diff --git a/README.md b/README.md
index 5d0b0d8..ff136ac 100644
--- a/README.md
+++ b/README.md
@@ -9,13 +9,13 @@ State-of-the-Art Language Modeling and Text Classification in Malayalam Language
 
 
 ## Results
-We trained a Malayalam language model on the Wikipedia article dump from Oct, 2018. The Wikipedia dump had 55k+ articles. The difficuly in training a Malayalam language model is *text tokenization*, since [Malayalam is a highly inflectional and agglutinative language.](https://thottingal.in/blog/2017/11/26/towards-a-malayalam-morphology-analyser/) In the current model, we are using `nltk tokenizer` (will try better alternative in the future) and the vocab size is 30k. The language model was used to train a classifier which classifies a news into 5 categories (India, Kerala, Sports, Business, Entertainment). Our classifier came out with a whooping 92% accuracy in the classification task.  
+We trained a Malayalam language model on the Wikipedia article dump from Oct, 2018. The Wikipedia dump had 55k+ articles. The difficuly in training a Malayalam language model is *text tokenization*, since [Malayalam is a highly inflectional and agglutinative language.](https://thottingal.in/blog/2017/11/26/towards-a-malayalam-morphology-analyser/) In the current model, we are using `nltk tokenizer` (will try better alternative in the future) and the vocab size is 30k. The language model was used to train a classifier which classifies a news into 5 categories (India, Kerala, Sports, Business, Entertainment). Our classifier came out with a whopping 92% accuracy in the classification task.  
 
 
 ## Releases
 
-- Proccessed wikipedia dump of articles split into test and train.
-- Script and weights for Malayalam Language model.
+- Processed wikipedia dump of articles split into test and train.
+- Script and weights for Malayalam language model.
 - Malayalam text classifier with pretrained weights.
 - Inference code for text classifier.
 
@@ -39,9 +39,10 @@ If you are using virtualenvwrapper use the following steps:
 
 ## Usage
 ### Training language model with  preprocessed data:
-1. Download the pretrained language model folder, it contains the preprocessed test and train csv. If you would like to preproccess and retrain the LM using the latest dump article dump using the scripts provided [here](https://github.com/fastai/fastai/tree/master/courses/dl2/imdb_scripts).
+1. Download the pretrained language model folder, it contains the preprocessed test and train csv. If you would like to preprocess and retrain the LM using the latest dump article dump using the scripts provided [here](https://github.com/fastai/fastai/tree/master/courses/dl2/imdb_scripts).
 2. Create tokens:  
- `python lm/create_toks.py <path_to_processed_wiki_dump>`  
+ `python lm/create_toks.py <path_to_
+ essed_wiki_dump>`  
 eg: `python lm/create_toks.py /home/adamshamsudeen/mal/Vaaku2Vec/wiki/ml/`
 3. Create a token to id mapping:  
  `python lm/tok2id.py <path_to_processed_wiki_dump>`  
@@ -74,9 +75,9 @@ We manually tested the model on news from other leading news paper and the model
 
 
 ### TODO
-- [x] Malayalam Language modeling based on wikipedia articles.
-- [x] Release Trained Language Models weights.
-- [x] Malayalam Text classifier script.
+- [x] Malayalam language modeling based on wikipedia articles.
+- [x] Release trained language models weights.
+- [x] Malayalam text classifier script.
 - [ ] Benchmark with [mlmorph](https://gitlab.com/smc/mlmorph) for tokenization.
 - [ ] Benchmark with [Byte pair encoding for tokenization](https://nlp.h-its.org/bpemb/ml/)
 - [ ] UI to train and test classifier.