (BERTScore) | 90.64 | default | lmqg/qg_dequad |
QAAlignedPrecision (MoverScore) | 65.37 | default | lmqg/qg_dequad |
QAAlignedRecall (BERTScore) | 90.69 | default | lmqg/qg_dequad |
QAAlignedRecall (MoverScore) | 65.36 | default | lmqg/qg_dequad |
- Metric (Question & Answer Generation, Pipeline Approach): Each question is generated on the answer generated by
lmqg/mbart-large-cc25-dequad-ae
. raw metric file
Score | Type | Dataset | |
---|---|---|---|
QAAlignedF1Score (BERTScore) | 0 | default | lmqg/qg_dequad |
QAAlignedF1Score (MoverScore) | 0 | default | lmqg/qg_dequad |
QAAlignedPrecision (BERTScore) | 0 | default | lmqg/qg_dequad |
QAAlignedPrecision (MoverScore) | 0 | default | lmqg/qg_dequad |
QAAlignedRecall (BERTScore) | 0 | default | lmqg/qg_dequad |
QAAlignedRecall (MoverScore) | 0 | default | lmqg/qg_dequad |
Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_dequad
- dataset_name: default
- input_types: ['paragraph_answer']
- output_types: ['question']
- prefix_types: None
- model: facebook/mbart-large-cc25
- max_length: 512
- max_length_output: 32
- epoch: 11
- batch: 4
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 16
- 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",
}
- Downloads last month
- 47
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Dataset used to train research-backup/mbart-large-cc25-dequad-qg
Evaluation results
- BLEU4 (Question Generation) on lmqg/qg_dequadself-reported0.750
- ROUGE-L (Question Generation) on lmqg/qg_dequadself-reported11.190
- METEOR (Question Generation) on lmqg/qg_dequadself-reported13.710
- BERTScore (Question Generation) on lmqg/qg_dequadself-reported80.770
- MoverScore (Question Generation) on lmqg/qg_dequadself-reported55.880
- QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_dequadself-reported90.660
- QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_dequadself-reported90.690
- QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_dequadself-reported90.640
- QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_dequadself-reported65.360
- QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_dequadself-reported65.360