metadata

datasets:
  - SZTAKI-HLT/HunSum-1
language:
  - hu
metrics:
  - rouge
pipeline_tag: text2text-generation
inference:
  parameters:
    num_beams: 5
    length_penalty: 2
    max_length: 128
    no_repeat_ngram_size: 3
    early_stopping: true
tags:
  - hubert
  - bert
  - summarization

Model Card for Bert2Bert-HunSum-1

The Bert2Bert-HunSum-1 is a Hungarian abstractive summarization model, which was trained on the SZTAKI-HLT/HunSum-1 dataset. The model is based on SZTAKI-HLT/hubert-base-cc.

Intended uses & limitations

Model type: Text Summarization
Language(s) (NLP): Hungarian
Resource(s) for more information:
- GitHub Repo

Parameters

Batch Size: 13
Learning Rate: 5e-5
Weight Decay: 0.01
Warmup Steps: 16000
Epochs: 15
no_repeat_ngram_size: 3
num_beams: 5
early_stopping: True

Results

Metric	Value
ROUGE-1	28.52
ROUGE-2	10.35
ROUGE-L	20.07

Citation

If you use our model, please cite the following paper:

@inproceedings {HunSum-1,
    title = {{HunSum-1: an Abstractive Summarization Dataset for Hungarian}},
    booktitle = {XIX. Magyar Számítógépes Nyelvészeti Konferencia (MSZNY 2023)},
    year = {2023},
    publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
    address = {Szeged, Magyarország},
    author = {Barta, Botond and Lakatos, Dorina and Nagy, Attila and Nyist, Mil{\'{a}}n Konor and {\'{A}}cs, Judit},
    pages = {231--243}
}