t5-base-dutch
Created by Yeb Havinga & Dat Nguyen during the Hugging Face community week, organized by HuggingFace and TPU usage sponsored by Google, for the project Pre-train T5 from scratch in Dutch.
See also the fine-tuned t5-base-dutch-demo model, and the demo application Netherformer 📰, that are based on this model.
5 jan 2022: Model updated. Evaluation accuracy increased from 0.64 to 0.70.
11 jan 2022: See also yhavinga/t5-v1.1-base-dutch-cased with eval acc 0.78
Model
- Configuration based on
google/t5-base - 12 layers, 12 heads
- Dropout set to 0.1
Dataset
This model was trained on the full configuration of cleaned Dutch mC4,
which is the original mC4, except
- 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.
Tokenization
A SentencePiece tokenizer was trained from scratch on this dataset.
The total tokens of the full configuration is 34B
Training
The model was trained on the full mc4_nl_cleaned dataset configuration for 1 epoch, consisting of 34B tokens,
for 528 482 steps with a batch size of 128 and took 57 hours.
A triangle learning rate schedule was used, with peak learning rate 0.005.
Evaluation
- Loss: 1.38
- Accuracy: 0.70
