正如Subham Kumar在提到的那样，可以使用这个Python 3库计算句子相似度：https://github.com/UKPLab/sentence-transformers
该库有一些代码示例来执行聚类：
fast_clustering.py：
"""
This is a more complex example on performing clustering on large scale dataset.
This examples find in a large set of sentences local communities, i.e., groups of sentences that are highly
similar. You can freely configure the threshold what is considered as similar. A high threshold will
only find extremely similar sentences, a lower threshold will find more sentence that are less similar.
A second parameter is 'min_community_size': Only communities with at least a certain number of sentences will be returned.
The method for finding the communities is extremely fast, for clustering 50k sentences it requires only 5 seconds (plus embedding comuptation).
In this example, we download a large set of questions from Quora and then find similar questions in this set.
"""
from sentence_transformers import SentenceTransformer, util
import os
import csv
import time
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
url = "http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv"
dataset_path = "quora_duplicate_questions.tsv"
max_corpus_size = 50000
if not os.path.exists(dataset_path):
print("Download dataset")
util.http_get(url, dataset_path)
corpus_sentences = set()
with open(dataset_path, encoding='utf8') as fIn:
reader = csv.DictReader(fIn, delimiter='\t', quoting=csv.QUOTE_MINIMAL)
for row in reader:
corpus_sentences.add(row['question1'])
corpus_sentences.add(row['question2'])
if len(corpus_sentences) >= max_corpus_size:
break
corpus_sentences = list(corpus_sentences)
print("Encode the corpus. This might take a while")
corpus_embeddings = model.encode(corpus_sentences, batch_size=64, show_progress_bar=True, convert_to_tensor=True)
print("Start clustering")
start_time = time.time()
clusters = util.community_detection(corpus_embeddings, min_community_size=25, threshold=0.75)
print("Clustering done after {:.2f} sec".format(time.time() - start_time))
for i, cluster in enumerate(clusters):
print("\nCluster {}, #{} Elements ".format(i+1, len(cluster)))
for sentence_id in cluster[0:3]:
print("\t", corpus_sentences[sentence_id])
print("\t", "...")
for sentence_id in cluster[-3:]:
print("\t", corpus_sentences[sentence_id])
kmeans.py:
"""
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('paraphrase-MiniLM-L6-v2')
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)
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("")
agglomerative.py:
"""
This is a simple application for sentence embeddings: clustering
Sentences are mapped to sentence embeddings and then agglomerative clustering with a threshold is applied.
"""
from sentence_transformers import SentenceTransformer
from sklearn.cluster import AgglomerativeClustering
import numpy as np
embedder = SentenceTransformer('paraphrase-MiniLM-L6-v2')
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)
corpus_embeddings = corpus_embeddings / np.linalg.norm(corpus_embeddings, axis=1, keepdims=True)
clustering_model = AgglomerativeClustering(n_clusters=None, distance_threshold=1.5)
clustering_model.fit(corpus_embeddings)
cluster_assignment = clustering_model.labels_
clustered_sentences = {}
for sentence_id, cluster_id in enumerate(cluster_assignment):
if cluster_id not in clustered_sentences:
clustered_sentences[cluster_id] = []
clustered_sentences[cluster_id].append(corpus[sentence_id])
for i, cluster in clustered_sentences.items():
print("Cluster ", i+1)
print(cluster)
print("")
