How does the Gensim Fasttext pre-trained model get

Question

I am using gensim to load pre-trained fasttext model. I downloaded the English wikipedia trained model from fasttext website.

here is the code I wrote to load the pre-trained model:

from gensim.models import FastText as ft
model=ft.load_fasttext_format("wiki.en.bin")

I try to check if the following phrase exists in the vocal(which rare chance it would as these are pre-trained model).

print("internal executive" in model.wv.vocab)
print("internal executive" in model.wv)

False
True

So the phrase "internal executive" is not present in the vocabulary but we still have the word vector corresponding to that.

model.wv["internal executive"]
Out[46]:
array([ 0.0210917 , -0.15233646, -0.1173932 , -0.06210957, -0.07288644,
       -0.06304111,  0.07833624, -0.17026938, -0.21922196,  0.01146349,
       -0.13639058,  0.17283678, -0.09251394, -0.17875175,  0.01339212,
       -0.26683623,  0.05487974, -0.11843193, -0.01982722,  0.37037706,
       -0.24370994,  0.14269598, -0.16363597,  0.00328478, -0.16560239,
       -0.1450972 , -0.24787527, -0.01318423,  0.03277111,  0.16175713,
       -0.19367714,  0.16955379,  0.1972683 ,  0.09044111,  0.01731548,
       -0.0034324 , -0.04834719,  0.14321515,  0.01422525, -0.08803893,
       -0.29411593, -0.1033244 ,  0.06278021,  0.16452256,  0.0650492 ,
        0.1506474 , -0.14194389,  0.10778475,  0.16008648, -0.07853138,
        0.2183501 , -0.25451994, -0.0345991 , -0.28843886,  0.19964759,
       -0.10923116,  0.26665714, -0.02544454,  0.30637854,  0.04568949,
       -0.04798719, -0.05769338,  0.25762403, -0.05158515, -0.04426906,
       -0.19901046,  0.00894193, -0.17269588, -0.24747233, -0.19061406,
        0.14322804, -0.10804397,  0.4002605 ,  0.01409482, -0.04675362,
        0.10039093,  0.07260711, -0.0938239 , -0.20434211,  0.05741301,
        0.07592541, -0.02921724,  0.21137556, -0.23188967, -0.23164661,
       -0.4569614 ,  0.07434579,  0.10841205, -0.06514647,  0.01220404,
        0.02679767,  0.11840229,  0.2247431 , -0.1946325 , -0.0990666 ,
       -0.02524677,  0.0801085 ,  0.02437297,  0.00674876,  0.02088535,
        0.21464555, -0.16240154,  0.20670174, -0.21640894,  0.03900698,
        0.21772243,  0.01954809,  0.04541844,  0.18990673,  0.11806394,
       -0.21336791, -0.10871669, -0.02197789, -0.13249406, -0.20440844,
        0.1967368 ,  0.09804545,  0.1440366 , -0.08401451, -0.03715726,
        0.27826542, -0.25195453, -0.16737154,  0.3561183 , -0.15756823,
        0.06724873, -0.295487  ,  0.28395334, -0.04908851,  0.09448399,
        0.10877471, -0.05020981, -0.24595442, -0.02822314,  0.17862654,
        0.06452435, -0.15105674, -0.31911567,  0.08166212,  0.2634299 ,
        0.17043628,  0.10063848,  0.0687021 , -0.12210461,  0.10803893,
        0.13644943,  0.10755012, -0.09816817,  0.11873955, -0.03881042,
        0.18548298, -0.04769253, -0.01511982, -0.08552645, -0.05218676,
        0.05387992,  0.0497043 ,  0.06922272, -0.0089245 ,  0.24790663,
        0.27209425, -0.04925154, -0.08621719,  0.15918174,  0.25831223,
        0.01654229, -0.03617229, -0.13490392,  0.08033483,  0.34922174,
       -0.01744722, -0.16894792, -0.10506647,  0.21708378, -0.22582002,
        0.15625793, -0.10860757, -0.06058934, -0.25798836, -0.20142137,
       -0.06613475, -0.08779443, -0.10732629,  0.05967236, -0.02455976,
        0.2229451 , -0.19476262, -0.2720119 ,  0.03687386, -0.01220259,
        0.07704347, -0.1674307 ,  0.2400516 ,  0.07338555, -0.2000631 ,
        0.13897157, -0.04637206, -0.00874449, -0.32827383, -0.03435039,
        0.41587186,  0.04643605,  0.03352945, -0.13700874,  0.16430037,
       -0.13630766, -0.18546128, -0.04692861,  0.37308362, -0.30846512,
        0.5535561 , -0.11573419,  0.2332801 , -0.07236694, -0.01018955,
        0.05936847,  0.25877884, -0.2959846 , -0.13610311,  0.10905041,
       -0.18220575,  0.06902339, -0.10624941,  0.33002165, -0.12087796,
        0.06742091,  0.20762768, -0.34141317,  0.0884434 ,  0.11247049,
        0.14748637,  0.13261876, -0.07357208, -0.11968047, -0.22124515,
        0.12290633,  0.16602683,  0.01055585,  0.04445777, -0.11142147,
        0.00004863,  0.22543314, -0.14342701, -0.23209116, -0.00003538,
        0.19272381, -0.13767233,  0.04850799, -0.281997  ,  0.10343244,
        0.16510887,  0.08671653, -0.24125539,  0.01201926,  0.0995285 ,
        0.09807415, -0.06764816, -0.0206733 ,  0.04697794,  0.02000999,
        0.05817033,  0.10478792,  0.0974884 , -0.01756372, -0.2466861 ,
        0.02877498,  0.02499748, -0.00370895, -0.04728201,  0.00107118,
       -0.21848503,  0.2033032 , -0.00076264,  0.03828803, -0.2929495 ,
       -0.18218371,  0.00628893,  0.20586628,  0.2410889 ,  0.02364616,
       -0.05220835, -0.07040054, -0.03744286, -0.06718048,  0.19264086,
       -0.06490505,  0.27364203,  0.05527219, -0.27494466,  0.22256687,
        0.10330909, -0.3076979 ,  0.04852265,  0.07411488,  0.23980476,
        0.1590279 , -0.26712465,  0.07580928,  0.05644221, -0.18824042],

