Opus-MT
tensorflow
Our great sponsors
| Opus-MT | tensorflow | |
|---|---|---|
| 3 | 222 | |
| 527 | 182,456 | |
| 8.7% | 0.8% | |
| 4.8 | 10.0 | |
| 4 days ago | 4 days ago | |
| Python | C++ | |
| MIT License | Apache License 2.0 |
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.
Opus-MT
-
“sync,corrected by elderman” issue in ML translation datasets spread on internet
- mention on GitHub repo of a translation model https://github.com/Helsinki-NLP/Opus-MT/issues/62
I'm curious to see if anyone else has interesting encounters with this
-
How worried are you about AI taking over music?
Yes, most models these days, except the exceptionally large ones, are possible to train on a laptop. Of course it helps if your laptop has Nvidia CUDA GPU, but even if it doesn't you can rent an AWS 4 core/16GB GPU instance for 0.5 cents an hour. 24 hours of training time would be quite a lot for most models, unless you're trying to train a FB any to any language type model, but typically the big huge models are not the most interesting ones, and you can get very good results, and interesting models with substantially smaller sets of data. Opus MT models are only one language to one language, but they're about 300MB a model, and the quality rivals FB's models, and the speed is substantially faster. I don't have as many examples from the music space, as it's still a fairly under explored area, but Google has released Magenta which is a pretrained Tensorflow music model(actually a group of 3-4 models).
- Helsinki-NLP/Opus-MT: Open neural machine translation models and web services
tensorflow
-
Google lays off its Python team
[3]: https://github.com/tensorflow/tensorflow/graphs/contributors
- TensorFlow-metal on Apple Mac is junk for training
-
🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
To get up to speed with TensorFlow, check their quickstart Support TensorFlow on GitHub ⭐
- One .gitignore to rule them all
-
10 Github repositories to achieve Python mastery
Explore here.
-
GitHub and Developer Ecosystem Control
Part of the major userbase pull in GitHub revolves around hosting a considerable number of popular projects including Angular, React, Kubernetes, cpython, Ruby, tensorflow, and well even the software that powers this site Forem.
-
Non-determinism in GPT-4 is caused by Sparse MoE
Right but that's not an inherent GPU determinism issue. It's a software issue.
https://github.com/tensorflow/tensorflow/issues/3103#issueco... is correct that it's not necessary, it's a choice.
Your line of reasoning appears to be "GPUs are inherently non-deterministic don't be quick to judge someone's code" which as far as I can tell is dead wrong.
Admittedly there are some cases and instructions that may result in non-determinism but they are inherently necessary. The author should thinking carefully before introducing non-determinism. There are many scenarios where it is irrelevant, but ultimately the issue we are discussing here isn't the GPU's fault.
-
Can someone explain how keras code gets into the Tensorflow package?
and things like y = layers.ELU()(y) work as expected. I wanted to see a list of the available layers so I went to the Tensorflow GitHub repository and to the keras directory. There's a warning in that directory that says:
-
Is it even possible to design a ML model without using Python or MATLAB? Like using C++, C or Java?
Exactly what language do you think TensorFlow is written in? :)
-
How to do deep learning with Caffe?
You can use Tensorflow's deep learning API for this.
What are some alternatives?
OPUS-MT-train - Training open neural machine translation models
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
OpenNMT-py - Open Source Neural Machine Translation and (Large) Language Models in PyTorch
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
fastText - Library for fast text representation and classification.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Neural-Machine-Translated-communication-system - The model is designed to train a single and large neural network in order to predict correct translation by reading the given sentence.
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
tensor2tensor - Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
scikit-learn - scikit-learn: machine learning in Python
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.