FastText vs Transformers: What are the differences?

Introduction

This Markdown code provides a comparison between FastText and Transformers, highlighting key differences between the two. FastText and Transformers are both popular methods used in natural language processing, but they have distinct characteristics that set them apart.

1. Model architecture: FastText is based on the bag of words model, where words are represented as n-grams, and the classification is performed by a linear classifier. On the other hand, Transformers utilize self-attention mechanisms, enabling the model to capture the dependencies between words and generate contextualized word embeddings.

2. Training data: FastText requires a lot of labeled training data to perform well, as it relies on frequency-based word representations. In contrast, Transformers have the ability to learn from smaller amounts of data due to their self-attention mechanism, allowing them to capture long-range dependencies effectively.

3. Word representations: FastText represents words as continuous dense vectors that are trained as part of the model. It can generate embeddings for out-of-vocabulary words based on subword information. In contrast, Transformers use pre-trained word embeddings like Word2Vec or GloVe, which encode semantic information. These embeddings are then fine-tuned during the training process.

4. Language modeling: FastText is primarily designed for text classification tasks and focuses on optimizing efficiency in training and inference. Transformers, on the other hand, excel in a wide range of natural language processing tasks, including language modeling and sequence-to-sequence tasks.

5. Model size: FastText models tend to have smaller file sizes compared to Transformers due to their simpler architecture and reliance on frequency-based word representations. Transformers, with their self-attention mechanism and pre-trained word embeddings, result in larger models, especially for complex tasks like machine translation.

6. Execution speed: FastText models are generally faster to train and evaluate compared to Transformers since they have a simpler architecture and utilize word frequency information. Transformers, being more complex models, may require more computational resources and time for training and inference, especially for large-scale tasks.

In summary, FastText and Transformers differ in their model architecture, training data requirements, word representations, applicable tasks, model size, and execution speed. These differences need to be considered when choosing the appropriate method for a given natural language processing task.