Instructions to use norkart/mt5-large-nn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use norkart/mt5-large-nn with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("norkart/mt5-large-nn") model = AutoModelForSeq2SeqLM.from_pretrained("norkart/mt5-large-nn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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<extra_id_0> kvar veke samlar medlemene av Regjeringa seg til Statsråd på <extra_id_1>. Dette organet er den høgste <extra_id_2> i Noreg. For at møtet skal vere <extra_id_3>, må meir enn halvparten av <extra_id_4> i regjeringa vere til stades.
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<extra_id_0> kvar veke samlar medlemene av Regjeringa seg til Statsråd på <extra_id_1>. Dette organet er den høgste <extra_id_2> i Noreg. For at møtet skal vere <extra_id_3>, må meir enn halvparten av <extra_id_4> i regjeringa vere til stades.
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This is a pruned version of the ```google/mt5-large model```. Here, the input and output embeddings are pruned to support a greatly reduced vocabulary. The chosen vocabulary has 30K norwegian, english and special tokens, ~12% of the old size. This reduces the model size by roughly 37%. The model is still OK on similar languages, like German and Danish, but very different languages like arabic are not a good fit anymore. This model is intended as a starting point for finetuning mt5 for norwegian applications.
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In addition it has been trained on Nynorsk using masked language modeling, in accordance with pretraining objective in the T5 paper.
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