readme: ---
  datasets: 
    - wikipedia
  language: 
    - lt
  license: apache-2.0
  tags: 
    - "text-generation"
  widget:
    - text: "Lietuva yra viena "
  ---
  ## Model description
  
  ![LT](LT.png)
  
  GPT-2 model from Lithuania using Wikipedia corpus dataset based on GPT-2 small model.
  
  This is only the first version of the model; over time model will be improved using a more extensive dataset and better data preparation.
  
  ## Training data
  This model was pre-trained with 180MB of Lithuanian Wikipedia. The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE).
  
  ## Training
  The model was trained on wiki-corpus for 40 hours using NVIDIA Tesla P100 GPU.
  
  ### How to use
  
  ### Load model
  
  ``` 
  from transformers import AutoTokenizer, TFAutoModelWithLMHead
  import tensorflow as tf
  
  tokenizer = AutoTokenizer.from_pretrained("DeividasM/gpt2_lithuanian_small")
  model = TFAutoModelWithLMHead.from_pretrained("DeividasM/gpt2_lithuanian_small")
  
  # Get sequence length max of 1024
  tokenizer.model_max_length=1024 
  
  model.eval()
  ```
  ## Generate text
  
  ``` 
  text = "tekstas "
  inputs = tokenizer.encode(text, return_tensors="tf")
  
  
  outputs = model.generate(inputs, eos_token_id=50256, pad_token_id=50256, 
                           do_sample=True,
                           max_length=40,
                           top_k=40)
                           
  print(tokenizer.decode(outputs[0]))
  
  ```
  ## Limitations and bias
  The training data used for this model come from Lithuanian Wikipedia. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the OpenAI team themselves point out in their model card:
  
  
  "Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true. Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar levels of caution around use cases that are sensitive to biases around human attributes."
  
  
  ## Author
  
  Lithuanian GPT-2 small was trained and evaluated by Deividas Mataciunas (https://www.linkedin.com/in/deividasmataciunas/)
  