Documents that contained words from a selection of the Dutch and English List of Dirty Naught Obscene and Otherwise Bad Words are removed
Sentences with less than 3 words are removed
Sentences with a word of more than 1000 characters are removed
Documents with less than 5 sentences are removed
Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies", "use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.

Models

TL;DR: yhavinga/t5-v1.1-base-dutch-cased is the best model.

yhavinga/t5-base-dutch is a re-training of the Dutch T5 base v1.0 model trained during the summer 2021 Flax/Jax community week. Accuracy was improved from 0.64 to 0.70.
The two T5 v1.1 base models are an uncased and cased version of t5-v1.1-base, again pre-trained from scratch on Dutch, with a tokenizer also trained from scratch. The t5 v1.1 models are slightly different from the t5 models, and the base models are trained with a dropout of 0.0. For fine-tuning it is intended to set this back to 0.1.
The large cased model is a pre-trained Dutch version of t5-v1.1-large. Training of t5-v1.1-large proved difficult. Without dropout regularization, the training would diverge at a certain point. With dropout training went better, be it much slower than training the t5-model. At some point convergance was too slow to warrant further training. The latest checkpoint, training scripts and metrics are available for reference. For actual fine-tuning the cased base model is probably the better choice.

	model	train seq len	acc	loss	batch size	epochs	steps	dropout	optim	lr	duration
yhavinga/t5-base-dutch	T5	512	0,70	1,38	128	1	528481	0.1	adafactor	5e-3	2d 9h
yhavinga/t5-v1.1-base-dutch-uncased	t5-v1.1	1024	0,73	1,20	64	2	1014525	0.0	adafactor	5e-3	5d 5h
yhavinga/t5-v1.1-base-dutch-cased	t5-v1.1	1024	0,78	0,96	64	2	1210000	0.0	adafactor	5e-3	6d 6h
yhavinga/t5-v1.1-large-dutch-cased	t5-v1.1	512	0,76	1,07	64	1	1120000	0.1	adafactor	5e-3	86 13h

The cased t5-v1.1 Dutch models were fine-tuned on summarizing the CNN Daily Mail dataset.

	model	input len	target len	Rouge1	Rouge2	RougeL	RougeLsum	Test Gen Len	epochs	batch size	steps	duration
yhavinga/t5-v1.1-base-dutch-cnn-test	t5-v1.1	1024	96	34,8	13,6	25,2	32,1	79	6	64	26916	2h 40m
yhavinga/t5-v1.1-large-dutch-cnn-test	t5-v1.1	1024	96	34,4	13,6	25,3	31,7	81	5	16	89720	11h

Acknowledgements

This project would not have been possible without compute generously provided by Google through the TPU Research Cloud. The HuggingFace 🤗 ecosystem was also instrumental in many, if not all parts of the training. The following repositories where helpful in setting up the TPU-VM, and training the models:

Created by Yeb Havinga

yhavinga/t5-v1.1-large-dutch-cnn-test

T5 v1.1 Large finetuned for CNN news summarization in Dutch 🇳🇱

Tokenizer

Dataset

Models

Acknowledgements

yhavinga/
t5-v1.1-large-dutch-cnn-test