+
+

Related Products

  • LM-Kit.NET
    29 Ratings
    Visit Website
  • NINJIO
    416 Ratings
    Visit Website
  • Gemini Enterprise Agent Platform
    1,161 Ratings
    Visit Website
  • Expedience Software
    34 Ratings
    Visit Website
  • Google Cloud Speech-to-Text
    443 Ratings
    Visit Website
  • Bluehost
    31,868 Ratings
    Visit Website
  • Docmosis
    51 Ratings
    Visit Website
  • Coursebox AI
    101 Ratings
    Visit Website
  • ClickLearn
    67 Ratings
    Visit Website
  • MobiPDF
    8,085 Ratings
    Visit Website

About

fastText is an open source, free, and lightweight library developed by Facebook's AI Research (FAIR) lab for efficient learning of word representations and text classification. It supports both unsupervised learning of word vectors and supervised learning for text classification tasks. A key feature of fastText is its ability to capture subword information by representing words as bags of character n-grams, which enhances the handling of morphologically rich languages and out-of-vocabulary words. The library is optimized for performance and capable of training on large datasets quickly, and the resulting models can be reduced in size for deployment on mobile devices. Pre-trained word vectors are available for 157 languages, trained on Common Crawl and Wikipedia data, and can be downloaded for immediate use. fastText also offers aligned word vectors for 44 languages, facilitating cross-lingual natural language processing tasks.

About

Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.

Platforms Supported

Windows Not Supported
Mac Supported
Linux Supported
Cloud Not Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Not Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Language processing practitioners and researchers requiring a tool for learning word embeddings and building text classifiers

Audience

Engineers and data scientists requiring a solution to manage and improve their machine learning research

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version Supported
Free Trial Not Supported

Pricing

Free
Free Version Supported
Free Trial Not Supported

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

fastText
fasttext.cc/

Company Information

scikit-learn
United States
scikit-learn.org/stable/

Alternatives

Gensim

Gensim

Radim Řehůřek

Alternatives

Gensim

Gensim

Radim Řehůřek
GloVe

GloVe

Stanford NLP
ML.NET

ML.NET

Microsoft
word2vec

word2vec

Google
MLlib

MLlib

Apache Software Foundation
LexVec

LexVec

Alexandre Salle
Keepsake

Keepsake

Replicate

Categories

Embedding Models Supported

Categories

Machine Learning Supported

Integrations

Python Supported
DagsHub Not Supported
Databricks Not Supported
Flower Not Supported
GLM-5.1 Not Supported
GLM-5.2 Not Supported
GLM-5.3 Not Supported
Gensim Supported
Guild AI Not Supported
JavaScript Supported
Keepsake Not Supported
MLJAR Studio Not Supported
Matplotlib Not Supported
ModelOp Not Supported
NumPy Not Supported
Thunder Compute Not Supported
Train in Data Not Supported
WebAssembly Supported

Integrations

Python Supported
DagsHub Supported
Databricks Supported
Flower Supported
GLM-5.1 Supported
GLM-5.2 Supported
GLM-5.3 Supported
Gensim Not Supported
Guild AI Supported
JavaScript Not Supported
Keepsake Supported
MLJAR Studio Supported
Matplotlib Supported
ModelOp Supported
NumPy Supported
Thunder Compute Supported
Train in Data Supported
WebAssembly Not Supported
Claim fastText and update features and information
Claim fastText and update features and information
Claim scikit-learn and update features and information
Claim scikit-learn and update features and information