Search Results for "training"

...It can be customized for specific tasks (e.g., named entity identification, de-identification of medical records). The goal of MAT is not to help you configure your training engine (in the default case, the Carafe CRF system) to achieve the best possible performance on your data. MAT is for "everything else": all the tools you end up wishing you had.

...Beyond the core EMNLP 2018 setup, the codebase exposes additional, optional capabilities such as multi-language training, language model pretraining with shared parameters, and adversarial training.

Linux Guist - is a Multi Lingual Live CD OS for most Asian Languages, with the ability to run of a CD & Old Hardware, with just 128 MB Memory, for DTP, Web Publishing & Data Entry purposes. This will help IT employers to take up Govt. Projects that require Data Collection, Entry & Publishing at a very very low cost, while providing Training & Job Opportunities to numerous students of these languages, in the various towns, of the country. Talk to your respective IT/HRD ministry to identify projects as well as Towns that have more Unemployed youths, to make a Difference in their Lives ! With Just a Click, on the Language script, in the "Select Language" folder on Desktop, you can keep switching between languages, in all Apps & start typing instantly. ...

This program is made to address two most common issues with the known classifying algorithms. First, over-training and second, shortage of data for a training of categories. Instead, each TXT file is a category on its own, rather than an assigned category. In a way, this is similar to clustering but not really a clustering algorithm since there is some training involved. The summarizer from Classifier4J has been adjusted to accept two inputs (lets call them A and B). ...

Java application for training and deploying text processing applications such as part-of-speech taggers, based on a re-implementation of Brill's algorithm in Java.

...It encodes model parameters by L-BFGS. Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. Currently, when training corpus, compared with CRF++, CRF# can make full use of multi-core CPUs and only uses very low memory, and memory grow is very smoothly and slowly while amount of training corpus, tags increase. with multi-threads process, CRF# is more suitable for large data and tags training than CRF++ now. For example, in machine with 64GB, CRF# encodes model with more than 4.5 hundred million features quickly.

Distributed phrase-based machine translation training tool based on Hadoop.

The Neurpheus Morphological Analyser performs morphological analysis, stemming or word form generation tasks using sophisticated classification methods for an analysis of words unseen in a training dictionary.