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| Hi Dinesh, the benchmark is measuring the performance of raw LLMs on the task of writing WL code. The notebook assistant is using [RAG][1] based on language documentation to boost the relevance of its answers so it would be like cheating! ... |
| The issue is with the compilation step in `FunctionLayer`t not being able to read the downvalues for the `map` symbol in order to inline that in the net. This is an usupported feature at the moment. |
| Great post! It's always nice to see the technology applied to real world cases. Just one comment: the default feature extractor for `CreateSemanticSearchIndex` ("SentenceBERT") is a local model that runs on your machine. `CreateVectorDatabase`... |
| If you are interested in the information available in the panel you can get them using Information[ClassifierFunction[...]] |
| I am confused by your problem setup. A single example is `in -> out` where `in` is 192 binary digits. A single modified example is `in$mod -> out` where `in$mod` is 24 integers stored as `ByteArray` If this is correct you can use normal to... |
| The easiest way right now is by using ``` Information[p, "DecisionTree"] // Head (* Tree *) ``` However, keep in mind that this tree is not acting on the input data but on the processed data. You can force the processing pipeline to be... |
| I personally find the JAX model compelling but I never seriously worked with it. I am courious: why you would not reccomend it's inclusion? Thanks! |
| It uses the biased estimator for the variance ``` x = {{1., 2., 3.}, {4., 5., 6.}, {7., 8., 9.}, {10., 11., 12.}, {13., 14., 15.}} xS01 = FeatureExtract[x, "StandardizedVector"]; xS02 = Standardize[x]; xS03 = Sqrt[Length[x]/(Length[x] -... |
| You can specify the point of origin of the axes. ``` Plot[Sin[x], {x, 0, 6 Pi}, AxesOrigin -> {6 Pi, 0}] ``` ![plot with y axis on the right side][1] [1]:... |
| It really depends on what you want your trainable parameters to be. For a model such as ``` a * Input1 * Input 2 + b * Input3 + c ``` you can use this network ``` FunctionLayer[ Function[ NetArray[]*#Input1*#Input2 +... |