Created README.md; Added essential model information

README.md

@@ -0,0 +1,63 @@
+---
+language: jv
+tags:
+- javanese-gpt2-small-imdb
+license: mit
+datasets:
+- w11wo/imdb-javanese
+widget:
+- text: "Train to Busan yaiku film sing digawe ing Korea Selatan"
+---
+
+## Javanese GPT-2 Small IMDB
+Javanese GPT-2 Small IMDB is a causal 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 Javanese IMDB movie reviews.
+
+The model was originally the pretrained [Javanese GPT-2 Small model](https://huggingface.co/w11wo/javanese-gpt2-small) and is later fine-tuned on the Javanese IMDB movie review dataset. It achieved a perplexity of 55.09 on the validation dataset. Many of the techniques used are based on a Hugging Face tutorial [notebook](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling.ipynb) written by [Sylvain Gugger](https://github.com/sgugger).
+
+Hugging Face's [Transformers]((https://huggingface.co/transformers)) library was used to train the model -- utilizing the base GPT-2 model and their `Trainer` class. 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) |
+|----------------------------|----------|-----------------|---------------------------------|
+| `javanese-gpt2-small-imdb` |   124M   |   GPT-2 Small   | Javanese IMDB (47.5 MB of text) |
+
+## Evaluation Results
+The model was trained for 5 epochs and the following is the final result once the training ended.
+
+| train loss | valid loss | perplexity | total time |
+|------------|------------|------------|------------|
+|    3.789   |    4.008   |   55.09    |   1:9:57   |
+
+## How to Use (PyTorch)
+### As Causal Language Model
+```python
+from transformers import pipeline
+
+pretrained_name = "w11wo/javanese-gpt2-small-imdb"
+
+nlp = pipeline(
+    "text-generation",
+    model=pretrained_name,
+    tokenizer=pretrained_name
+)
+
+nlp("Jenengku Budi, saka Indonesia")
+```
+### Feature Extraction in PyTorch
+```python
+from transformers import GPT2LMHeadModel, GPT2TokenizerFast
+
+pretrained_name = "w11wo/javanese-gpt2-small-imdb"
+model = GPT2LMHeadModel.from_pretrained(pretrained_name)
+tokenizer = GPT2TokenizerFast.from_pretrained(pretrained_name)
+
+prompt = "Indonesia minangka negara gedhe."
+encoded_input = tokenizer(prompt, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+## Disclaimer
+Do consider the biases which came from the IMDB review that may be carried over into the results of this model.
+
+## Author
+Javanese 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.