What Is FastText? Compared To Word2Vec & GloVe [How To Tutorial In Python]

by | Dec 5, 2023 | Natural Language Processing

What is fastText?

fastText, a product of Facebook’s AI Research (FAIR) team, represents a remarkable leap forward in natural language processing (NLP). This library, introduced in 2016, builds upon the foundations laid by Word2Vec while introducing pivotal innovations.

Unlike conventional word embedding models, fastText operates at the subword level, utilising character n-grams to encapsulate morphological nuances. This approach offers a distinct advantage by efficiently handling out-of-vocabulary comments and accommodating morphologically complex languages.

It incorporates techniques like Hierarchical Softmax and Negative Sampling, which optimise the training process, ensuring computational efficiency. Since its inception, fastText has continued to evolve, adapting to the latest trends and insights in NLP research, solidifying its position as an indispensable tool.

One of fastText’s hallmark features lies in its exceptional speed and efficiency. Its design facilitates rapid training on extensive corpora, rendering it ideal for real-time applications and large-scale datasets. Moreover, the ability to generate embeddings for subword units significantly enhances its utility, particularly in scenarios involving rare or unseen words. This attribute proves invaluable across diverse linguistic landscapes and specialised domains.

Beyond word embeddings, fastText shines in text classification tasks, encompassing sentiment analysis, topic modelling, and document classification applications. As an open-source library, fastText fosters collaboration, accessibility, and continual improvement within the NLP community. This blend of innovative techniques, efficiency, and versatility positions fastText as a pivotal asset in the NLP toolkit.

A Simple Example of fastText

Imagine fastText as a tool that learns to understand words by breaking them down into smaller parts.

For instance, let’s take the word “apple.” Instead of treating it as a single entity, fastText dissects it into smaller components called character n-grams, such as ‘ap,’ ‘pp,’ ‘pl,’ ‘le,’ etc. These character-level fragments or subword units help fastText understand the word’s structure and meaning.

Learning from a large dataset of words and their contexts, fastText grasps relationships between these subword units and how they combine to form words. This knowledge enables fastText to represent known words and unseen or rare words by considering their constituent subword components.

This approach allows fastText to create embeddings and numerical representations of words based on their subword information. These embeddings capture similarities and relationships between words, making them an efficient tool for language identification, text classification, and handling morphologically rich languages or specialized vocabularies.

How Do fastText embeddings Work?

What are Word Embeddings in NLP?

Word embeddings form the backbone of many NLP applications by representing words as continuous vectors in a high-dimensional space. These embeddings capture semantic relationships between words, enabling algorithms to process and understand language more effectively. fastText, like its predecessors, excels in generating these embeddings but stands out due to its approach at the subword level.

What are Skip-gram Models and Continuous Bag-of-Words (CBOW)? 

fastText employs two primary models: Skip-gram and Continuous Bag-of-Words (CBOW). The Skip-gram model predicts the context words given a central word, while CBOW predicts the central word given its context.

The difference between a skip-gram model and the continuous bag of words model. fastText uses both these techniques.

However, fastText introduces a pivotal shift by considering words as composed of character n-grams, enabling it to build representations for words based on these subword units. This approach allows the model to understand and generate embeddings for words not seen in the training data, offering a substantial advantage in handling morphologically rich languages and rare words.

Hierarchical Softmax and Negative Sampling in fastText 

To improve computational efficiency during training, fastText integrates two essential techniques: Hierarchical Softmax and Negative Sampling. Hierarchical Softmax organises the output layer in a hierarchical structure, reducing the computation required for calculating output probabilities.

On the other hand, Negative Sampling enhances efficiency by training the model to differentiate between true and noisy words. These techniques streamline the training process, making it significantly faster and more scalable, even with extensive vocabularies.

Understanding fastText’s utilisation of subword information, coupled with its employment of the Skip-gram and CBOW models alongside techniques like Hierarchical Softmax and Negative Sampling, provides insights into its unique approach to generating word embeddings efficiently and effectively.

What is the Difference Between fastText and Word2Vec?

fastText and Word2Vec are two popular algorithms for generating word embeddings, but they differ significantly in their approaches and capabilities. Here are the critical differences between fastText and Word2Vec:

1. Handling of Out-of-Vocabulary (OOV) Words

  • Word2Vec: Word2Vec operates at the word level, generating embeddings for individual words. It struggles with out-of-vocabulary words as it cannot represent words it hasn’t seen during training.
  • fastText: In contrast, fastText introduces subword embeddings by considering words to be composed of character n-grams. This enables it to handle out-of-vocabulary words effectively by breaking terms into subword units and generating embeddings for these units, even for unseen words. This capability makes fastText more robust in dealing with rare or morphologically complex expressions.

