I am trying to compile TensorflowLite for emscripten (I am aware of TensorflowJS) and pthreads are currently disabled. Is there a way to use it without pthreads?

🚀 Feature

Add numerically stable cumulative logsumexp function. Also we have associated PR on cummax that is needed for numerically stable implementation (pytorch/pytorch#20240).

Motivation

This is useful when computing sum of probabilities and have different applications.

Pitch

Torch has cumsum and cumprod so I suggest logcumsumexp to be added.

I was going though the existing enhancement issues again and though it'd be nice to collect ideas for spaCy plugins and related projects. There are always people in the community who are looking for new things to build, so here's some inspiration ✨ For existing plugins and projects, check out the spaCy universe.

If you have questions about the projects I suggested,

Reference from TensorFlow: https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/matrix-band-part

This op is used by the Music Transformer model.

We currently support reading FastText models from Facebook's format. The gensim.models._fasttext_bin does this.

This enables people to use gensim with a model that was trained using Facebook's binaries.

Sometimes, people want things to work the other way: they start with gensim, train a model, and then

i'm a newbie in programming. I try to use this library. it's very useful for me.
I want to show centroid in K-means clustering. how to show it? thank u so much..

Support for storing large tensor values in external files was introduced in #678, but AFAICT is undocumented.

This is a pretty important feature, functionally, but it's also important for end users who may not realise that they need to move around more than just the *.onnx file.

I would suggest it should be documented in IR.md, and perhaps there are other locations from which it could be s

Hi I would like to propose a better implementation for 'test_indices':

We can remove the unneeded np.array casting:

Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))

Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))