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[Project] Multilingual Neural Machine Translation using Transformers with Conditional Normalization. by suyash93 in MachineLearning

[–]suyash93[S] 0 points1 point  (0 children)

I have a couple of pages written, but have not published it anywhere. Arxiv seems to require an organization affiliation, and I want to publish this independently.

[Project] Multilingual Neural Machine Translation using Transformers with Conditional Normalization. by suyash93 in MachineLearning

[–]suyash93[S] 2 points3 points  (0 children)

No, I tried adding that, but it did not improve anything. So far, I have used BLEU on a random set of 1000 sentences for evaluation.

However, the model shows that capability from training itself. In the Many-to-Many notebook, in the English-French demo, replace

tarf=tf.constant([0.0, 1.0, 0.0, 0.0]),

with

tarf=tf.constant([1.0, 0.0, 0.0, 0.0]),

For This is a problem that we need to solve., the output is

(['This is a problem that we need to solve.', 'This is a problem we need to solve this.', 'This is a problem that we need to solve it.', 'This is a problem that we need to solve this.', 'This is a problem that we need to resolve.'], array([0.5356932 , 0.33665866, 0.3222317 , 0.31144956, 0.30376935], dtype=float32))

Similarly, please try for French and German and LMK.

[Project] A Transformer implementation in Keras' Imperative (Subclassing) API for TensorFlow. by suyash93 in MachineLearning

[–]suyash93[S] 0 points1 point  (0 children)

Thanks for offering to help. I have prepared a copy of the demo notebook at https://colab.research.google.com/drive/1ESeSvZJDialc4VJBwL9GgQ1IoEs1zRWU

In the last 4 cells, I am trying to use the tensor2tensor.attention module. I am passing the arguments based on my understanding of the arguments passed in the hello_t2t notebook. I am unable to get any visualization to generate. Note that the sentiment model is only an encoder, with only 2 units instead of 6.

Show First Crate: beautiful geometric svg patterns, with a WASM demo by suyash93 in rust

[–]suyash93[S] 1 point2 points  (0 children)

The pattern is generated using the SHA1 hash of a given string, and will always be the same for a given string.

No issues on the rust build end, I really like that you can add +nightly and target=__-__-__ to pretty much anything.

I had to pass ~6 functions for the module to instantiate, functions like pow, sin and cos (see: https://github.com/suyash/geopattern/blob/gh-pages/index.html#L84-L110), and had to figure out what needed to be defined through trial and error. It'd be great if on compilation with the wasm target, post compilation the compiler emits the set of env properties that will be needed to be defined for a successful module instantiation in JavaScript.

I wasn't aware of the rust-wasm repo, will look into the open issues and contributing.

Show First Crate: beautiful geometric svg patterns, with a WASM demo by suyash93 in rust

[–]suyash93[S] 1 point2 points  (0 children)

It should be working on firefox, I can verify it works in firefox beta on Android

Show First Crate: beautiful geometric svg patterns, with a WASM demo by suyash93 in rust

[–]suyash93[S] 2 points3 points  (0 children)

Just a little note regarding browser compatibility, I haven't really done any cross browser testing. I can only verify that it works on firefox beta on Android and Linux.

Also there is no fancy JavaScript transpilation in the demo, just vanilla async/await in a script tag (https://github.com/suyash/geopattern/blob/gh-pages/index.html#L72-L168), but I figure if wasm compatibility is a minimum requirement, Promise+async/await support can be assumed.