2. Representation of Words

  • Word2Vec: Word2Vec generates word embeddings based solely on the words without considering internal structure or morphological information.
  • fastText: fastText captures subword information, allowing it to understand word meanings based on their constituent character n-grams. This enables fastText to represent words by considering their morphological makeup, providing a richer representation, especially for morphologically rich languages or domains with specialised jargon.

3. Training Efficiency

  • Word2Vec: The training process in Word2Vec is relatively faster than older methods but might be slower than fastText due to its word-level approach.
  • fastText: fastText is known for its exceptional speed and scalability, especially when dealing with large datasets, as it operates efficiently at the subword level.

4. Use Cases

  • Word2Vec: Word2Vec’s word-level embeddings are well-suited for tasks like finding similar words, understanding relationships between words, and capturing semantic similarities.
  • fastText: fastText’s subword embeddings make it more adaptable in scenarios involving out-of-vocabulary words, sentiment analysis, language identification, and tasks requiring a deeper understanding of morphology.

Summary

AspectWord2VecfastText
Handling of OOV WordsStruggles with OOV wordsHandles OOV words efficiently using subword embeddings
Representation of WordsGenerates embeddings solely based on wordsConsiders subword information for richer representations
Training EfficiencyTraining speed is moderateExceptional speed and scalability, especially with large datasets
Use CasesWell-suited for finding word relationships and semantic similaritiesPreferred for handling OOV words, sentiment analysis, and understanding morphology
Word-Level vs. Subword-LevelOperates at the word levelConsiders subword units for understanding word meanings and morphology

In summary, while both algorithms generate word embeddings, fastText’s incorporation of subword information enables it to handle OOV words more effectively, making it a preferred choice in scenarios with limited data or languages featuring complex word structures.

Applications of fastText

1. Text Classification and Categorisation 

fastText excels in text classification tasks, efficiently categorising texts into predefined classes or categories. Its ability to capture subword information allows for more nuanced Understanding, enabling accurate classification even with limited training data. This capability finds applications in spam filtering, topic categorisation, and content tagging across various domains.

2. Language Identification and Translation 

The subword-level embeddings in fastText empower it to discern and work with languages even in cases where only fragments or limited text samples are available. This proves beneficial in language identification tasks, aiding multilingual applications and facilitating language-specific processing. Additionally, fastText’s embeddings have been utilised to enhance machine translation systems, improving the accuracy and performance of translation models.

3. Sentiment Analysis and Opinion Mining 

In sentiment analysis, fastText’s robustness in capturing subtle linguistic nuances allows for more accurate sentiment classification. Its ability to understand and represent words based on their subword units enables a more profound comprehension of sentiment-laden expressions, contributing to more nuanced opinion mining in social media analysis, product reviews, and customer feedback.

4. Entity Recognition and Tagging 

Entity recognition involves identifying and classifying entities within a text, such as names of persons, organisations, locations, and more. fastText’s subword embeddings contribute to better handling of unseen or rare entities, improving the accuracy of entity recognition systems. This capability finds applications in information extraction, search engines, and content analysis.

fastText’s versatility across these applications stems from its unique ability to handle subword information effectively, enabling a deeper understanding of language nuances. Its prowess in tasks like text classification, language identification, sentiment analysis, and entity recognition underlines its significance in diverse NLP applications, contributing to more accurate and efficient processing of textual data.

How To Use fastText In Python: Practical Tutorial With Examples

1. Installation and Setup Guide for fastText To start with fastText, the installation process is relatively straightforward. It’s an open-source library and can be installed on various platforms. Here’s a step-by-step guide:

Installation:

  • For Python, using pip: pip install fasttext
  • For building from source, clone the GitHub repository and follow the provided instructions for compilation.

Training Models:

Pre-trained models are available for download, but training custom models using your dataset is recommended for specific tasks or domain-specific applications. Use fasttext.train_supervised for text classification and fasttext.train_unsupervised for unsupervised tasks.

2. Example Use Cases and Code Snippets

Text Classification: Implementing a text classification task using a pre-existing dataset or your data involves:

import fasttext

# Training data file format: __label__<class_name> <text>
train_data = [
    "__label__positive This movie is fantastic!",
    "__label__negative I didn't like the ending of this book.",
    "__label__neutral The weather today is quite pleasant."
]

# Saving training data to a file
with open('train.txt', 'w') as f:
    for line in train_data:
        f.write(line + '\n')

# Training the model
model = fasttext.train_supervised(input="train.txt", epoch=25, lr=1.0)

# Testing the model
text_to_predict = "This restaurant serves delicious food!"
predicted_label = model.predict(text_to_predict)
print(predicted_label)

This example demonstrates how to train a simple text classification model using fastText. It starts by preparing training data in the format __label__<class_name> <text> and saves it to a file (‘train.txt’ in this case). Then, the model is trained using fasttext.train_supervised() with specified parameters like the input file, number of epochs, and learning rate.

