readme: 
  ---
  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.
  