Moved Package contents readme section to src/README.md.

Author: Russell

README.md

@@ -4,19 +4,19 @@
 -------------------------|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
 <li> frogs <li> toad <li> litoria <li> leptodactylidae <li> rana <li> lizard <li> eleutherodactylus | ![](http://nlp.stanford.edu/projects/glove/images/litoria.jpg)  |  ![](http://nlp.stanford.edu/projects/glove/images/leptodactylidae.jpg) |  ![](http://nlp.stanford.edu/projects/glove/images/rana.jpg) |  ![](http://nlp.stanford.edu/projects/glove/images/eleutherodactylus.jpg)
 
-We provide an implementation of the GloVe model for learning word representations. Please see the [project page](http://nlp.stanford.edu/projects/glove/) for more information.
-
 man -> woman             |  city -> zip | comparative -> superlative
 :-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
 ![](http://nlp.stanford.edu/projects/glove/images/man_woman_small.jpg)  |   ![](http://nlp.stanford.edu/projects/glove/images/city_zip_small.jpg) |  ![](http://nlp.stanford.edu/projects/glove/images/comparative_superlative_small.jpg)
 
+We provide an implementation of the GloVe model for learning word representations. Please see the [project page](http://nlp.stanford.edu/projects/glove/) for more information on the paper.
+
 ## Download pre-trained word vectors
 The links below contain word vectors obtained from the respective corpora. If you want word vectors trained on massive web datasets, you need only download one of these text files! Pre-trained word vectors are made available under the <a href="http://opendatacommons.org/licenses/pddl/">Public Domain Dedication and License</a>. 
 <div class="entry">
 <ul style="padding-left:0px; margin-top:0px; margin-bottom:0px">
-  <li> <a href="http://dumps.wikimedia.org/enwiki/20140102/">Wikipedia 2014</a> + <a href="https://catalog.ldc.upenn.edu/LDC2011T07">Gigaword 5</a> (6B tokens, 400K vocab, uncased, 50d, 100d, 200d, &amp; 300d vectors, 822 MB download): <a href="http://nlp.stanford.edu/data/wordvecs/glove.6B.zip">glove.6B.zip</a> </li>
   <li> Common Crawl (42B tokens, 1.9M vocab, uncased, 300d vectors, 1.75 GB download): <a href="http://nlp.stanford.edu/data/wordvecs/glove.42B.300d.zip">glove.42B.300d.zip</a> </li>
   <li> Common Crawl (840B tokens, 2.2M vocab, cased, 300d vectors, 2.03 GB download): <a href="http://nlp.stanford.edu/data/wordvecs/glove.840B.300d.zip">glove.840B.300d.zip</a> </li>
+  <li> <a href="http://dumps.wikimedia.org/enwiki/20140102/">Wikipedia 2014</a> + <a href="https://catalog.ldc.upenn.edu/LDC2011T07">Gigaword 5</a> (6B tokens, 400K vocab, uncased, 50d, 100d, 200d, &amp; 300d vectors, 822 MB download): <a href="http://nlp.stanford.edu/data/wordvecs/glove.6B.zip">glove.6B.zip</a> </li>
   <li> Twitter (2B tweets, 27B tokens, 1.2M vocab, uncased, 25d, 50d, 100d, &amp; 200d vectors, 1.42 GB download): <a href="http://nlp.stanford.edu/data/wordvecs/glove.twitter.27B.zip">glove.twitter.27B.zip</a> Ruby <a href="preprocess-twitter.rb">script</a> for preprocessing Twitter data </li>
 </ul>
 </div>
@@ -28,18 +28,7 @@ If the web datasets above don't match the semantics of your end use case, you ca
     $ cd glove && make
     $ ./demo.sh
 
-The demo.sh scipt downloads a small corpus, consisting of the first 100M characters of Wikipedia. It collects unigram counts, constructs and shuffles cooccurrence data, and trains a simple version of the GloVe model. It also runs a word analogy evaluation script in python. Continue reading for further usage details and instructions for how to run on your own corpus.
-
-### Package Contents
-This package includes four main tools:
-#### 1) vocab_count
-Constructs unigram counts from a corpus, and optionally thresholds the resulting vocabulary based on total vocabulary size or minimum frequency count. This file should already consist of whitespace-separated tokens. Use something like the Stanford Tokenizer (http://nlp.stanford.edu/software/tokenizer.shtml) first on raw text.
-#### 2) cooccur
-Constructs word-word cooccurrence statistics from a corpus. The user should supply a vocabulary file, as produced by 'vocab_count', and may specify a variety of parameters, as described by running './build/cooccur'.
-#### 3) shuffle
-Shuffles the binary file of cooccurrence statistics produced by 'cooccur'. For large files, the file is automatically split into chunks, each of which is shuffled and stored on disk before being merged and shuffled togther. The user may specify a number of parameters, as described by running './build/shuffle'.
-#### 4) glove
-Train the GloVe model on the specified cooccurrence data, which typically will be the output of the 'shuffle' tool. The user should supply a vocabulary file, as given by 'vocab_count', and may specify a number of other parameters, which are described by running './build/glove'.
+The demo.sh scipt downloads a small corpus, consisting of the first 100M characters of Wikipedia. It collects unigram counts, constructs and shuffles cooccurrence data, and trains a simple version of the GloVe model. It also runs a word analogy evaluation script in python to verify word vector quality. More details about training on your own corpus can be found by reading [demo.sh](https://github.com/stanfordnlp/GloVe/blob/master/demo.sh) or the [src/README.md](https://github.com/stanfordnlp/GloVe/tree/master/src)
 
 ### License
 All work contained in this package is licensed under the Apache License, Version 2.0. See the include LICENSE file.

src/README.md

@@ -0,0 +1,10 @@
+### Package Contents
+This package includes four main tools:
+#### 1) vocab_count
+Constructs unigram counts from a corpus, and optionally thresholds the resulting vocabulary based on total vocabulary size or minimum frequency count. This file should already consist of whitespace-separated tokens. Use something like the Stanford Tokenizer (http://nlp.stanford.edu/software/tokenizer.shtml) first on raw text.
+#### 2) cooccur
+Constructs word-word cooccurrence statistics from a corpus. The user should supply a vocabulary file, as produced by 'vocab_count', and may specify a variety of parameters, as described by running './build/cooccur'.
+#### 3) shuffle
+Shuffles the binary file of cooccurrence statistics produced by 'cooccur'. For large files, the file is automatically split into chunks, each of which is shuffled and stored on disk before being merged and shuffled togther. The user may specify a number of parameters, as described by running './build/shuffle'.
+#### 4) glove
+Train the GloVe model on the specified cooccurrence data, which typically will be the output of the 'shuffle' tool. The user should supply a vocabulary file, as given by 'vocab_count', and may specify a number of other parameters, which are described by running './build/glove'.