modified .travis and README.md

Author: shivam05011996

.travis.yml

@@ -2,10 +2,11 @@ language: python
 python:
 - '3.5'
 before_install:
-- wget 'https://s3.amazonaws.com/deepcompute-public-data/wordvecspace/small_test_data.tgz'
-  && tar xvzf small_test_data.tgz
-- export WORDVECSPACE_DATADIR='small_test_data'
+    - wget 'https://s3.amazonaws.com/deepcompute-public-data/wordvecspace/test_data-0_5_4.tgz'
+  && tar xvzf test_data-0_5_4.tgz
+- export WORDVECSPACE_DATADIR='test_data-0_5_4.tgz'
 install:
+- sudo apt update
 - pip install .[service]
 - sudo apt install libopenblas-base
 script:
@@ -15,8 +16,8 @@ deploy:
   skip_cleanup: true
   api-key:
     secure: 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
-  name: wordvecspace-0.5.3
-  tag_name: 0.5.3
+  name: wordvecspace-0.5.4
+  tag_name: 0.5.4
   on:
     repo: deep-compute/wordvecspace
   # pypitest

README.md

@@ -4,7 +4,7 @@ A high performance pure python module that helps in loading and performing opera
 This module has ability to the load data into memory using `WordVecSpaceMem` and it can also support performing operations on the data which is on the disk using `WordVecSpaceAnnoy` and                   `WordVecSpaceDisk`.
 
 ## Installation
-> Prerequisites: Python3.5.2
+> Prerequisites: >=Python3.5.2
 
 ```bash
 
@@ -23,7 +23,7 @@ word vector space data. Here are two ways to get that.
 
 ```bash
 $ wget https://s3.amazonaws.com/deepcompute-public-data/wordvecspace/test_data-0_5_4.tgz
-$ tar test_data-0_5_4.tgz
+$ tar zxvf test_data-0_5_4.tgz
 ```
 
 > NOTE: We got this data by downloading the `text8` corpus
@@ -42,7 +42,7 @@ $ git clone https://github.com/tmikolov/word2vec.git
 
 # 1. Navigate to the folder word2vec
 # 2. open demo-word.sh for editing
-# 3. Edit the command "time ./word2vec -train text8 -output vectors.bin -cbow 1 -size 200 -window 8 -negative 25 -hs 0 -sample 1e-4 -threads 20 -binary 1 -iter 15" ----to----> "time ./word2vec -train     text8 -output vectors.bin -cbow 1 -size 5 -window 8 -negative 25 -hs 0 -sample 1e-4 -threads 20 -binary 1 -save-vocab vocab.txt -iter 15" to get vocab.txt file also as output.
+# 3. Edit the command "time ./word2vec -train text8 -output vectors.bin -cbow 1 -size 200 -window 8 -negative 25 -hs 0 -sample 1e-4 -threads 20 -binary 1 -iter 15" ----to----> "time ./word2vec -train text8 -output vectors.bin -cbow 1 -size 5 -window 8 -negative 25 -hs 0 -sample 1e-4 -threads 20 -binary 1 -save-vocab vocab.txt -iter 15" to get vocab.txt file also as output.
 # 4. Run demo-word.sh
 
 $ chmod +x demo-word.sh
@@ -97,7 +97,7 @@ $ wordvecspace convert /home/user/bindata /home/user/output_dir
 
 `WordVecSpaceMem` and `WordVecSpaceDisk` is a bruteforce algorithm which compares given word with all the words in the vector space
 
-`WordVecSpaceAnnoy` takes wordvecspace output_dir as input and creates annoy indexes in another file (index file). Using this file `annoy` gives approximate results quickly. For better understanding of   `Annoy` please go through this [link](https://github.com/spotify/annoy)
+`WordVecSpaceAnnoy` takes wordvecspace output_dir as input and creates annoy indexes in another file (index file). Using this file `annoy` gives approximate results quickly. For better understanding of `Annoy` please go through this [link](https://github.com/spotify/annoy)
 
 As we have seen how to import `WordVecSpaceDisk` above, let us look at `WordVecSpaceAnnoy` and `WordVecSpaceMem`