DeText Files

A Deep Neural Text Understanding Framework

This is an exact mirror of the DeText project, hosted at https://github.com/linkedin/detext. SourceForge is not affiliated with DeText.

The interactive file manager requires Javascript. Please enable it or use sftp or scp.
You may still browse the files here.

Name	Modified	Size	InfoDownloads / Week
v2.0.8	2020-08-22		0
v2.0.6	2020-08-05		0
v1.2.0	2020-05-29		0
v1.1.0	2020-05-13		0
v1.0.12	2020-05-01		0
Totals: 5 Items			0

Other Useful Business Software

MongoDB Atlas runs apps anywhere

Deploy in 115+ regions with the modern database for every enterprise.

MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.

Start Free

$300 Free Credits to Build on Google Cloud

New customers can spin up VMs, build with AI, and query data at no cost.

Put your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.

Start Free

$300 Free Credits to Build on Google Cloud

New customers can spin up VMs, build with AI, and query data at no cost.

Put your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.

Start Free

Recommended Projects

Haystack
Haystack is an open source NLP framework to interact with your data
DeepSparse
Sparsity-aware deep learning inference runtime for CPUs
MeCab
MeCab is a fast and customizable Japanese morphological analyzer. MeCab is designed for generic purpose and applied to variety of NLP tasks, such as Kana-Kanji conversion. MeCab provides parameter estimation functionalities based on CRFs and HMM
imgclsmob Deep learning networks
Sandbox for training deep learning networks
CRF++
CRF++ is a simple, customizable, and open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data. CRF++ is designed for generic purpose and will be applied to a variety of NLP tasks.