What is FastText?
It is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices.
FastText is a tool in the NLP / Sentiment Analysis category of a tech stack.
FastText is an open source tool with 25K GitHub stars and 4.7K GitHub forks. Here’s a link to FastText's open source repository on GitHub
Who uses FastText?
Companies
6 companies reportedly use FastText in their tech stacks, including Shelf, Data Science, Data Analytics, Machine Learning, and Vector.ai.
Developers
25 developers on StackShare have stated that they use FastText.
Pros of FastText
1
Decisions about FastText
Here are some stack decisions, common use cases and reviews by companies and developers who chose FastText in their tech stack.
Sonali Ajankar
I want to encode the news article which has many named entities like person names, organization names, etc. means many vocabulary words are out of a dictionary. My dataset is having around 3 million articles and the average length of an article is 650. What are the benefits or drawbacks if I used FastText word embedding?
Biswajit Pathak
Project Manager at Sony · | 6 upvotes · 461.6K views
Can you please advise which one to choose FastText Or Gensim, in terms of:
- Operability with ML Ops tools such as MLflow, Kubeflow, etc.
- Performance
- Customization of Intermediate steps
- FastText and Gensim both have the same underlying libraries
- Use cases each one tries to solve
- Unsupervised Vs Supervised dimensions
- Ease of Use.
Please mention any other points that I may have missed here.
FastText's Features
- Train supervised and unsupervised representations of words and sentences
- Written in C++
FastText Alternatives & Comparisons
What are some alternatives to FastText?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Gensim
It is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
SpaCy
It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.
Transformers
It provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
rasa NLU
rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.
