How does the Gensim Fasttext pre-trained model get vectors for out-of-vocabulary words?

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?

Silique

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.

Barrow

Thanks. So this means, given a pre-trained fastext model, if I give a string or whole text document, then it lookups vector for each word in the string (if exists in vocab) or if the word doesn't exist in vocab , it creates a vector of the unknown word by looking up the character ngram of that unknown word and then summing the character ngram of that unknown word to get the vector for that unknown word. It does the same above thing for each word in a phrase or sentence and then finally sums all the vectors to get a final vector for the whole sentence? Is this correct understanding? – Silique

If you access the vectors as in your example code (model.wv[mystring]), then mystring is treated as a single word - it will not be automatically tokenized for you. If mystring is a sentence or longer this is probably not what you want. If you tokenize it (model.wv[mystring.split(' ')]) then gensim will return a 2D array of all the word vectors, which are caculated as you describe. This interface will not generate a single vector for the whole sentence/document, there are separate interfaces for that. – Barrow

Do you know of interface which can provide a single vector for a sentence? Like word2vec has doc2vec but I don't want to use word2vec as I am using pre-trained and out of words vocabulary is not there. Anything with fastext or something new embeddings? Or do you think splitting a sentence and getting vectors of all words and summing/avg them (by no. of words in sentence) can be used as a vector for whole sentence? This is what they do in doc2vec too I guess – Silique

I don't think Gensim has any sentence/document to vector interface besides Doc2Vec; creating good sentence/document vectors is still very much a research problem. That said, taking an average of the vectors of words in a sentence is one simple way to get a sentence vector, which may or may not work well for your application. You can read more about it here, for example: github.com/facebookresearch/fastText/issues/26 – Barrow

I have word embeddings for 10 million words which were trained on a huge corpus. Now can I design some char RNN to use these word embeddings and generate embeddings for out of vocab words? – Condonation

@Uma maybe, but that's not how fastext works - it trains the ngram and word vectors at the same time. – Barrow

@Barrow So there is no way we can use these pre-trained vectors to build some model which could give us vectors for out of vocab words? Do we have a GPU implementation for FastText? I have a huge corpus to train and with CPU implementation it could take months. Any suggestion on this? – Condonation

There is no GPU fasttext. I'm not sure why you're asking this in a comment, you should create a new question. – Barrow