datasets:
- oscar
language:
- da
widget:
- text: Der var engang
What is this?
A GPT-2 model (small version, ~354 M parameters) for Danish text generation. The model was not pre-trained from scratch but adapted from the English version using CLP-Transfer.
How to use
Test the model using the pipeline from the 🤗 Transformers library:
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:
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("KennethTM/gpt2-medium-danish")
model = AutoModelForCausalLM.from_pretrained("KennethTM/gpt2-medium-danish")
Model training
The model is trained using the Danish part of the oscar dataset ('unshuffled_deduplicated_da') and a context length of 1024 tokens.
The model is initialized from the English GPT-2 medium model ('source model') with new word token embeddings created from the Danish GPT-2 small model ('helper model') using the CLP-Transfer method.
The whole 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.