multisense-prob-fasttext
FastText model
An implementation of a probabilistic FastText model for multi-sense word embeddings
ACL 2018 paper: Probabilistic FastText for Multi-Sense Word Embeddings (Athiwaratkun et al., 2018)
148 stars
10 watching
30 forks
Language: C++
last commit: over 8 years agoRelated projects:
| Repository | Description | Stars |
|---|---|---|
 babylonhealth/fasttext_multilingual | A repository providing aligned multilingual word vectors for 78 languages using the SVD method. | 1,197 |
 apcode/tensorflow_fasttext | An open-source implementation of a text classification system using word embeddings and TensorFlow. | 302 |
 ispras/tm | Implementation of probabilistic semantic analysis for multilingual text processing | 7 |
 tylertreat/boomfilters | Probabilistic data structures for processing continuous, unbounded streams | 1,596 |
 hexops/fastfilter | Probabilistic data structures for quickly checking membership in a set. | 260 |
 junwei-pan/fasttext_torch | An implementation of a text classification algorithm using Torch and the fasttext technique | 72 |
 com-lihaoyi/fastparse | A Scala-based library for writing fast parsers using parser combinators. | 1,098 |
 joashc/csharp-probability-monad | A monadic probabilistic programming framework for Bayesian modelling and inference in C#. | 96 |
 bigredt/vico | Multi-sense word embeddings learned from visual cooccurrences | 25 |
 benlau/quickfuture | Provides QML compatibility and utilities for working with asynchronous tasks in C++ | 53 |
 ankane/fasttext-ruby | Efficient text classification and representation learning library for Ruby | 204 |
 sergioburdisso/pyss3 | A Python package implementing an interpretable machine learning model for text classification with visualization tools | 336 |
 mustafaturan/omnicat-bayes | An implementation of the Naive Bayes algorithm as a text classification strategy in Ruby. | 32 |
 cosmicmind/algorithm | A comprehensive Swift library of tools for creating intelligent applications through probabilistic programming and advanced data structures | 828 |
 ailab-cvc/seed-bench | A benchmark for evaluating large language models' ability to process multimodal input | 322 |