Update glove-word-vectors example for most recent version of torchtext

glove-word-vectors/glove-word-vectors.ipynb

@@ -48,52 +48,59 @@
    "source": [
     "## Loading word vectors\n",
     "\n",
-    "The `load_word_vectors` function will download and unpack the word vectors directly from http://nlp.stanford.edu/data/"
+    "Torchtext includes functions to download GloVe (and other) embeddings"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torchtext.vocab as vocab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loading word vectors from ./glove.6B.100d.pt\n",
       "Loaded 400000 words\n"
      ]
     }
    ],
    "source": [
-    "import torch\n",
-    "from torchtext.vocab import load_word_vectors\n",
+    "glove = vocab.GloVe(name='6B', dim=100)\n",
     "\n",
-    "wv_dict, wv_arr, wv_size = load_word_vectors('.', 'glove.6B', 100)\n",
-    "\n",
-    "print('Loaded', len(wv_arr), 'words')"
+    "print('Loaded {} words'.format(len(glove.itos)))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`load_word_vectors` returns a dictionary of words to indexes, and an array of actual vectors. To get a word vector get the index to get the vector:"
+    "The returned `GloVe` object includes attributes:\n",
+    "- `stoi` _string-to-index_ returns a dictionary of words to indexes\n",
+    "- `itos` _index-to-string_ returns an array of words by index\n",
+    "- `vectors` returns the actual vectors. To get a word vector get the index to get the vector:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
    "source": [
     "def get_word(word):\n",
-    "    return wv_arr[wv_dict[word]]"
+    "    return glove.vectors[glove.stoi[word]]"
    ]
   },
   {
@@ -109,14 +116,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
-    "def closest(d, n=10):\n",
-    "    all_dists = [(w, torch.dist(d, get_word(w))) for w in wv_dict]\n",
+    "def closest(vec, n=10):\n",
+    "    \"\"\"\n",
+    "    Find the closest words for a given vector\n",
+    "    \"\"\"\n",
+    "    all_dists = [(w, torch.dist(vec, get_word(w))) for w in glove.itos]\n",
     "    return sorted(all_dists, key=lambda t: t[1])[:n]"
    ]
   },
@@ -129,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "collapsed": true
    },
@@ -149,10 +159,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",