Now my confusion is that Fastext creates vectors for character ngrams of a word too. So for a word "internal" it will create vectors for all its character ngrams including the full word and then the final word vector for the word is the sum of its character ngrams.

However, how it is still able to give me vector of a word or even the whole sentence? Isn't fastext vector is for a word and its ngram? So what are these vector I am seeing for the phrase when its clearly two words?

Answer 1

From the paper Enriching Word Vectors with Subword Information:

Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the morphology of words, by assigning a distinct vector to each word. This is a limitation, especially for languages with large vocabularies and many rare words. In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram; words being represented as the sum of these representations.

So out-of-vocab words are represented as the sum of character ngram vectors. While the intent is to handle out-of-vocab words (unks) like "blargfizzle", it also handles phrases like your input.

If you look at the implementation of the vectors in Gensim you can see this is indeed what it's doing (along with normalization and hashing etc) - I added some comments starting with XXX:

def word_vec(self, word, use_norm=False):
    """
    Accept a single word as input.
    Returns the word's representations in vector space, as a 1D numpy array.
    If `use_norm` is True, returns the normalized word vector.
    """
    if word in self.vocab:
        # XXX in-vocab terms return with a simple lookup
        return super(FastTextKeyedVectors, self).word_vec(word, use_norm)
    else:
        # from gensim.models.fasttext import compute_ngrams
        # XXX Initialize the vector for the unk
        word_vec = np.zeros(self.vectors_ngrams.shape[1], dtype=np.float32)
        ngrams = _compute_ngrams(word, self.min_n, self.max_n)
        if use_norm:
            ngram_weights = self.vectors_ngrams_norm
        else:
            ngram_weights = self.vectors_ngrams
        ngrams_found = 0
        for ngram in ngrams:
            ngram_hash = _ft_hash(ngram) % self.bucket
            if ngram_hash in self.hash2index:
                # XXX add the vector for the ngram to the unk vector
                word_vec += ngram_weights[self.hash2index[ngram_hash]]
                ngrams_found += 1
        if word_vec.any():
            return word_vec / max(1, ngrams_found)
        else:  # No ngrams of the word are present in self.ngrams
            raise KeyError('all ngrams for word %s absent from model' % word)

Note that this doesn't mean it can provide vectors for any arbitrary string - it still needs to have data for at least some of the ngrams in an unk, so a string like xwkxwkzrw or 天爾遠波 will probably fail to return anything if your vectors are trained on English.