This is an encoder Language Model pre-trained from scratch on transcriptions of the archives of the Dutch East India Company. It is therefore a model specialized on Early Modern Dutch as used in the archive (1602–1800). 
The model follows a RoBERTa architecture. It can be fine-tuned on any NLP task. 

This version of the model is the best performing GloBERTise model when tested on binary event detection of the four I have pre-trained (in august 2025)

Comparison to other models: Adapted settings for 'num_training_steps' and 'num_warmup_steps' compared to GloBERTise-v01 and GloBERTise-v01-rerun, otherwise the same. Different seed compared to GloBERTise-rerun, same parameter settings.


See my GitHub repos
- for pre-training: https://github.com/globalise-huygens/GloBERTise
- for evaluation: https://github.com/globalise-huygens/GloBERTise-eval

And a small presentation: https://docs.google.com/presentation/d/1gkg5hChWAMXA6mxfgFkkvIieWdj_17yKitwBkBNcJBo/edit?usp=sharing

Most important parameter settings:

|    |      |  
|------------------|--------------|
| learning rate            | 0.0003 |
| betas            | [ 0.9, 0.98] |
| weight_decay     | 0.01         | 
| num_train_epochs | 2            |  
| per_device_train_batch_size    | 40       | 
| gradient_accumulation_steps | 10         | 
| fp16 | true         |