After training, the model can be used to predict the label of new text samples using model.predict(). The output will display the provided text sample’s predicted label(s).

Word Embeddings: Generating word embeddings:

import fasttext 

model = fasttext.train_unsupervised('corpus.txt', model='skipgram') 
print(model.get_word_vector('example')) 

3. Practical Tips for Efficient Utilisation

By following these steps and best practices, you can swiftly integrate fastText into your NLP projects, from installing the library to implementing models for various tasks, thereby harnessing its efficiency and capabilities for text analysis and classification.

Comparison with Other NLP Tools

1. Compared to Other Word Embedding Models: Word2Vec & GloVe

  • Word2Vec: While Word2Vec focuses on word-level embeddings, fastText’s innovation considers subword information. This allows fastText to handle out-of-vocabulary words more effectively, providing an advantage in capturing morphological nuances.
  • GloVe: GloVe generates word embeddings based on global word-word co-occurrence statistics. In contrast, fastText’s subword-level embeddings enable it to handle unseen or rare words, making it more adaptable in scenarios with limited data or morphologically rich languages.

2. Strengths and Weaknesses of fastText in Comparison

Strengths:

  • Efficiency: fastText’s speed and scalability make it well-suited for processing large volumes of text data.
  • Subword Information: Incorporating subword embeddings enables better handling unseen words and morphologically complex languages.
  • Text Classification: Its efficacy in text classification tasks is notable, especially in scenarios with limited labelled data.

Weaknesses:

  • Contextual Understanding: Compared to contextual embeddings like BERT, fastText may not capture as much contextual information due to its focus on subword embeddings.
  • Semantic Relationships: While vital in capturing morphological information, fastText might not capture complex semantic relationships between words as effectively as other models.

Understanding these comparative aspects helps select the most suitable tool for specific NLP tasks. While fastText’s efficiency and subword-level handling provide distinct advantages, its limitations in capturing intricate contextual and semantic nuances might be a consideration in particular applications where such Understanding is crucial.

Challenges and Future of fastText

1. Limitations and Areas for Improvement

  • Contextual Understanding: fastText’s reliance on subword embeddings might limit its ability to comprehend nuanced contextual relationships between words, unlike models based on contextual embeddings like BERT or GPT.
  • Semantic Representation: While proficient in capturing morphological information, fastText might struggle to represent intricate semantic relationships between words, impacting tasks requiring deeper semantic Understanding.

2. Recent Advancements and Updates

  • Continual Refinement: The fastText library continues to undergo refinements, integrating the latest advancements in NLP research.
  • Enhanced Models: Efforts are ongoing to strengthen fastText’s capabilities, potentially addressing some of its limitations, such as improving contextual Understanding and semantic representations.

3. Potential Future Applications and Developments

  • Advancements in Representation Learning: There’s a growing focus on advancing representation learning techniques within fastText, aiming to enhance its ability to capture richer semantic information.
  • Domain-Specific Enhancements: Tailoring fastText for specific domains or tasks could unlock new possibilities, leveraging its efficiency and subword-level Understanding in specialised areas like healthcare, finance, or legal.

3. Addressing Computational Challenges

  • Scalability: As data volumes grow, ensuring fastText’s scalability remains crucial to maintain its efficiency in handling large datasets.
  • Model Complexity: Balancing model complexity and efficiency is crucial; future iterations might aim to strike a better balance while accommodating more sophisticated linguistic nuances.

4. Community Engagement and Collaboration

  • Open-Source Contributions: The open-source nature of fastText encourages community involvement, fostering collaboration to address challenges and drive innovation.
  • Research Collaboration: Continued collaboration between researchers and practitioners could pave the way for breakthroughs in leveraging fastText for more diverse and challenging NLP tasks.

Understanding the limitations and ongoing efforts to refine fastText provides insights into its potential advancements and the challenges that need addressing. Despite its current strengths, continual evolution and adaptation remain pivotal to expanding its capabilities and solidifying its position as a robust tool in the evolving landscape of natural language processing.

Conclusion

fastText is a pivotal tool in natural language processing, revolutionising how we understand and process textual data. Its unique approach to subword embeddings and its efficiency and scalability have propelled it to the forefront of NLP applications.

Throughout this exploration, we’ve uncovered the foundational aspects of fastText, delving into its subword-level Understanding, efficient training methods, and diverse applications across text classification, language identification, sentiment analysis, and entity recognition. Its speed and adaptability make it invaluable in handling vast corpora and addressing challenges in various linguistic landscapes.

While fastText boasts remarkable strengths in handling subword information and scaling efficiently, it lacks contextual understanding. Intricate semantic relationships pose challenges, leaving room for future advancements. However, continual refinement, ongoing research efforts, and community collaboration promise a trajectory towards addressing these challenges and unlocking new frontiers for fastText.

About the Author

Neri Van Otten

Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

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