cleora
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Cleora AI is a general-purpose model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data.
I am trying to run Cleora on a simple dataset. My TSV file is simple and follows the format of "leads attributes" l1 a1 l2 a1 l1 a2 l3 a2...
Hi! Apologies if this is not the best place to ask this but. I've been reading Your paper and I hope You could clarify this little thing which is confusing...
hi I am not able to find two things: 1. How to give data where edges are present with some weight?? So that edge weights are taken into embedding calculation....
How can I force it to learn the embedding of a directed graph?
There's an option named 'num_partitions' in [pytorch-biggraph](https://github.com/facebookresearch/PyTorch-BigGraph) that can reduce the peak memory usage, Can Cleora provide that option too? is it possible in the future? my situation: 40M nodes...
Is it possible to leverage the information of the node features, e.g. initialize the embeddings?
* Added an installation of requirements with pinned version * Got rid of warnings about deprecated `log_loss` and `np.int`
The implementation of: #69 issue. Pull requests which adds: 1) pyo3 integration 2) support for parquet output 3) support for s3 inputs/outputs
I'd like to add a few features. 1) Integration with pyo3 bindings which will enable to publish library as a python package and use without using subprocess 2) Support for...