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Add section headings for parameters #1348

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May 22, 2017
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Add section headings for parameters #1348

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86609fe
Adding headers for different parameters, small grammar fixes.
MikeTheReader May 22, 2017
4bce206
Missing comma
MikeTheReader May 22, 2017
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14 changes: 8 additions & 6 deletions docs/notebooks/word2vec.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@
"metadata": {},
"source": [
"## Preparing the Input\n",
"Starting from the beginning, gensim’s `word2vec` expects a sequence of sentences as its input. Each sentence a list of words (utf8 strings):"
"Starting from the beginning, gensim’s `word2vec` expects a sequence of sentences as its input. Each sentence is a list of words (utf8 strings):"
]
},
{
Expand Down Expand Up @@ -276,7 +276,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## More data would be nice\n",
"### More data would be nice\n",
"For the following examples, we'll use the [Lee Corpus](https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/test/test_data/lee_background.cor) (which you already have if you've installed gensim):"
]
},
Expand Down Expand Up @@ -324,8 +324,8 @@
"source": [
"## Training\n",
"`Word2Vec` accepts several parameters that affect both training speed and quality.\n",
"\n",
"One of them is for pruning the internal dictionary. Words that appear only once or twice in a billion-word corpus are probably uninteresting typos and garbage. In addition, there’s not enough data to make any meaningful training on those words, so it’s best to ignore them:"
"\n### min_count\n",
"`min_count` is for pruning the internal dictionary. Words that appear only once or twice in a billion-word corpus are probably uninteresting typos and garbage. In addition, there’s not enough data to make any meaningful training on those words, so it’s best to ignore them:"
]
},
{
Expand Down Expand Up @@ -365,6 +365,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### size\n",
"`size` is the number of dimensions (N) of the N-dimensional space that gensim Word2Vec maps the words onto.\n",
"\n",
"Bigger size values require more training data, but can lead to better (more accurate) models. Reasonable values are in the tens to hundreds."
Expand Down Expand Up @@ -407,7 +408,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The last of the major parameters (full list [here](http://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.Word2Vec)) is for training parallelization, to speed up training:"
"### workers\n",
"`workers`, the last of the major parameters (full list [here](http://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.Word2Vec)) is for training parallelization, to speed up training:"
]
},
{
Expand Down Expand Up @@ -471,7 +473,7 @@
"## Evaluating\n",
"`Word2Vec` training is an unsupervised task, there’s no good way to objectively evaluate the result. Evaluation depends on your end application.\n",
"\n",
"Google have released their testing set of about 20,000 syntactic and semantic test examples, following the “A is to B as C is to D” task. It is provided in the 'datasets' folder.\n",
"Google has released their testing set of about 20,000 syntactic and semantic test examples, following the “A is to B as C is to D” task. It is provided in the 'datasets' folder.\n",
"\n",
"For example a syntactic analogy of comparative type is bad:worse;good:?. There are total of 9 types of syntactic comparisons in the dataset like plural nouns and nouns of opposite meaning.\n",
"\n",
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