How to create word embedding using FastText ?

How to create word embedding using FastText

FastText is one of the popular names in Word Embedding these days. In short, It is created by FaceBook. Still, FastText is open source so you don’t have to pay anything for commercial use. 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 so you can categorize and find sense of the text. 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 (fasttext word embeddings tutorial) –

create word embedding using FastText
create word embedding using FastText

How to Train FastText Embeddings –

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. Let’s understand one by one –

  1. Import required modules.
  2. You need some corpus for training. Here the corpus must be a list of lists tokens. The regular text must contain sentences. you need to create the tokens out of it. Hence every element of the list will be a sentence token. Now any text data must contain multiple sentences. So there will be a corresponding list for each sentence. So the final data structure will be a list of lists.
  3. Create the object for FastText with the required parameters. Here size is a number of feature or embedding dimensions. For more clarification 4 represents that each word 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. The fourth 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 points for every data scientist. Actually, there is a very clear line between these two. Let’s understand them one by one. See FastText is not a model, It’s 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 the 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 data.

Conclusion (FastText embeddings python) –

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 that you should understand when it comes to word embedding. Word Embedding is a very vast and hot research topic. So keep on the reading-related the latest content on this.   Please refer to 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 ?

I hope this tutorial has helped you in understanding FastText in detail. If you have any queries then you can contact us for more help and information.

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Meet Abhishek ( Chief Editor) , a data scientist with major expertise in NLP and Text Analytics. He has worked on various projects involving text data and have been able to achieve great results. He is currently manages, where he and his team share knowledge and help others learn more about data science.
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