对于 ElMo、FastText 和 Word2Vec，我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 做同样的事情（使用来自拥抱脸的 BERT python 包），但是我不太熟悉如何提取原始单词/句子向量以便将它们输入到聚类算法中。我知道 BERT 可以输出句子表示 - 那么我将如何从句子中实际提取原始向量呢？
任何信息都有帮助。
您可以使用句子转换器来生成句子嵌入。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量为数百万或更多，则可以使用基于 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。
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生成句子的嵌入。
这就是您如何为需要聚类的句子列表生成 bert 向量的方法。在bert-as-service存储库中解释得非常好： 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`
下载https://github.com/google-research/bert上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。
Bert 在每个样本/句子的开头添加一个特殊的 [CLS] 标记。在对下游任务进行微调之后，这个 [CLS] 标记或 pooled_output 的嵌入，他们在拥抱脸实现中调用它表示句子嵌入。
但是我认为您没有标签，因此您将无法进行微调，因此您不能将 pooled_output 用作句子嵌入。相反，您应该在 encoded_layers 中使用词嵌入，它是一个尺寸为 (12,seq_len, 768) 的张量。在这个张量中，您有来自 Bert 的 12 层中的每一层的嵌入（维度 768）。要获得词嵌入，您可以使用最后一层的输出，您可以连接或求和最后 4 层的输出，依此类推。
这是提取特征的脚本：https ://github.com/ethanjperez/pytorch-pretrained-BERT/blob/master/examples/extract_features.py
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
该库有一些代码示例来执行聚类：
"""
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])
"""
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("")
"""
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("")
不确定您是否仍然需要它，但最近一篇论文提到了如何使用文档嵌入来聚类文档并从每个集群中提取单词来表示一个主题。这是链接： https ://arxiv.org/pdf/2008.09470.pdf，https : //github.com/ddangelov/Top2Vec
受上述论文的启发，这里提到了另一种使用 BERT 生成句子嵌入的主题建模算法： https ://towardsdatascience.com/topic-modeling-with-bert-779f7db187e6，https : //github.com/MaartenGr/BERTopic
上述两个库提供了从语料库中提取主题的端到端解决方案。但如果您只对生成句子嵌入感兴趣，请查看 Gensim 的 doc2vec ( https://radimrehurek.com/gensim/models/doc2vec.html ) 或句子转换器 ( https://github.com/UKPLab/sentence -transformers），如其他答案中所述。如果您使用句子转换器，建议您在特定领域的语料库上训练模型以获得良好的结果。