FastText vs Gensim: What are the differences?

Key Differences between FastText and Gensim

FastText and Gensim are both popular libraries used in natural language processing tasks. However, there are some key differences that set them apart.

1. Representation of Words: One of the key differences between FastText and Gensim is the way they represent words. FastText uses a vector representation that includes subword information. This means that even if a word is not present in the training data, it can still estimate its representation based on its subwords. On the other hand, Gensim uses traditional word embeddings that do not take into account subword information.

2. Pre-trained Models: Another difference is the availability of pre-trained models. FastText provides pre-trained models for a wide range of languages, allowing users to easily leverage these models in their applications. Gensim, on the other hand, does not provide pre-trained models out of the box, although it does provide an interface to train custom models.

3. Training Speed: FastText is known for its fast training speed. It uses a hierarchical softmax algorithm that speeds up the training process, making it ideal for large datasets. Gensim, while still efficient, may take longer to train models compared to FastText.

4. Support for Training on External Corpora: Gensim allows users to train word embeddings on external corpora without needing the entire corpus in memory. This can be useful when dealing with very large text datasets. FastText, on the other hand, requires the entire training corpus to be loaded into memory.

5. Model Size: FastText models tend to have larger file sizes compared to Gensim models. This is because FastText includes additional information such as subword embeddings, which can increase the size of the model files. Gensim models, without subword information, tend to have smaller file sizes.

6. Handling Out of Vocabulary Words: FastText handles out of vocabulary (OOV) words better than Gensim. Thanks to its subword information, it can approximate representations for OOV words based on their subwords. Gensim, on the other hand, will simply ignore OOV words in its word embedding models.

In summary, FastText and Gensim differ in their representation of words, availability of pre-trained models, training speed, support for training on external corpora, model size, and handling of out of vocabulary words.