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--- |
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datasets: |
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- oscar |
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language: |
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- da |
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widget: |
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- text: Der var engang |
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--- |
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# What is this? |
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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). |
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# How to use |
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Test the model using the pipeline from the [🤗 Transformers](https://github.com/huggingface/transformers) library: |
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```python |
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from transformers import pipeline |
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generator = pipeline("text-generation", model = "KennethTM/gpt2-medium-danish") |
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text = generator("Manden arbejdede som") |
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print(text[0]["generated_text"]) |
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``` |
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Or load it using the Auto* classes: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("KennethTM/gpt2-medium-danish") |
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model = AutoModelForCausalLM.from_pretrained("KennethTM/gpt2-medium-danish") |
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``` |
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# Model training |
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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. |
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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). |
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The model is trained using ~1.000.000 samples. |
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For reference, the model achieves a perplexity of 24.7 on 5.000 random validation samples. |
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The model is trained on an 8 GB GPU. |
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# Notes |
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This is a pre-trained model, for optimal performance it should be finetuned for new tasks. |
