metadata
language:
- hu
pipeline_tag: summarization
inference:
parameters:
num_beams: 5
length_penalty: 2
max_length: 128
encoder_no_repeat_ngram_size: 4
no_repeat_ngram_size: 3
datasets:
- SZTAKI-HLT/HunSum-1
metrics:
- rouge
license: apache-2.0
Model Card for mT5-base-HunSum-1
The mT5-base-HunSum-1 is a Hungarian abstractive summarization model, which was trained on the SZTAKI-HLT/HunSum-1 dataset. The model is based on google/mt5-base.
Intended uses & limitations
- Model type: Text Summarization
- Language(s) (NLP): Hungarian
- Resource(s) for more information:
Parameters
- Batch Size: 12
- Learning Rate: 5e-5
- Weight Decay: 0.01
- Warmup Steps: 3000
- Epochs: 10
- no_repeat_ngram_size: 3
- num_beams: 5
- early_stopping: False
- encoder_no_repeat_ngram_size: 4
Results
| Metric | Value |
|---|---|
| ROUGE-1 | 37.70 |
| ROUGE-2 | 11.22 |
| ROUGE-L | 24.37 |
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}
}