Sentence Clustering with BERT (SCB)

Sentence Clustering with BERT project which aim to use state-of-the-art BERT models to compute vectors for sentences. A few tools are also implemented to explore those vectors and how sentences are related to each others in the latent space.

Demonstration

• Load example data set :

from SCBert.load_data import DataLoader

cls = DataLoader().load_cls_fr()
data = cls.review

• Create vectors from raw data :

#How to transform raw french texts into vectors using BERT model. 
from SCBert.SCBert import Vectorizer

vectorizer = Vectorizer("flaubert_small")
#Here the small version of FLauBERT only has 6 layers and we will take layers 4 and 5 and mean pool them to create 
#a vector for each word, then mean pool all words vectors to have a unique vector for each text
text_vectors = vectorizer.vectorize(data, layers=[4,5], word_pooling_method="average", sentence_pooling_method="average")

• Explore the embedded space :

#How to explore the relation in your data. 
from SCBert.SCBert import EmbeddingExplorer

ee = EmbeddingExplorer(data,text_vectors)
labels = ee.cluster(k=3,  cluster_algo="quick_k-means")     #Cluster with k-means 
ee.extract_keywords(num_top_words=15)                       #Extract 15 keywords using Rake algorithm, then accessible with ee.keywords
ee.compute_coherence(vectorizer)                            #Compute coherence for the keywords in each cluster
ee.explore_cls(color_label=cls.code, 'PCA')                              #This function is here to explore a the repartition of cluster in the FULL cls dataset 

Built-in example

There is a built-in example that you can find here. It comes with it's own data which is the CLS-fr composed of Amazon reviews from different sources (DVD, CD, Livres)

Installation

You can either download the zip file or use the Pypi package that you can install with the following command :

> pip install SCBert

If you encounter problems during the installation it may be because of the multi-rake dependy with cld2-cffi. I will try to address this later on. To bypass, just follow the instructions :

> export CFLAGS="-Wno-narrowing"
> pip install cld2-cffi
> pip install multi-rake