tm2tb VS stopes

Compare tm2tb vs stopes 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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tm2tb stopes
1 1
47 237
- 1.3%
8.3 5.8
5 months ago 5 months ago
Python Python
GNU General Public License v3.0 only MIT License
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.

tm2tb

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

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.

What are some alternatives?

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

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

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)

lingvo - Lingvo

flores - Facebook Low Resource (FLoRes) MT Benchmark

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

nematus - Open-Source Neural Machine Translation in Tensorflow

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

Thirukkural-English-Translation-Dataset - Thirukural in English

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