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"""
This is a simple application for sentence embeddings: clustering
Sentences are mapped to sentence embeddings and then k-mean clustering is applied.
"""
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
embedder = SentenceTransformer('all-MiniLM-L6-v2')
# Corpus with example sentences
corpus = ['A man is eating food.',
'A man is eating a piece of bread.',
'A man is eating pasta.',
'The girl is carrying a baby.',
'The baby is carried by the woman',
'A man is riding a horse.',
'A man is riding a white horse on an enclosed ground.',
'A monkey is playing drums.',
'Someone in a gorilla costume is playing a set of drums.',
'A cheetah is running behind its prey.',
'A cheetah chases prey on across a field.'
]
corpus_embeddings = embedder.encode(corpus)
# Perform kmean clustering
num_clusters = 5
clustering_model = KMeans(n_clusters=num_clusters)
clustering_model.fit(corpus_embeddings)
cluster_assignment = clustering_model.labels_
clustered_sentences = [[] for i in range(num_clusters)]
for sentence_id, cluster_id in enumerate(cluster_assignment):
clustered_sentences[cluster_id].append(corpus[sentence_id])
for i, cluster in enumerate(clustered_sentences):
print("Cluster ", i+1)
print(cluster)
print("")
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