README.md

---
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.