- Gensim Tutorial
- Gensim - Home
- Gensim - Introduction
- Gensim - Getting Started
- Gensim - Documents & Corpus
- Gensim - Vector & Model
- Gensim - Creating a Dictionary
- Creating a bag of words (BoW) Corpus
- Gensim - Transformations
- Gensim - Creating TF-IDF Matrix
- Gensim - Topic Modeling
- Gensim - Creating LDA Topic Model
- Gensim - Using LDA Topic Model
- Gensim - Creating LDA Mallet Model
- Gensim - Documents & LDA Model
- Gensim - Creating LSI & HDP Topic Model
- Gensim - Developing Word Embedding
- Gensim - Doc2Vec Model
- Gensim Useful Resources
- Gensim - Quick Guide
- Gensim - Useful Resources
- Gensim - Discussion
Gensim - Doc2Vec Model
Doc2Vec model, as opposite to Word2Vec model, is used to create a vectorised representation of a group of words taken collectively as a single unit. It doesn’t only give the simple average of the words in the sentence.
Creating Document Vectors Using Doc2Vec
Here to create document vectors using Doc2Vec, we will be using text8 dataset which can be downloaded from gensim.downloader.
Downloading the Dataset
We can download the text8 dataset by using the following commands −
import gensim
import gensim.downloader as api
dataset = api.load("text8")
data = [d for d in dataset]
It will take some time to download the text8 dataset.
Train the Doc2Vec
In order to train the model, we need the tagged document which can be created by using models.doc2vec.TaggedDcument() as follows −
def tagged_document(list_of_list_of_words):
for i, list_of_words in enumerate(list_of_list_of_words):
yield gensim.models.doc2vec.TaggedDocument(list_of_words, [i])
data_for_training = list(tagged_document(data))
We can print the trained dataset as follows −
print(data_for_training [:1])
Output
[TaggedDocument(words=['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against', 'early', 'working', 'class', 'radicals', 'including', 'the', 'diggers', 'of', 'the', 'english', 'revolution', 'and', 'the', 'sans', 'culottes', 'of', 'the', 'french', 'revolution', 'whilst', 'the', 'term', 'is', 'still', 'used', 'in', 'a', 'pejorative', 'way', 'to', 'describe', 'any', 'act', 'that', 'used', 'violent', 'means', 'to', 'destroy', 'the', 'organization', 'of', 'society', 'it', 'has', 'also', 'been' , 'taken', 'up', 'as', 'a', 'positive', 'label', 'by', 'self', 'defined', 'anarchists', 'the', 'word', 'anarchism', 'is', 'derived', 'from', 'the', 'greek', 'without', 'archons', 'ruler', 'chief', 'king', 'anarchism', 'as', 'a', 'political', 'philosophy', 'is', 'the', 'belief', 'that', 'rulers', 'are', 'unnecessary', 'and', 'should', 'be', 'abolished', 'although', 'there', 'are', 'differing', 'interpretations', 'of', 'what', 'this', 'means', 'anarchism', 'also', 'refers', 'to', 'related', 'social', 'movements', 'that', 'advocate', 'the', 'elimination', 'of', 'authoritarian', 'institutions', 'particularly', 'the', 'state', 'the', 'word', 'anarchy', 'as', 'most', 'anarchists', 'use', 'it', 'does', 'not', 'imply', 'chaos', 'nihilism', 'or', 'anomie', 'but', 'rather', 'a', 'harmonious', 'anti', 'authoritarian', 'society', 'in', 'place', 'of', 'what', 'are', 'regarded', 'as', 'authoritarian', 'political', 'structures', 'and', 'coercive', 'economic', 'institutions', 'anarchists', 'advocate', 'social', 'relations', 'based', 'upon', 'voluntary', 'association', 'of', 'autonomous', 'individuals', 'mutual', 'aid', 'and', 'self', 'governance', 'while', 'anarchism', 'is', 'most', 'easily', 'defined', 'by', 'what', 'it', 'is', 'against', 'anarchists', 'also', 'offer', 'positive', 'visions', 'of', 'what', 'they', 'believe', 'to', 'be', 'a', 'truly', 'free', 'society', 'however', 'ideas', 'about', 'how', 'an', 