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A Swedish Bert model

Model description

This model follows the Bert Large model architecture as implemented in Megatron-LM framework. It was trained with a batch size of 512 in 600k steps. The model contains following parameters:

Hyperparameter Value
nparametersn_{parameters} 340M
nlayersn_{layers} 24
nheadsn_{heads} 16
nctxn_{ctx} 1024
nvocabn_{vocab} 30592

Training data

The model is pretrained on a Swedish text corpus of around 85 GB from a variety of sources as shown below.

Dataset Genre Size(GB)
Anföranden Politics 0.9
DCEP Politics 0.6
DGT Politics 0.7
Fass Medical 0.6
Författningar Legal 0.1
Web data Misc 45.0
JRC Legal 0.4
Litteraturbanken Books 0.3O
SCAR Misc 28.0
SOU Politics 5.3
Subtitles Drama 1.3
Wikipedia Facts 1.8

Intended uses & limitations

The raw model can be used for the usual tasks of masked language modeling or next sentence prediction. It is also often fine-tuned on a downstream task to improve its performance in a specific domain/task.

How to use

from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("AI-Nordics/bert-large-swedish-cased")
model = AutoModelForMaskedLM.from_pretrained("AI-Nordics/bert-large-swedish-cased")
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This model can be loaded on the Inference API on-demand.