readme: 
  ---
  language: 
    - "lb"
  license: "mit"
  tags:
  - "luxembourgish"
  - "lëtzebuergesch"
  - "text generation"
  model-index:
  - name: "LuxGPT2"
    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: "0.33"      # Required. Example: 20.90
  - name: "LuxGPT2"
    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: "46.69"      # Required. Example: 20.90
  ---
  ## LuxGPT-2 
  
  GPT-2 model for Text Generation in luxembourgish language, trained on 667 MB of text data, consisting of RTL.lu news articles, comments, parlament speeches, the luxembourgish Wikipedia, Newscrawl, Webcrawl and subtitles.
  The training took place on a 32 GB Nvidia Tesla V100
  - with an initial learning rate of 5e-5
  - with Batch size 4
  - for 109 hours
  - for 30 epochs
  - using the transformers library
  <br/>
  more detailed training information can be found in the "trainer_state.json".  
  
  ## Usage
  ```python
  from transformers import AutoTokenizer, AutoModelForCausalLM
  
  tokenizer = AutoTokenizer.from_pretrained("laurabernardy/LuxGPT2")
  
  model = AutoModelForCausalLM.from_pretrained("laurabernardy/LuxGPT2")
  ```
  
  ## 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.
  