# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("SZTAKI-HLT/mT5-base-HunSum-2")
model = AutoModelForSeq2SeqLM.from_pretrained("SZTAKI-HLT/mT5-base-HunSum-2")Quick Links
Model Card for mT5-base-HunSum-2
The mT5-base-HunSum-2 is a Hungarian abstractive summarization model, which was trained on the SZTAKI-HLT/HunSum-2-abstractive 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 | 40.06 |
| ROUGE-2 | 12.67 |
| ROUGE-L | 25.93 |
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Model tree for SZTAKI-HLT/mT5-base-HunSum-2
Base model
google/mt5-base
# 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-2")