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An Upgrade to SyntaxNet, New Models and a Parsing Competition

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Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition

#51

Post by taliesinb »

jtraffic wrote: post #46
taliesinb wrote: post #22

Earlier quoted context omitted.

Plug for Mathematica, which after its installed you can do deep learning on in one or two lines, with GPU support on all three platforms with no setup. Very concise. Getting fairly competitive in features with other high level declarative frameworks as of 11.1 (which was just released today). Very nice visualizations thanks to being in Mathematica. The language is of course closed source, paid software. Many universi…

Two times in my life, I've gotten deeply excited about Mathematica. The first time I wanted to use it for economics homework as an undergraduate. (Don't worry, I did it on paper first.) The second time, I wanted to use it for machine learning, especially NLP. Mainly the knowledge base Mathematica hooks into is what drew me. The problem in the end is that the customizability of Mathematica ends right where things get…

> If you want to show people cool little examples, Mathematica is clean and fast, but you can't build anything serious with it.

Mathematica, which is a serious project, is largely written in Mathematica. Wolfram|Alpha, another large project, is built in Mathematica. Outside the company, https://emeraldcloudlab.com/ for example has built their platform on Mathematica.

> And by "serious", I guess I mean something with few enough constraints to have an identity of its own, rather than being "a thing you can do with Mathematica."

For research, a "thing you can do with Mathematica" is often what you want. But other than that you can put things you build in the cloud via APIFunctions (similar to AWS Lambda functions), or call out to them via wolframscript, or talk to kernels directly via MathLink or LibraryLink, or over sockets via ChannelListen.

> Another limitation is the data input. Someone needs to rethink it seriously.

That's vague, but I imagine you mean importers. Certain built-in importers aren't as good as they should be, like CSV and XLS are memory hogs and die on relatively small amounts of data (at least they used to, I haven't checked recently). The HDF5 importer is now pretty good, and for large datasets it's a good choice for scientific computing anyway.

> Programming languages/platforms are network goods. IMHO, Mathematica has tried to swim against this fact and has failed.

No arguments there. I think it would be great if we could open source at least parts of it, because no doubt new life would be breathed into cobwebby parts of the codebase and various pet peeves fixed. But Mathematica still dominates the computer algebra space despite being closed source, and probably will continue to do so for a while.

Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition

#52

Post by chronicx0 »

greenpizza13 wrote: post #36
jacquesm wrote: post #15

I've been fighting Tensorflow in the last couple of days to try an application on it, never before have I seen such a convoluted build process and a maze of dependencies. The best manual on getting tensorflow with CUDA support up and running is here: http://www.nvidia.com/object/gpu-accelerated-applications-te... But it is a little bit out of date when it comes to version numbers. If you're going to try TensorBox ( h…

I strongly recommend you use Keras if you are new to Tensorflow. The API abstractions will make testing your network ideas a breeze. It won't save you from the hell of building TF, but should save you loads of time with implementation and testing.

Keras is dying. Tensorflow-Slim is contains nearly all its features and is included in Tensorflow from Google.

Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition

#53

Post by plusepsilon »

minimaxir wrote: post #5

See also: spaCy, which is an open-source NLP framework that has some integration with Keras as well: https://news.ycombinator.com/item?id=13874787 ...and apparently will release a major version update today . Ouch.

I think spaCy uses perceptrons (essentially a shallow neural network) so it should be faster. Accuracy is pretty similar with SyntaxNet at least on the training data but I'm guessing SyntaxNet works better on long range dependencies.

I wonder if the spaCy update will go deep :)

Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition

#54

Post by jakekovoor »

This is definitely a game changer!

It's a very interesting research carried out by Google's research team and I believe this will be especially beneficial for future speech translation algorithms that would bring us a whole new, fresh experience with the way we converse with Alexa, Google Home, Siri, and many more.

If you need to install TensorFlow onto your Windows 10 computer then here's a great guide which I have followed quiet a few times. :)

http://saintlad.com/install-tensorflow-on-windows/

Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition

#55

Post by canada_dry »

squeaky-clean wrote: post #42
canada_dry wrote: post #25

Earlier quoted context omitted.

