Added instructions for colab

week3/week3-Embeddings.ipynb

@@ -85,7 +85,7 @@
     "\n",
     "To solve the problem, you will use two different models of embeddings:\n",
     "\n",
-    " - [Pre-trained word vectors](https://code.google.com/archive/p/word2vec/) from Google which were trained on a part of Google News dataset (about 100 billion words). The model contains 300-dimensional vectors for 3 million words and phrases. You need to download it by following this [link](https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing).\n",
+    " - [Pre-trained word vectors](https://code.google.com/archive/p/word2vec/) from Google which were trained on a part of Google News dataset (about 100 billion words). The model contains 300-dimensional vectors for 3 million words and phrases. You need to download it by following this [link](https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing). If you run this task in Google Colab, you could download the vectors directly (`wget` command) from one of the links mentioned [here](https://groups.google.com/forum/#!topic/word2vec-toolkit/z0Aw5powUco).\n",
     " - Representations using StarSpace on StackOverflow data sample. You will need to train them from scratch."
    ]
   },
@@ -276,6 +276,8 @@
    },
    "outputs": [],
    "source": [
+    "import nltk\n",
+    "nltk.download('stopwords')\n",
     "from util import array_to_string"
    ]
   },