如何使用 BERT 聚类相似的句子

Question

对于 ElMo、FastText 和 Word2Vec，我正在对句子中的词嵌入进行平均，并使用 HDBSCAN/KMeans 聚类对相似的句子进行分组。

在这篇短文中可以看到一个很好的实现示例：http : //ai.intelligentonlinetools.com/ml/text-clustering-word-embedding-machine-learning/

我想使用 BERT（使用 Hugging face 中的 BERT python 包）做同样的事情，但是我不太熟悉如何提取原始词/句子向量以将它们输入到聚类算法中。我知道 BERT 可以输出句子表示 - 那么我实际上如何从句子中提取原始向量呢？

任何信息都有帮助。

Answer 1

您可以使用Sentence Transformers生成句子嵌入。与从 bert-as-service 获得的嵌入相比，这些嵌入更有意义，因为它们已经过微调，使得语义相似的句子具有更高的相似度得分。如果要聚类的句子数以百万计或更多，您可以使用基于 FAISS 的聚类算法，因为像聚类算法这样的普通 K 均值需要二次时间。

Answer 2

您需要首先为句子生成 bert embeddidngs。bert-as-service 提供了一种非常简单的方法来为句子生成嵌入。

这就是如何为需要聚类的句子列表生成 bert 向量。在 bert-as-service 仓库中有很好的解释：https : //github.com/hanxiao/bert-as-service

安装：

pip install bert-serving-server  # server
pip install bert-serving-client  # client, independent of `bert-serving-server`

在https://github.com/google-research/bert下载可用的预训练模型之一

启动服务：

bert-serving-start -model_dir /your_model_directory/ -num_worker=4 

为句子列表生成向量：

from bert_serving.client import BertClient
bc = BertClient()
vectors=bc.encode(your_list_of_sentences)

这将为您提供一个向量列表，您可以将它们写入 csv 并使用任何聚类算法，因为句子被简化为数字。

Answer 3

正如Subham Kumar 提到的，可以使用这个 Python 3 库来计算句子相似度： https: //github.com/UKPLab/sentence-transformers

该库有一些执行聚类的代码示例：

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 for computing sentence embeddings. We use one trained for similar questions detection
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')

# We donwload the Quora Duplicate Questions Dataset (https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs)
# and find similar question in it
url = "http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv"
dataset_path = "quora_duplicate_questions.tsv"
max_corpus_size = 50000 # We limit our corpus to only the first 50k questions


# Check if the dataset exists. If not, download and extract
# Download dataset if needed
if not os.path.exists(dataset_path):
    print("Download dataset")
    util.http_get(url, dataset_path)

# Get all unique sentences from the file
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()

#Two parameters to tune:
#min_cluster_size: Only consider cluster that have at least 25 elements
#threshold: Consider sentence pairs with a cosine-similarity larger than threshold as similar
clusters = util.community_detection(corpus_embeddings, min_community_size=25, threshold=0.75)

print("Clustering done after {:.2f} sec".format(time.time() - start_time))

#Print for all clusters the top 3 and bottom 3 elements
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 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("")

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 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)

# Normalize the embeddings to unit length
corpus_embeddings = corpus_embeddings /  np.linalg.norm(corpus_embeddings, axis=1, keepdims=True)

# Perform kmean clustering
clustering_model = AgglomerativeClustering(n_clusters=None, distance_threshold=1.5) #, affinity='cosine', linkage='average', distance_threshold=0.4)
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("")