stopes VS tm2tb

Compare stopes vs tm2tb and see what are their differences.

stopes

A library for preparing data for machine translation research (monolingual preprocessing, bitext mining, etc.) built by the FAIR NLLB team. (by facebookresearch)
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stopes tm2tb
1 1
237 47
3.4% -
5.8 8.3
5 months ago 4 months ago
Python Python
MIT License GNU General Public License v3.0 only
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

stopes

Posts with mentions or reviews of stopes. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-07-21.

tm2tb

Posts with mentions or reviews of tm2tb. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing stopes and tm2tb you can also consider the following projects:

NeMo - A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)

edenai-apis - Eden AI: simplify the use and deployment of AI technologies by providing a unique API that connects to the best possible AI engines

flores - Facebook Low Resource (FLoRes) MT Benchmark

lingvo - Lingvo

nematus - Open-Source Neural Machine Translation in Tensorflow

argos-translate - Open-source offline translation library written in Python

Thirukkural-English-Translation-Dataset - Thirukural in English

seq2seq - A general-purpose encoder-decoder framework for Tensorflow

refinery - The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.