How to create word embedding using FastText

How to create word embedding using FastText ?

FastText is one of the popular name in Word Embedding these days . In short , It is  created by FaceBook . Still FastText is open source . You may use FastText in many ways like test classification and text representation etc . While under this article , We will only explore the text representation . Basically for any Machine Learning algorithms on Text , You need to convert them into numbers vectors .Lets understand How to create word embedding using FastText ?

Using FastText own Implementation –

Yes ! FastText has its own implementation for word embedding . Here I am sharing the official link  for FastText own implementation for word embedding .

FastText Word embedding
FastText Word embedding

Here you can use FastText pre train model as well as you may train your own model of embedding with fastText algorithms . For implementation prospective I will suggest you to visit the official FastText tutorial on embeddings .

Gensim for FastText Implementation –

create word embedding using FastText
create word embedding using FastText

Gensim provide the another way to apply FastText Algorithms and create word embedding .Here is the simple code example –

from gensim.models import FastText  
from gensim.test.utils import common_texts
model_FastText = FastText(size=4, window=3, min_count=1)
model_FastText .train(sentences=common_texts, total_examples=len(common_texts), epochs=10)

The above example is of 4 line implementation . Lets understand one by one –

  1. Import required modules .
  2. You need some corpus for training . Here the corpus must be list of lists of tokens . Regular text must contains sentence . you need to create the tokens out of it . Hence every element of the list will be a sentence tokens .Now any text data must contains multiple sentence . So there will be a corresponding list for each sentence . So the final data structure will be list of lists .
  3. Create the object for FastText with require parameters .Here size is numbers of feature or embedding dimensions . For more clarification 4 represents that each words will be represented in 4 columns . For more details , Please have look here –
  4.  create word embedding using FastText paramters
    create word embedding using FastText paramters

    4. This line gives the last train command syntax for FastText .

Fast Text Pre Trained Model –

When you read this title , you must have a question . Embedding on your training data or FastText Pre trained Model . Actually this is one of the big question point for every data scientist . Actually There is very clear line between these two . Lets understand them one by one . See FastText is not a model , Its an algorithm or Library which we use to train sentence embedding . However Pre train Fast Text Models are the ready made solution ( models ) on some large corpus . This is only beneficiary for generalize data itself .

See Training on large data involves heavy computation cost . Also any deep learning model like FastText etc needs too much of data .

Conclusion –

Friends how did you find this article – How to create word embedding using FastText ? Please write your views on this topic . Apart from this article , There are some other key terms which you should understand when it comes to word embedding . Word Embedding is very vast and hot research topic . So keep on reading related latest content on this .   Please refer the below article for reference and basic understanding  –

Word Embedding in Python : Different Approaches

Prediction Based Word Embedding Techniques

Which is the Best Word Embedding Technique with Domain Data ?

Join our list

Subscribe to our mailing list and get interesting stuff and updates to your email inbox.

Thank you for signup. A Confirmation Email has been sent to your Email Address.

Something went wrong.

 
Thank you For sharing.We appreciate your support. Don't Forget to LIKE and FOLLOW our SITE to keep UPDATED with Data Science Learner