commit | 20e7dd0800fb69f73b770463b3b90fde283219f5 | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Wed Apr 17 20:59:59 2024 +0200 |
committer | Matthias Boehm <mboehm7@gmail.com> | Wed Apr 17 21:12:18 2024 +0200 |
tree | 8ea6349af0e6eb153fc6169b2116f5b19ace2826 | |
parent | 51597685f6f16e35f2fd3ff2cb84a334ec2df1f1 [diff] |
[SYSTEMDS-3538] New word-error-rate builtin function for TPCx-AI This patch adds the word-error-rate (WER) builtin function, which is derived from the Levenshtein distance but on words instead of characters. Part of the feature pack for implementing TPCx-AI on SystemDS.
Overview: SystemDS is an open source ML system for the end-to-end data science lifecycle from data integration, cleaning, and feature engineering, over efficient, local and distributed ML model training, to deployment and serving. To this end, we aim to provide a stack of declarative languages with R-like syntax for (1) the different tasks of the data-science lifecycle, and (2) users with different expertise. These high-level scripts are compiled into hybrid execution plans of local, in-memory CPU and GPU operations, as well as distributed operations on Apache Spark. In contrast to existing systems - that either provide homogeneous tensors or 2D Datasets - and in order to serve the entire data science lifecycle, the underlying data model are DataTensors, i.e., tensors (multi-dimensional arrays) whose first dimension may have a heterogeneous and nested schema.
Resource | Links |
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Quick Start | Install, Quick Start and Hello World |
Documentation: | SystemDS Documentation |
Python Documentation | Python SystemDS Documentation |
Issue Tracker | Jira Dashboard |
Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source