readme: ---
  language: id
  tags:
    - gpt2-indo-medium-kids-stories
  license: mit
  widget:
    - text: "Archie sedang mengendarai roket ke planet Mars."
  ---
  
  ## GPT-2 Indonesian Medium Kids Stories
  
  GPT-2 Indonesian Medium Kids Stories is a causal language model based on the [OpenAI GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) model. The model was originally the pre-trained [GPT2 Medium Indonesian](https://huggingface.co/flax-community/gpt2-medium-indonesian) model, which was then fine-tuned on Indonesian kids' stories from [Room To Read](https://literacycloud.org/) and [Let's Read](https://reader.letsreadasia.org/).
  
  10% of the dataset was kept for evaluation purposes. The pre-trained model was fine-tuned and achieved an evaluation loss of 3.579 and an evaluation perplexity of 35.84.
  
  Hugging Face's `Trainer` class from the [Transformers](https://huggingface.co/transformers) library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with other frameworks nonetheless.
  
  ## Model
  
  | Model                           | #params | Arch.       | Training/Validation data (text)   |
  | ------------------------------- | ------- | ----------- | --------------------------------- |
  | `gpt2-indo-medium-kids-stories` | 345M    | GPT2 Medium | Indonesian Kids' Stories (860 KB) |
  
  ## Evaluation Results
  
  The model was fine-tuned for 3 epochs.
  
  | Epoch | Training Loss | Validation Loss |
  | ----- | ------------- | --------------- |
  | 1     | 3.909100      | 3.627678        |
  | 2     | 3.375300      | 3.562854        |
  | 3     | 3.113300      | 3.578999        |
  
  ## How to Use (PyTorch)
  
  ### As Causal Language Model
  
  ```python
  from transformers import pipeline
  
  pretrained_name = "bookbot/gpt2-indo-medium-kids-stories"
  
  nlp = pipeline(
      "text-generation",
      model=pretrained_name,
      tokenizer=pretrained_name
  )
  
  nlp("Archie sedang mengendarai roket ke planet Mars.")
  ```
  
  ### Feature Extraction in PyTorch
  
  ```python
  from transformers import GPT2LMHeadModel, GPT2TokenizerFast
  
  pretrained_name = "bookbot/gpt2-indo-medium-kids-stories"
  model = GPT2LMHeadModel.from_pretrained(pretrained_name)
  tokenizer = GPT2TokenizerFast.from_pretrained(pretrained_name)
  
  prompt = "Archie sedang mengendarai roket ke planet Mars."
  encoded_input = tokenizer(prompt, return_tensors='pt')
  output = model(**encoded_input)
  ```
  
  ## Disclaimer
  
  Do consider the biases which come from both the pre-trained GPT-2 model and the Indonesian Kids' Stories dataset that may be carried over into the results of this model.
  
  ## Author
  
  GPT-2 Indonesian Medium Kids Stories was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access.
  