Initial README update

README.md

@@ -1 +1,67 @@
-GPT-2 English trained on Indonesian Wikipedia articles.
+---
+language: id
+tags:
+- indo-gpt2-small
+license: mit
+datasets:
+- wikipedia
+---
+
+## Indo GPT-2 Small
+Indo GPT-2 Small is a language model based on the [GPT-2 model](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). It was trained on the latest (around late December) Indonesian Wikipedia articles.
+
+The model was originally HuggingFace's pretrained [English GPT-2 model](https://huggingface.co/transformers/model_doc/gpt2.html) and is later fine-tuned on the Indonesian dataset. Many of the techniques used
+are based on a [notebook](https://github.com/piegu/fastai-projects/blob/master/finetuning-English-GPT2-any-language-Portuguese-HuggingFace-fastaiv2.ipynb)/[blog](https://medium.com/@pierre_guillou/faster-than-training-from-scratch-fine-tuning-the-english-gpt-2-in-any-language-with-hugging-f2ec05c98787) shared by [Pierre Guillou](https://medium.com/@pierre_guillou), where Pierre Guillou fine-tuned the English GPT-2 model on a Portuguese dataset.
+
+Frameworks used include HuggingFace's [Transformers](https://huggingface.co/transformers) and fast.ai's [Deep Learning library](https://docs.fast.ai/). PyTorch was used as the backend framework during training, but the model remains compatible with TensorFlow nonetheless.
+
+## Model
+| Model             | #params | Arch.       | Training /Validation data (text)      |
+|-------------------|---------|-------------|---------------------------------------|
+| `indo-gpt2-small` |  124M   | GPT-2 Small | Indonesian Wikipedia (275 MB of text) |
+
+## Evaluation Results
+The model was trained for only 1 epoch and the following is the final result once the training ended.
+
+| epoch | train loss | valid loss | perplexity | total time |
+|-------|------------|------------|------------|------------|
+|   0   |    2.981   |    2.936   |    18.85   |   2:45:25  |
+
+## How to Use (PyTorch)
+### Load Model and Byte-level Tokenizer
+```python
+from transformers import GPT2TokenizerFast, GPT2LMHeadModel
+pretrained_name = "w11wo/indo-gpt2-small"
+tokenizer = GPT2TokenizerFast.from_pretrained(pretrained_name)
+tokenizer.model_max_length = 1024
+model = GPT2LMHeadModel.from_pretrained(pretrained_name)
+```
+
+### Generate a Sequence
+```python
+# sample prompt
+prompt = "Nama saya Budi, dari Indonesia"
+input_ids = tokenizer.encode(prompt, return_tensors='pt')
+model.eval()
+
+# generate output using top-k sampling
+sample_outputs = model.generate(input_ids, 
+                                pad_token_id=50256,
+                                do_sample=True, 
+                                max_length=40, 
+                                min_length=40,
+                                top_k=40,
+                                num_return_sequences=1)
+
+for i, sample_output in enumerate(sample_outputs):
+    print(tokenizer.decode(sample_output.tolist()))
+```
+
+## Disclaimer
+Do remember that although the dataset originated from Wikipedia, the model may not always generate factual texts. Additionally, the biases which came from the Wikipedia articles may be carried over into the results of this model.
+
+## Credits
+Major thanks to Pierre Guillou for sharing his work, which did not only enable me to realize this project but also taught me tons of new, exciting stuff.
+
+## Author
+Indo GPT-2 Small 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.