'anarchist', 'society', 'might', 'work', 'vary', 'considerably', 'especially', 'with', 'respect', 'to', 'economics', 'there', 'is', 'also', 'disagreement', 'about', 'how', 'a', 'free', 'society', 'might', 'be', 'brought', 'about', 'origins', 'and', 'predecessors', 'kropotkin', 'and', 'others', 'argue', 'that', 'before', 'recorded', 'history', 'human', 'society', 'was', 'organized', 'on', 'anarchist', 'principles', 'most', 'anthropologists', 'follow', 'kropotkin', 'and', 'engels', 'in', 'believing', 'that', 'hunter', 'gatherer', 'bands', 'were', 'egalitarian', 'and', 'lacked', 'division', 'of', 'labour', 'accumulated', 'wealth', 'or', 'decreed', 'law', 'and', 'had', 'equal', 'access', 'to', 'resources', 'william', 'godwin', 'anarchists', 'including', 'the', 'the', 'anarchy', 'organisation', 'and', 'rothbard', 'find', 'anarchist', 'attitudes', 'in', 'taoism', 'from', 'ancient', 'china', 'kropotkin', 'found', 'similar', 'ideas', 'in', 'stoic', 'zeno', 'of', 'citium', 'according', 'to', 'kropotkin', 'zeno', 'repudiated', 'the', 'omnipotence', 'of', 'the', 'state', 'its', 'intervention', 'and', 'regimentation', 'and', 'proclaimed', 'the', 'sovereignty', 'of', 'the', 'moral', 'law', 'of', 'the', 'individual', 'the', 'anabaptists', 'of', 'one', 'six', 'th', 'century', 'europe', 'are', 'sometimes', 'considered', 'to', 'be', 'religious', 'forerunners', 'of', 'modern', 'anarchism', 'bertrand', 'russell', 'in', 'his', 'history', 'of', 'western', 'philosophy', 'writes', 'that', 'the', 'anabaptists', 'repudiated', 'all', 'law', 'since', 'they', 'held', 'that', 'the', 'good', 'man', 'will', 'be', 'guided', 'at', 'every', 'moment', 'by', 'the', 'holy', 'spirit', 'from', 'this', 'premise', 'they', 'arrive', 'at', 'communism', 'the', 'diggers', 'or', 'true', 'levellers', 'were', 'an', 'early', 'communistic', 'movement', (truncated…)
Initialise the Model
Once trained we now need to initialise the model. it can be done as follows −
model = gensim.models.doc2vec.Doc2Vec(vector_size=40, min_count=2, epochs=30)
Now, build the vocabulary as follows −
model.build_vocab(data_for_training)
Now, let’s train the Doc2Vec model as follows −
model.train(data_for_training, total_examples=model.corpus_count, epochs=model.epochs)
Analysing the Output
Finally, we can analyse the output by using model.infer_vector() as follows −
print(model.infer_vector(['violent', 'means', 'to', 'destroy', 'the','organization']))
Complete Implementation Example
import gensim
import gensim.downloader as api
dataset = api.load("text8")
data = [d for d in dataset]
def tagged_document(list_of_list_of_words):
for i, list_of_words in enumerate(list_of_list_of_words):
yield gensim.models.doc2vec.TaggedDocument(list_of_words, [i])
data_for_training = list(tagged_document(data))
print(data_for_training[:1])
model = gensim.models.doc2vec.Doc2Vec(vector_size=40, min_count=2, epochs=30)
model.build_vocab(data_training)
model.train(data_training, total_examples=model.corpus_count, epochs=model.epochs)
print(model.infer_vector(['violent', 'means', 'to', 'destroy', 'the','organization']))
Output
[ -0.2556166 0.4829361 0.17081228 0.10879577 0.12525807 0.10077011 -0.21383236 0.19294572 0.11864349 -0.03227958 -0.02207291 -0.7108424 0.07165232 0.24221905 -0.2924459 -0.03543589 0.21840079 -0.1274817 0.05455418 -0.28968817 -0.29146606 0.32885507 0.14689675 -0.06913587 -0.35173815 0.09340707 -0.3803535 -0.04030455 -0.10004586 0.22192696 0.2384828 -0.29779273 0.19236489 -0.25727913 0.09140676 0.01265439 0.08077634 -0.06902497 -0.07175519 -0.22583418 -0.21653089 0.00347822 -0.34096122 -0.06176808 0.22885063 -0.37295452 -0.08222228 -0.03148199 -0.06487323 0.11387568 ]
Advertisements