Abe2G/FastText-Amharic-Embedding-Vectors

Amharic FastText word vector. For both CBOW and SKIPGRAM Models # FastText-Amharic-Embedding-Vectors Toget word vectors and other related files, please refer into AM-Vectors Repository # Neural Word Embedding Word embeddings are one of the few currently successful applications of unsupervised learning. Their main benefit arguably is that they don't require expensive annotation, but can be derived from large unannotated corpora that are readily available. The term word embeddings is coined in 2003, but the eventual popularization of word embedding can be attributed to Mikolov et al., in 2013 who created word2vec, a toolkit that allows the seamless training and use of English pre-trained embedding. In 2016, Facebook released another predictive model called FastText that represent word information through sub-words or character n-grams. FastText is extension of word2vec model by extending character n-gram feature. # Word2Vec Word2Vec is the name given to a class of neural network models with two layer that, given an unlabeled training corpus, produce a vector for each word in the corpus that encodes its semantic information. Word2vec can utilize either of two model architectures to produce a distributed representation of words: continuous bag-of-words (CBOW) or continuous skip-gram. In the continuous bag-of-words architecture, the model predicts the current word from a window of surrounding context words. The order of context words does not influence prediction (bag-of-words assumption). In the continuous skip-gram architecture, the model uses the current word to predict the surrounding window of context words. The skip-gram architecture weighs nearby context words more heavily than more distant context words. CBOW is faster while skip-gram is slower but does a better job for infrequent words. The neurons in the hidden layer are all linear neurons. The input layer is set to have as many neurons as there are words in the vocabulary for training. The hidden layer size is set to the dimensionality of the resulting word vectors. The …