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.
The library works out of the box with gensim models, sentence-transformers models, and fse models.
import yase
df = ...
# Gensim Model
import gensim.downloader as model_api
gensim_model = model_api.load("glove-wiki-gigaword-300")
gsb = yase.encoders.embed_column(df.body, model=gensim_model,verbose=True)
# sentence-transformers model
from sentence_transformers import SentenceTransformer
sb_model = SentenceTransformer('all-MiniLM-L6-v2')
rsb = yase.encoders.embed_column(df.body, model=sb_model, verbose=True)
# FSE model
from fse import Vectors, Average
vecs = Vectors.from_pretrained("glove-wiki-gigaword-50")
fse_model = Average(vecs)
fsb = yase.encoders.embed_column(df.body, model=fse_model,verbose=True)python -m unittest discover tests