Update README.md

README.md

@@ -9,4 +9,19 @@ size_categories:
 - 100M<n<1B
 ---
 
-# tokenizer
+# Tokenizer
+
+This is a tokeniser created on a custom-written algorithm on a huge vocabulary of `~1B` tokens. These tokens are given in the files (such that they are `<2GB` each, making them trackable by Git LFS). The text corpus is from the `SlimPajama` dataset by cerebras and consists of the whole text and validation corpus.
+The final tokeniser is available in two versions (`0.5B` version - Val. data only and `1B` version - Val data + Test data, created using the same algo).
+The files includes the token counts, the text corpus used, individual lines/paras from SlimPajama as a list JSON, ordered tokeniser with token ids (in order of their counts), unordered tokeniser  with token ids.
+
+## To do:
+- Write custom code for the final tokenisation part (to break text into tokens)
+- Create a python library for using the tokeniser
+- Put the files on GitHub for a general overview
+- Release the experimentation notebook and the tokenisation code (as part of the library)
+- Make a writeup to explain the algo used
+
+`Note: algo used is no industry standard algo like Byte Pair encoding, infact i have not studied BPE yet, i wanted to create a tokeniser from scratch first without any idea of what is currently used, and then compare the two, so a lot of what i implement may have similarities to that in industry but may not provide some performance improvement`
+
+I am storing it on HF, not on GITHUB, because of storage issues, and due to easy availability for the AI community.