Yet Another Sentence Embedding Library

The goal of this library is to make it easy to transform lists of sentences or sets of sentences into a matrix of embeddings (eg. one per sentence). This can be done either at the sentence/document level or by grouping sentence embeddings into grouped embeddings.

Such matrices of documents can easily be queried using kd-trees (see notebook in examples) for the most similar document in training data to a queried sentence. It can also be used to cluster document groups together solely by the text in the campaign.

The results can be tested for quality on a handcrafted evaluation dataset by checking how well the sentence embeddings cluster around the natural clusters of the existing ad campaigns.

(Gensim) Weighed Sentence Embeddings with Gensim model

    import gensim.downloader as model_api
    import yase
    # Load pretrained gensim model
    model = model_api.load("glove-wiki-gigaword-300")
    # Tokenize list of sentences 
    tokens = yase.tokenize(data, lower=True, split=True)
    # get word weights for higher quality embeddings
    weights = yase.getWordWeights(data, "tf-idf")
    # create sentence embeddings from tokens
    my_embeddings = embedding.sentenceEmbedding(tokens, model, weights)

Running unit tests

python -m unittest discover tests