jax

Related to #5142, AlbertTokenizer (which uses SentencePiece) doesn't decode special tokens (like [CLS], [MASK]) properly. This issue was discovered when adding the Nystromformer model (#14659), which uses this tokenizer.

To reproduce (Transformers v4.15 or below):

!pip install -q transformers sentencepiece

from transformers import AlbertTokenizer

tokenizer = AlbertTokenizer.from

It would be great if you could add the JAX equivalent of scipy.stats.mode, which is currently unavailable.

A use case may be a ML classification task with ensembles, where multiple models give different predictions, and we are interested in finding the most common one.

As an example, consider predictions to be a two-dimensional DeviceArray of predictions, with
shape = (number of model

Is there a way to train a bidirectional RNN (like LSTM or GRU) on trax nowadays?

Love the book! Ran into this display issue when viewing it on Chrome's PDF viewer:

Dear Numpyro developers,

Please develop Euler Maruyama features in numpyro similar to features found in PyMC.

Thanks alot.

Dear Brax team,

Since Brax is fully differentiable, I thought it'd be possible to use it like DiffTaichi or GradSim for system identification (e.g. determining the mass of an object from a trajectory and known force) but I couldn't find any example for this.
Do you happen to have any demo or tips for this?

From the top of my head I would do something like this:
Let's say the task is to es

jit and unroll don't seem to be documented in https://jaxopt.github.io/stable/_autosummary/jaxopt.GradientDescent.html#jaxopt-gradientdescent