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-# todo 
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+# Todo 
+- [ ] Update these docs
+
+Getting Started
+---
+
+## Download and Extract the Wikipedia corpus
+
+In Linux, you can do all the following steps automatically with [prepare_wiki.sh](./prepare_wiki.sh)
+
+**Manual Instructions**
+
+We use the [WikiExtractor.py](http://medialab.di.unipi.it/wiki/Wikipedia_Extractor). It is a Python script that extracts and cleans text from a [Wikipedia database dump](http://download.wikimedia.org/).
+
+At the end of this step, you should have the following directory structure inside ulmfit:
+```bash
+
+|- data
+  |- wiki
+  |- wiki_dumps
+  |- wiki_extr
+|- wikiextractor
+```
+The extracted data should be in the folder `wiki_extr` -> language name e.g.`en` (english), `fr` (french) `hi` (hindi) and so on. 
+
+## Create and Post Process WikiText
+
+### Get the Dependencies
+
+**Python Fire**: To install Python Fire with pip, run: `pip install fire`
+
+To install Python Fire with conda, run: `conda install fire -c conda-forge`
+
+**Moses Tokenizer**: To install Moses Tokenizer: `pip install -U sacremoses`
+
+Use the Python script [create_wikitext.py](./create_wikitext.py) to process the extracted Wikipedia documents. 
+
+### Create and Post-Process 
+If you used the automated shell script from previous step, this might look something like
+```bash
+python create_wikitext.py -i data/wiki_extr/hi -o data/hindi -l hi
+``` 
+for hindi (unicode: 'hi')
+
+This should create two splits of your Wikimedia Dumps: a small and large one. 
+
+_**Then**_, use the [postprocess_wikitext.py](./postprocess_wikitext.py) script to finish post processing. This processes numbers, builds a vocab, and limits the vocabulary size. This might look following for Hindi (`hi`)
+```bash
+python postprocess_wikitext.py -i data/hindi -l hi
+```