What is this?

This is a model based on the EleutherAI/gpt-neo-1.3B model containing 1.3 B 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/gpt-neo-1.3B-danish")
text = generator("Der var engang ")

print(text[0]["generated_text"])

Or load it using the Auto* classes:

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KennethTM/gpt-neo-1.3B-danish")
model = AutoModelForCausalLM.from_pretrained("KennethTM/gpt-neo-1.3B-danish")

Model training

The training data are the Danish part of the oscar dataset ('unshuffled_deduplicated_da') which is split randomly into training (95%) and validation (5%) datasets.

The model weights are initialized from the English gpt-neo-1.3B model ('source model') with new word token embeddings created from the Danish GPT-2 small model ('helper model') using the CLP-Transfer method.

Training is done using a context window of 1024 and mixed precision (bf16). First, only the word token embeddings are trained using 0.5 M samples followed by training of all weights using approximately 2 M samples (1 epoch).

The model achieves a perplexity of 16.75 on approximately 0.1 M validation samples.

The model is trained on a 24 GB GPU.

Notes

This is a pre-trained model, for optimal performance it should be finetuned for new downstream tasks tasks.