What is fastText?

Key takeaways:

• FastText, developed by Facebook, is a tool for word embeddings and text classification in NLP.

• Efficient, scalable, and supports 157 languages with pre-trained models.

• Uses character n-grams for better handling of rare and unseen words.

• Tasks: sentiment analysis, topic classification, and summarization.

• Key functions: get_word_vector (word vectors) and get_nearest_neighbors (similar words).

FastText is an open-source library developed by Facebook’s AI Research lab. It’s a powerful tool for two main tasks in natural language processing (NLP):

1. Learning word embeddings: This means representing words as numerical vectors, where similar words have similar vector representations.

2. Text classification: This involves assigning labels or categories to text data, such as classifying sentiment (positive, negative) or topic (sports, politics).

FastText handles rare words and unseen words by leveraging the information from their constituent characters. For example, imagine the word “unseeable,” which may be rare in a given text dataset. FastText can break it down into 3-character n-grams like “uns,” “nse,” “see,” “eab,” “abl,” and “ble.” Even if “unseeable” itself hasn’t been encountered, these n-grams are likely common within other words (like “see” in “seeing” or “able” in “capable”).

By using these familiar n-grams, fastText can infer a meaningful vector for “unseeable” based on the embeddings of its sub-parts, effectively handling new words without needing them to be explicitly present in the training data.

Features of fastText library

Here are the key features of the fastText library:

• Word embeddings:

  • FastText learns word representations (embeddings) by breaking down words into character n-grams. This allows it to capture more information about the morphology of words, making it effective at handling rare or unseen words.

• Text classification:

  • FastText supports supervised learning for text classification. We can train models for tasks like sentiment analysis, topic classification, and spam detection.

• Pretrained models:

  • FastText offers pretrained word vectors for over 150 languages, which can be used for various NLP tasks without having to train models from scratch.

• Out-of-Vocabulary (OOV) handling:

  • Due to its subword approach, fastText can handle words that are not present in the training data by leveraging their character n-grams.

These features make fastText a powerful tool for natural language processing tasks like learning word embeddings, text classification, and handling a variety of languages and datasets.

Role of character n-grams in fastText

Unlike traditional word embedding methods that focus on whole words, FastText leverages character n-grams. These are subsequences of characters of a fixed length (n) that capture morphological informationMorphological information refers to the structure of words, including prefixes, suffixes, and roots, which helps in understanding word variations and their meanings..

For example, with n=3, the word “Educative” would be represented by the character n-grams: “Edu,” “duc,” “us,” “cat,” “ati,” “tiv,” “live,” etc. Each n-gram and the word itself are then mapped to a vector representation. The final embedding for the word is obtained by averaging the vectors of all its n-grams.

How to install fastText

To install fastText in Python, follow these steps:

1. Install Python (if not already installed): Ensure Python 3.6 or higher is installed.

2. Install fastText via pip: Open a terminal or command prompt and run the following command:

3. Verify installation: After installation, check that fastText is installed correctly by running:

Alternative method: Install from source

If you face issues with the pip installation, you can install fastText from the source:

1. Clone the repository:

1. Build and install:

After installation, you can use fastText for tasks like word embeddings and text classification.

Advantages

• Efficiency: It is lightweight, performs well on typical computing devices, and can even be trained on mobile devices.

• Scalability: It can handle and process large datasets containing millions of words.

• Multilingual: It supports a wide range of languages, with pretrained models available for over 157 languages.

Disadvantages

Here are the disadvantages of fastText in concise form:

• Memory consumption: Training on large datasets can be memory-intensive.

• Limited deep learning features: It is not suitable for advanced custom model building.

• Performance on complex tasks: May underperform on highly nuanced NLP tasks such as complex sentence generation and long-range dependencies.

• Limited fine-tuning: It lacks flexibility for custom model adjustments.

• Training speed on large datasets: It can be slow on very large datasets without specialized hardware.

Conclusion

FastText is a versatile and efficient tool for NLP tasks. Its ability to learn word embeddings based on character n-grams makes it particularly useful for working with large datasets and multiple languages. By leveraging fastText, we can gain valuable insights from text data for various applications.