--- language: - "lb" license: "mit" tags: - "luxembourgish" - "lëtzebuergesch" - "text generation" - "transfer learning" model-index: - name: "LuxGPT2-basedGER" results: - task: type: "text-generation" # Required. Example: automatic-speech-recognition name: "Text Generation" # Optional. Example: Speech Recognition dataset: type: "LuxembourgishTestDataset" # Required. Example: common_voice. Use dataset id from https://hf.co/datasets name: "Luxembourgish Test Dataset" # Required. A pretty name for the dataset. Example: Common Voice (French) metrics: - type: "accuracy" # Required. Example: wer. Use metric id from https://hf.co/metrics value: "0.34" # Required. Example: 20.90 - name: "LuxGPT2-basedGER" results: - task: type: "text-generation" # Required. Example: automatic-speech-recognition name: "Text Generation" # Optional. Example: Speech Recognition dataset: type: "LuxembourgishTestDataset" # Required. Example: common_voice. Use dataset id from https://hf.co/datasets name: "Luxembourgish Test Dataset" # Required. A pretty name for the dataset. Example: Common Voice (French) metrics: - type: "perplexity" # Required. Example: wer. Use metric id from https://hf.co/metrics value: "45.89" # Required. Example: 20.90 --- ## LuxGPT-2 based GER GPT-2 model for Text Generation in luxembourgish language, trained on 711 MB of text data, consisting of RTL.lu news articles, comments, parlament speeches, the luxembourgish Wikipedia, Newscrawl, Webcrawl and subtitles. Created via transfer learning with an German base model, feature space mapping from LB on Base feature space and gradual layer freezing. The training took place on a 32 GB Nvidia Tesla V100 - with One Cycle policy for the learning rate - with the help of fastai's LR finder - for 53.4 hours - for 20 epochs and 7 cycles - using the fastai library ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("laurabernardy/LuxGPT2-basedGER") model = AutoModelForCausalLM.from_pretrained("laurabernardy/LuxGPT2-basedGER") ``` ## Limitations and Biases See the [GPT2 model card](https://huggingface.co/gpt2) for considerations on limitations and bias. See the [GPT2 documentation](https://huggingface.co/transformers/model_doc/gpt2.html) for details on GPT2.