> I've got the project on hold for now because I can't find a decent, non-commercial way of converting voice to text. I'd really rather not send my audio out to Amazon/Google/MS/IBM Same concern here... so my voice->text method is via android's google voice - forced to offline mode. The offline mode is surprisingly good. Re mis triggers... I also have opencv running on the same android. It only activates the voice re…

> text method is via android's google voice - forced to offline mode. The offline mode is surprisingly good. I actually tried this at one point with a wall-mounted tablet before trying Sphinx. It is surprisingly good for offline, probably the best offline I've tried yet outside of dedicated software like Dragon. But it doesn't meet my open criteria, so I'm hoping to find something better. I'll most likely give up on…

> doesn't meet my open criteria

With the leaps and bounds being made in ml these days it can't be long before magnitudes better open source voice recognition becomes available. I gave Sphinx a try but it was horribly disappointing.

For me, the combination of google voice (offline) and Ivona voice (Amy) is pretty damn good for my android/python/arduino based home AI.

Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition

#56

Post by matt4077 »

peterbonney wrote: post #48
jorgemf wrote: post #19

Earlier quoted context omitted.

It is not a good idea to compile TensorFlow by your own unless you really need it (for example for TensorFlow serving). Python packages are the way to go.

I disagree - I think you are well-served to compile on your own unless you know you don't need it, e.g. if you are just trying it out to learn how it works. The standard build uses a "least common denominator" Intel instruction set (SSE4), but the odds are extremely high that the machine on which you're running tensorflow supports 4.2. Building from source allows you to use the most up-to-date instruction set (the de…

I don't quite understand how you performance-obsessed enough to care about the compiler options they use – but not run Tensorflow on a GPU. Even mobile GPUs offer a 5x speedup at least.

Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition

#57

Post by jorgemf »

peterbonney wrote: post #48
jorgemf wrote: post #19

Earlier quoted context omitted.

It is not a good idea to compile TensorFlow by your own unless you really need it (for example for TensorFlow serving). Python packages are the way to go.

I disagree - I think you are well-served to compile on your own unless you know you don't need it, e.g. if you are just trying it out to learn how it works. The standard build uses a "least common denominator" Intel instruction set (SSE4), but the odds are extremely high that the machine on which you're running tensorflow supports 4.2. Building from source allows you to use the most up-to-date instruction set (the de…

>The standard build uses a "least common denominator" Intel instruction set (SSE4)

TensorFlow is not meant for the CPU. If you want to do something serious you have to move to the GPU, which it is at least 8 times faster than your optimized CPU with an average GPU.

Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition

#58

Post by dang »

We changed the title from "Google open-sources Tensorflow-based framework for NLP", which appears misleading, given that it happened last May: https://news.ycombinator.com/item?id=11686029.

On HN the idea is to rewrite titles only to make them less misleading (or less baity). Please see https://news.ycombinator.com/newsguidelines.html.

Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition

#59

Post by jorgemf »

jacquesm wrote: post #30
jorgemf wrote: post #19

Earlier quoted context omitted.

It is not a good idea to compile TensorFlow by your own unless you really need it (for example for TensorFlow serving). Python packages are the way to go.

I have to because I'm trying to use TensorBox which does not play well with the regular version of tensorflow that you can get pre-compiled. See this issue: https://github.com/TensorBox/TensorBox/issues/100 and https://github.com/TensorBox/TensorBox/issues/102 So then we're full-circle and installing from pip which doesn't work :( sigh. Anyway, I'll get it to work, somehow.

Those issues are related with TensorFlow <1.0. It wasn't an stable release. Try 1.0.1.

Re: An Upgrade to SyntaxNet, New Models and a Parsing Competition

#60

Post by jorgemf »

aseipp wrote: post #33
jorgemf wrote: post #19

Earlier quoted context omitted.

It is not a good idea to compile TensorFlow by your own unless you really need it (for example for TensorFlow serving). Python packages are the way to go.

This is a bit unfortunate, in a real sense. I mean, I already build enough software, so I'm not sad on missing out. But here's the thing: TensorFlow actually installed great on Windows and it took less than 10 minutes to get running, once I had Python3 installed, even with GPU support. Even worked awesome in VS Code, out of the box, with autocomplete in the python mode. Even a baby like me got started easily. But it'…

> But it's a bit disappointing to hear that the build system is something of a nightmare

I know... I am building it for TensorFlow Serving and it is pain in the ass. I will help but I have no idea how to do it, only find workarounds to compile specific commits.

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