How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
# Warning: Pipeline type "summarization" is no longer supported in transformers v5.
# You must load the model directly (see below) or downgrade to v4.x with:
# 'pip install "transformers<5.0.0'
from transformers import pipeline

pipe = pipeline("summarization", model="SZTAKI-HLT/mT5-base-HunSum-1")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("SZTAKI-HLT/mT5-base-HunSum-1")
model = AutoModelForSeq2SeqLM.from_pretrained("SZTAKI-HLT/mT5-base-HunSum-1")
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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}
}
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Dataset used to train SZTAKI-HLT/mT5-base-HunSum-1