---
language: nl
widget:
- text: "In het jaar 2030 zullen we"
- text: "Toen ik gisteren volledig in de ban was van"
- text: "Studenten en leraren van de Bogazici Universiteit in de Turkse stad Istanbul"
- text: "In Israël was een strenge lockdown"
tags:
- gpt2-large
- gpt2
pipeline_tag: text-generation
datasets:
- yhavinga/mc4_nl_cleaned
---
# GPT2-Large pre-trained on cleaned Dutch mC4 🇳🇱

A GPT2 large model (762M parameters) trained from scratch on Dutch, with perplexity 15.1 on cleaned Dutch mC4.

## How To Use

You can use this GPT2-model directly with a pipeline for text generation.

```python
MODEL_DIR='yhavinga/gpt2-large-dutch'
from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained(MODEL_DIR)
model = GPT2LMHeadModel.from_pretrained(MODEL_DIR)
generator = pipeline('text-generation', model, tokenizer=tokenizer)

generated_text = generator('Het eiland West-', max_length=100, do_sample=True, top_k=40, top_p=0.95, repetition_penalty=2.0))
```

*"Het eiland West-" - "Terschelling wordt sinds jaar en dag bewoond door de mens. De mensen die in het huidige Terherne wonen doen er alles aan om hun dorp te behouden voor deze diersoort, namelijk; een natuurreservaat dat vooral bestaat uit hoge duinen met lage begroeing waar planten van vroeger worden afgewisseld (zoals wilde hyacinten)en waarop grassen groeien waarvan sommige soorten zeldzame vormen hebben ontwikkeld: duinlelie of blauwe bosbes zijn bijvoorbeeld bekend vanwege onder andere kleurmole"*

## Tokenizer

* BPE tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface
  Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).

## Dataset

This model was trained on of the `full` configuration (33B tokens) of
[cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
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](https://github.com/LDNOOBW/List-of-Dirty-Naughty-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/gpt2-medium-dutch](https://huggingface.co/yhavinga/gpt2-medium-dutch) is the best model.

* The models with `a`/`b` in the step-column have been trained to step `a` of a total of `b` steps.

|                                                                                   | model   | params | train seq len | ppl  | loss | batch size | epochs | steps           | optim     | lr     | duration | config    |
|-----------------------------------------------------------------------------------|---------|--------|---------------|------|------|------------|--------|-----------------|-----------|--------|----------|-----------|
| [yhavinga/gpt-neo-125M-dutch](https://huggingface.co/yhavinga/gpt-neo-125M-dutch) | gpt neo | 125M   | 512           | 20.9 | 3.04 | 128        | 1      | 190000/558608          | adam | 2.4e-3 | 1d 12h   | full |
| [yhavinga/gpt2-medium-dutch](https://huggingface.co/yhavinga/gpt2-medium-dutch)   | gpt2    | 345M   | 512           | 15.1 | 2.71 | 128        | 1      | 320000/520502   | adam | 8e-4   | 7d 2h    | full      |
| [yhavinga/gpt2-large-dutch](https://huggingface.co/yhavinga/gpt2-large-dutch)     | gpt2    | 762M   | 512           | 15.1 | 2.72 | 32         | 1      | 1100000/2082009 | adafactor | 3.3e-5 | 8d 15h   | large     |
| [yhavinga/gpt-neo-1.3B-dutch](https://huggingface.co/yhavinga/gpt-neo-1.3B-dutch) | gpt neo | 1.3B   | 512           | 16.0 | 2.77 | 16         | 1      | 960000/3049896  | adafactor | 5e-4   | 7d 11h   | full      |


## Acknowledgements

This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem was also
instrumental in most, if not all, parts of the training. The following repositories where helpful in setting up the TPU-VM,
and training the models:

* [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
* [HUggingFace Flax MLM examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
* [gpt2-medium-persian](https://huggingface.co/flax-community/gpt2-medium-persian)
* [gpt2-medium-indonesian](https://huggingface.co/flax-community/gpt2-medium-persian)

Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)