In this post we will look at doc2vec word embedding model, how to build it or use pretrained embedding file. For practical example we will explore how to do text clustering with doc2vec model.
Doc2vec is an unsupervised computer algorithm to generate vectors for sentence/paragraphs/documents. The algorithm is an adaptation of word2vec which can generate vectors for words. Below you can see frameworks for learning word vector word2vec (left side) and paragraph vector doc2vec (right side). For learning doc2vec, the paragraph vector was added to represent the missing information from the current context and to act as a memory of the topic of the paragraph. 
If you need information about word2vec here are some posts:
Vector Representation of Text – Word Embeddings with word2vec
word2vec application –
Text Analytics Techniques with Embeddings
Using Pretrained Word Embeddinigs in Machine Learning
K Means Clustering Example with Word2Vec in Data Mining or Machine Learning
The vectors generated by doc2vec can be used for tasks like finding similarity between sentences / paragraphs / documents.  With doc2vec you can get vector for sentence or paragraph out of model without additional computations as you would do it in word2vec, for example here we used function to go from word level to sentence level:
Text Clustering with Word Embedding in Machine Learning
word2vec was very successful and it created idea to convert many other specific texts to vector. It can called “anything to vector”. So there are many different word embedding models that like doc2vec can convert more than one word to numeric vector.  Here are few examples:
tweet2vec Tweet2Vec: Character-Based Distributed Representations for Social Media
lda2vec Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec. Here is proposed model that learns dense word vectors jointly with Dirichlet-distributed latent document-level mixtures of topic vectors.
Topic2Vec Learning Distributed Representations of Topics
Med2vec Multi-layer Representation Learning for Medical Concepts
The list can go on. In the next section we will look how to load doc2vec and use for text clustering.
Building doc2vec Model
Here is the example for converting word paragraph to vector using own built doc2vec model. The example is taken from .
The script consists of the following main steps:
- build model using own text
- save model to file
- load model from this file
- infer vector representation
from gensim.test.utils import common_texts from gensim.models.doc2vec import Doc2Vec, TaggedDocument print (common_texts) """ output: [['human', 'interface', 'computer'], ['survey', 'user', 'computer', 'system', 'response', 'time'], ['eps', 'user', 'interface', 'system'], ['system', 'human', 'system', 'eps'], ['user', 'response', 'time'], ['trees'], ['graph', 'trees'], ['graph', 'minors', 'trees'], ['graph', 'minors', 'survey']] """ documents = [TaggedDocument(doc, [i]) for i, doc in enumerate(common_texts)] print (documents) """ output [TaggedDocument(words=['human', 'interface', 'computer'], tags=), TaggedDocument(words=['survey', 'user', 'computer', 'system', 'response', 'time'], tags=), TaggedDocument(words=['eps', 'user', 'interface', 'system'], tags=), TaggedDocument(words=['system', 'human', 'system', 'eps'], tags=), TaggedDocument(words=['user', 'response', 'time'], tags=), TaggedDocument(words=['trees'], tags=), TaggedDocument(words=['graph', 'trees'], tags=), TaggedDocument(words=['graph', 'minors', 'trees'], tags=), TaggedDocument(words=['graph', 'minors', 'survey'], tags=)] """ model = Doc2Vec(documents, size=5, window=2, min_count=1, workers=4) #Persist a model to disk: from gensim.test.utils import get_tmpfile fname = get_tmpfile("my_doc2vec_model") print (fname) #output: C:\Users\userABC\AppData\Local\Temp\my_doc2vec_model #load model from saved file model.save(fname) model = Doc2Vec.load(fname) # you can continue training with the loaded model! #If you’re finished training a model (=no more updates, only querying, reduce memory usage), you can do: model.delete_temporary_training_data(keep_doctags_vectors=True, keep_inference=True) #Infer vector for a new document: #Here our text paragraph just 2 words vector = model.infer_vector(["system", "response"]) print (vector) """ output [-0.08390492 0.01629403 -0.08274432 0.06739668 -0.07021132] """
Using Pretrained doc2vec Model
We can skip building embedding file step and use already built file. Here is an example how to do coding with pretrained word embedding file for representing test docs as vectors. The script is based on .
The below script is using pretrained on Wikipedia data doc2vec model from this location
Here is the link where you can find links to different pre-trained doc2vec and word2vec models and additional information.
You need to download zip file, unzip , put 3 files at some folder and provide path in the script. In this example it is “doc2vec/doc2vec.bin”
The main steps of the below script consist of just load doc2vec model and infer vectors.
import gensim.models as g import codecs model="doc2vec/doc2vec.bin" test_docs="data/test_docs.txt" output_file="data/test_vectors.txt" #inference hyper-parameters start_alpha=0.01 infer_epoch=1000 #load model m = g.Doc2Vec.load(model) test_docs = [ x.strip().split() for x in codecs.open(test_docs, "r", "utf-8").readlines() ] #infer test vectors output = open(output_file, "w") for d in test_docs: output.write( " ".join([str(x) for x in m.infer_vector(d, alpha=start_alpha, steps=infer_epoch)]) + "\n" ) output.flush() output.close() """ output file 0.03772797 0.07995503 -0.1598981 0.04817521 0.033129826 -0.06923918 0.12705861 -0.06330753 ......... """
So we got output file with vectors (one per each paragraph). That means we successfully converted our text to vectors. Now we can use it for different machine learning algorithms such as text classification, text clustering and many other. Next section will show example for Birch clustering algorithm with word embeddings.
Using Pretrained doc2vec Model for Text Clustering (Birch Algorithm)
In this example we use Birch clustering algorithm for clustering text data file from 
Birch is unsupervised algorithm that is used for hierarchical clustering. An advantage of this algorithm is its ability to incrementally and dynamically cluster incoming data 
We use the following steps here:
- Load doc2vec model
- Load text docs that will be clustered
- Convert docs to vectors (infer_vector)
- Do clustering
from sklearn import metrics import gensim.models as g import codecs model="doc2vec/doc2vec.bin" test_docs="data/test_docs.txt" #inference hyper-parameters start_alpha=0.01 infer_epoch=1000 #load model m = g.Doc2Vec.load(model) test_docs = [ x.strip().split() for x in codecs.open(test_docs, "r", "utf-8").readlines() ] print (test_docs) """ [['the', 'cardigan', 'welsh', 'corgi'........ """ X= for d in test_docs: X.append( m.infer_vector(d, alpha=start_alpha, steps=infer_epoch) ) k=3 from sklearn.cluster import Birch brc = Birch(branching_factor=50, n_clusters=k, threshold=0.1, compute_labels=True) brc.fit(X) clusters = brc.predict(X) labels = brc.labels_ print ("Clusters: ") print (clusters) silhouette_score = metrics.silhouette_score(X, labels, metric='euclidean') print ("Silhouette_score: ") print (silhouette_score) """ Clusters: [1 0 0 1 1 2 1 0 1 1] Silhouette_score: 0.17644188 """
If you want to get some test with text clustering and word embeddings here is the online demo Currently it is using word2vec and glove models and k means clustering algorithm. Select ‘Text Clustering’ option and scroll down to input data.
We looked what is doc2vec is, we investigated 2 ways to load this model: we can create embedding model file from our text or use pretrained embedding file. We applied doc2vec to do Birch algorithm for text clustering. In case we need to work with paragraph / sentences / docs, doc2vec can simplify word embedding for converting text to vectors.
1. Distributed Representations of Sentences and Documents
2. What is doc2vec?
3. Anything to Vec
4. Anything2Vec, or How Word2Vec Conquered NLP
5. models.doc2vec – Doc2vec paragraph embeddings