FastText vs Spark NLP: What are the differences?

## Introduction
When considering natural language processing technologies, FastText and Spark NLP present two popular options. Below are the key differences between the two to help you choose the right tool for your specific needs.

1. **Training approach**: FastText utilizes a supervised learning approach, where it works by first learning a vector representation for each word in the training text to predict the probability of a word appearing in a context. On the other hand, Spark NLP implements a range of pre-trained models and transformers that are fine-tuned for specific NLP tasks, offering a quicker implementation and streamlined deployment process.

2. **Word embeddings**: FastText incorporates subword information by breaking words into character n-grams, which can help capture morphological information and improve performance for rare words. In contrast, Spark NLP relies on pre-trained word embeddings like GloVe or Word2Vec, which may not fully capture rich morphology as effectively as FastText when dealing with out-of-vocabulary words.

3. **Scalability**: Spark NLP is built on Apache Spark, which inherently provides scalability and distributed computing capabilities, making it suitable for handling large datasets and processing tasks efficiently. FastText, although powerful for individual training tasks, may struggle with scalability in a distributed computing environment due to limitations in parallel processing.

4. **Model architecture**: FastText employs a shallow neural network architecture with a softmax function for text classification, enabling it to achieve impressive performance with high efficiency. In contrast, Spark NLP offers a modular architecture with various components like tokenizer, lemmatizer, and entity recognizer, providing flexibility for users to customize and construct complex NLP pipelines tailored to their specific requirements.

5. **Industry adoption**: FastText, developed by Facebook AI Research, has gained significant popularity in various applications, especially in academia and research settings, due to its efficient text classification and language identification capabilities. Spark NLP, on the other hand, is widely preferred in industry settings, particularly in enterprises dealing with big data, thanks to its seamless integration with Spark and support for scalable NLP workflows.

6. **Community support and documentation**: FastText benefits from a robust open-source community, offering extensive documentation, tutorials, and resources to facilitate usage and troubleshooting. In comparison, Spark NLP provides comprehensive documentation and dedicated support channels, ensuring users have access to timely assistance and updates for smooth integration and implementation in production environments.

In Summary, FastText and Spark NLP differ in their training approach, word embeddings, scalability, model architecture, industry adoption, and community support and documentation, catering to diverse needs in natural language processing applications.