Update README.md

README.md

@@ -5,4 +5,46 @@ 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](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 model is trained using the Danish part of the [oscar dataset](https://huggingface.co/datasets/oscar) ('unshuffled_deduplicated_da') and a context length of 1024 tokens.
+
+The model is 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 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.