What are some alternatives to FastText?

What is FastText and what are its top alternatives?

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 25.5K GitHub stars and 4.7K GitHub forks. Here’s a link to FastText's open source repository on GitHub

Top 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. ...
Amazon Comprehend
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to discover insights from text. Amazon Comprehend provides Keyphrase Extraction, Sentiment Analysis, Entity Recognition, Topic Modeling, and Language Detection APIs so you can easily integrate natural language processing into your applications. ...
Google Cloud Natural Language API
You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage on Google Cloud Storage. ...
Spark NLP
It is a Natural Language Processing library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines that scale easily in a distributed environment. It comes with 160+ pretrained pipelines and models in more than 20+ languages. ...

FastText alternatives & related posts

TensorFlow

3.7K

3.5K

106

Open Source Software Library for Machine Intelligence

Stacks3.7K

Followers3.5K

+ 1

Votes106

PROS OF TENSORFLOW

32
High Performance
19
Connect Research and Production
16
Deep Flexibility
12
Auto-Differentiation
11
True Portability
6
Easy to use
5
High level abstraction
5
Powerful

CONS OF TENSORFLOW

9
Hard
6
Hard to debug
2
Documentation not very helpful

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Compare TensorFlow vs FastText

related TensorFlow posts

Conor Myhrvold

Tech Brand Mgr, Office of CTO at Uber · Dec 4, 2018 | 8 upvotes · 2.8M views

Shared insights

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

Meet Horovod: Uber's Open Source Distributed Deep Learning Framework

StackShare Editors

Sep 9, 2018 | 5 upvotes · 1M views

Shared insights

In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

Meet Michelangelo: Uber's Machine Learning Platform