Create README.md

README.md

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+---
+license: mit
+datasets:
+- oscar
+language:
+- da
+widget:
+  - text: Der var engang
+---
+
+# What is this?
+
+This is a model based on the [EleutherAI/gpt-neo-1.3B](https://huggingface.co/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](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/gpt-neo-1.3B-danish")
+text = generator("Der var engang ")
+
+print(text[0]["generated_text"])
+```
+
+Or load it using the Auto* classes:
+
+```python
+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](https://huggingface.co/datasets/oscar) ('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](https://huggingface.co/EleutherAI/gpt-neo-1.3B) ('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).
+
+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.