model-zoo
Transformers.jl
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model-zoo | Transformers.jl | |
---|---|---|
5 | 7 | |
883 | 503 | |
1.2% | - | |
4.6 | 6.9 | |
about 1 month ago | 2 months ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | MIT License |
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.
model-zoo
- sci-kit learn best for machine learning with Julia?
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Are there anyway to translate IPA to English? atleast, IPA text to pronounce sound
If your task is limited to isolated words or words that are separated by spaces, you can reverse the [CMU Pronouncing Dictionary](www.speech.cs.cmu.edu/cgi-bin/cmudict). In a programmatic environment with a dictionary/hash map, you will need to have the values be an extensible list of some sort to account for homophones. You could also train an ML model to convert phonemes to graphemes, like reversing this neural network model.
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Project in julia
You can also do some deep learning in Julia if you like. Flux.jl is Julia's deep learning library, and they have a model zoo of easy to follow working examples as a good starting point.
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Simple neural network model in Flux.jl doesn't seem to update the loss function
Secondly, flux.train! computes epochs automatically and is designed to take the entire data set. If you want to use the DataLoader, I think you probably need to write a custom training loop. It's not that complicated thankfully. The model zoo has some nice examples like: https://github.com/FluxML/model-zoo/blob/master/vision/mlp_mnist/mlp_mnist.jl
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Any Machine Learning Projects to Make with Julia
Check out the Flux.jl model zoo: https://github.com/FluxML/model-zoo#examples-listing
Transformers.jl
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Julia 1.10 Released
Flux is quite a nice lower level library:
https://github.com/FluxML/Flux.jl
On top of that there are many higher level libraries such as Transformers.jl
https://github.com/chengchingwen/Transformers.jl
- How is Julia Performance with GPUs (for LLMs)?
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Load a transformer model with julia
Check out Transformers.jl. It’s a library that implements transformer based models in Julia using Flux.jl. They have support for some of the huggingface transformers.
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Ask HN: Why hasn't the Deep Learning community embraced Julia yet?
https://github.com/chengchingwen/Transformers.jl but I have not had any personal experience with.
All of this is build by the community and your mileage may vary.
In my rather biased opinion the strengths of Julia are that the various ML libraries can share implementations, e.g. Pytorch and Tensorflow contain separate Numpy derivatives. One could say that you can write an ML framework in Julia, instead of writting a DSL in Python as part of your C++ ML library. As an example Julia has a GPU compiler so you can write your own layer directly in Julia and integrate it into your pipeline.
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Help on Differentiable Programming
I think you might have some luck with looking at a transformers implementation in flux, e.g: https://github.com/chengchingwen/Transformers.jl/tree/master/src/basic
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Fastai.jl: Fastai for Julia
Having tried fastai for a "serious" research project and helped (just a bit) towards FastAI.jl development, here's my take:
> motivation behind this is unclear.
Julia currently has two main DL libraries. Flux, which is somewhere between PyTorch and (tf.)Keras abstraction wise, and Knet, which is a little lower level (think just below PyTorch/around where MXNet Gluon sits). Frameworks like fastai, PyTorch Lightning and Keras demonstrate that there's a desire for higher-level, more batteries included libraries. FastAI.jl is looking to fill that gap in Julia.
> Since FastAI.jl uses Flux, and not PyTorch, functionality has to be reimplemented. FastAI.jl has vision support but no text support yet.
This is correct. That said, FastAI.jl is not and does not plan to be a copy of the Python API (hence "inspired by"). One consequence of this is that integration with other libraries is much easier, e.g. https://github.com/chengchingwen/Transformers.jl for NLP tasks.
> What is the timeline for FastAI.jl to achieve parity?
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Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?
If NLP primitives are all that's keeping you from testing the waters, have a look at https://github.com/chengchingwen/Transformers.jl.
What are some alternatives?
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
NaiveGAflux.jl - Evolve Flux networks from scratch!
PackageCompiler.jl - Compile your Julia Package
AlphaZero.jl - A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.
DataLoaders.jl - A parallel iterator for large machine learning datasets that don't fit into memory inspired by PyTorch's `DataLoader` class.
reproduced_data
Chain.jl - A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.
FastAI.jl - Repository of best practices for deep learning in Julia, inspired by fastai
StatsPlots.jl - Statistical plotting recipes for Plots.jl
org-mode - This is a MIRROR only, do not send PR.
cmssw - CMS Offline Software