pytorch-tutorial
OpenNMT-py
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pytorch-tutorial
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PyTorch - What does contiguous() do?
I was going through this example of a LSTM language model on github (link).What it does in general is pretty clear to me. But I'm still struggling to understand what calling contiguous() does, which occurs several times in the code.
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How to 'practice' pytorch after finishing its basic tutorial?
I tried to move straight to practicing implementing papers and trying to understand other people's codes but failed miserably. I feel like there was too much of a gap between the basic tutorial and being able to implement ideas into code....hence the question: Is there any resource/way to practice pytorch in general? I did find this and this, but I just wanted to hear what others have gone through to become better at PyTorch up to the point they can build stuff from their own ideas
- [P] Probabilistic Machine Learning: An Introduction, Kevin Murphy's 2021 e-textbook is out
OpenNMT-py
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Making a custom Google Translate equivalent / web translation filter for my conlang?
I already tried this with OpenNMT.
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Cutting edge language translation models
fairseq and OpenNMT are very good starting points if you want to train your NMT model from scratch.
- How Telegram Messenger circumvents Google Translate's API
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WEBNLG challenge 2017 on Google Colab error
It looks like this uses the version of OpenNMT implemented in torch, which has been deprecated. You will be much better off using the pytorch implementation of OpenNMT or the transformers library. In fact, I would recommend taking a look at the GEM benchmark, since it also uses the WebNLG dataset. Here is a tutorial to get started, you can change the dataset here to WebNLG instead of CommonGen.
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Help with Neural Machine Translation
Umm... open-nmt This is a library maintained since 2016 for NMT
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Oop concepts for pytorch
However, you do not need to use much OOP when training models with pytorch. Most of the time it is just inheriting a class and overwriting functions. You might need more advanced stuff if you were writing a framework on top of it, something like ONMT
What are some alternatives?
mixture-of-experts - PyTorch Re-Implementation of "The Sparsely-Gated Mixture-of-Experts Layer" by Noam Shazeer et al. https://arxiv.org/abs/1701.06538
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
InceptionTime - InceptionTime: Finding AlexNet for Time Series Classification
tensor2tensor - Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
Conv-TasNet - A PyTorch implementation of Conv-TasNet described in "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" with Permutation Invariant Training (PIT).
Transformer-Models-from-Scratch - implementing various transformer models for various tasks
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Opus-MT - Open neural machine translation models and web services
BigGAN-PyTorch - The author's officially unofficial PyTorch BigGAN implementation.
OpenNMT - Open Source Neural Machine Translation in Torch (deprecated)
bonito - A PyTorch Basecaller for Oxford Nanopore Reads
LibreTranslate - Free and Open Source Machine Translation API. Self-hosted, offline capable and easy to setup.