Adding link to word embeddings
"### What is Word2Vec?\n",
"Word2vec is a predictive model for learning word embeddings from text. Word embeddings are learned such that words that share common contexts in the corpus will be close together in the vector space. There are two different model architectures that can be used to produce word2vec embeddings: continuous bag-of-words (CBOW) or continuous skip-gram. The former uses a window of surrounding words (the \"context\") to predict the current word and the latter uses the current word to predict the surrounding context words. See this [tutorial](https://www.guru99.com/word-embedding-word2vec.html#3) on word2vec for more detailed background on the model.\n",
"\n",
- "For our purposes, we use pretrained word2vec word embeddings. These embeddings were trained on a Google News corpus and provide 300-dimensional embeddings (vectors) for 3 million English words. See the code below to load in these word embeddings."
+ "For our purposes, we use pretrained word2vec word embeddings. These embeddings were trained on a Google News corpus and provide 300-dimensional embeddings (vectors) for 3 million English words. See this link for the original source (https://code.google.com/archive/p/word2vec/) and see the code below to load in these word embeddings."
]
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- "fastText is an unsupervised algorithm created by Facebook Research for efficiently learning word embeddings. fastText is significantly different than word2vec or GloVe in that these two algorithms we saw earlier treat each word as the smallest possible unit to find an embedding for. Conversely, fastText assumes that words are formed by an n-gram of characters (i.e. 2-grams of the word \"language\" would be {la, an, ng, gu, ua, ag, ge}). The embedding for a word is then composed of the sum of these character n-grams. This has advantages when finding word embeddings for rare words and words not present in the dictionary, as these words can still be broken down into character n-grams. Typically, for smaller datasets, fastText performs better than word2vec or GloVe. See this [tutorial](https://fasttext.cc/docs/en/unsupervised-tutorial.html) on fastText for more detail. We will use the pretrained word embeddings for the English language (wiki.en.bin; these embeddings as well as embeddings for 293 other languages can be found [here](https://fasttext.cc/docs/en/pretrained-vectors.html)). These are 300-dimensional embeddings (vectors) trained on Wikipedia data."
+ "fastText is an unsupervised algorithm created by Facebook Research for efficiently learning word embeddings. fastText is significantly different than word2vec or GloVe in that these two algorithms we saw earlier treat each word as the smallest possible unit to find an embedding for. Conversely, fastText assumes that words are formed by an n-gram of characters (i.e. 2-grams of the word \"language\" would be {la, an, ng, gu, ua, ag, ge}). The embedding for a word is then composed of the sum of these character n-grams. This has advantages when finding word embeddings for rare words and words not present in the dictionary, as these words can still be broken down into character n-grams. Typically, for smaller datasets, fastText performs better than word2vec or GloVe. See this [tutorial](https://fasttext.cc/docs/en/unsupervised-tutorial.html) on fastText for more detail. We will use the pretrained word embeddings for the English language (wiki.en.bin; these embeddings as well as embeddings for 156 other languages can be found at https://fasttext.cc/docs/en/english-vectors.html). These are 300-dimensional embeddings (vectors) trained on Wikipedia data."
]
},
{
