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---
datasets:
- oscar
language:
- da
widget:
- text: Der var engang
---
# What is this?
A GPT-2 model (medium version, ~354.8 M parameters) for Danish text generation. The model was not pre-trained from scratch but adapted from the English version using [CLP-Transfer](https://arxiv.org/abs/2301.09626).
# How to use
Test the model using the pipeline from the [🤗 Transformers](https://github.com/huggingface/transformers) library:
```python
from transformers import pipeline
generator = pipeline("text-generation", model = "KennethTM/gpt2-medium-danish")
text = generator("Manden arbejdede som")
print(text[0]["generated_text"])
```
Or load it using the Auto* classes:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("KennethTM/gpt2-medium-danish")
model = AutoModelForCausalLM.from_pretrained("KennethTM/gpt2-medium-danish")
```
# Model training
The training data are the Danish part of the [oscar dataset](https://huggingface.co/datasets/oscar) ('unshuffled_deduplicated_da') and a context length of 1024 tokens.
The model weights are initialized from the English [GPT-2 medium model](https://huggingface.co/gpt2-medium) ('source model') with new word token embeddings created from the Danish [GPT-2 small model](https://huggingface.co/KennethTM/gpt2-small-danish) ('helper model') using the [CLP-Transfer method](https://github.com/malteos/clp-transfer).
The model is trained using ~1.000.000 samples.
For reference, the model achieves a perplexity of 24.7 on 5.000 random validation samples.
The model is trained on an 8 GB GPU.
# Notes
This is a pre-trained model, for optimal performance it should be finetuned for new tasks. |