readme update

README.md

@@ -125,13 +125,13 @@ Those embeddings can then be utilized in a novel way thanks to their dim-wise in
 
 ## Key technical features of Cleora embeddings
 The embeddings produced by Cleora are different from those produced by Node2vec, Word2vec, DeepWalk or other systems in this class by a number of key properties:
- - efficiency - Cleora is two orders of magnitude faster than Node2Vec or DeepWalk
- - inductivity - as Cleora embeddings of an entity are defined only by interactions with other entities, vectors for new entities can be computed on-the-fly
- - updatability - refreshing a Cleora embedding for an entity is a very fast operation allowing for real-time updates without retraining
- - stability - all starting vectors for entities are deterministic, which means that Cleora embeddings on similar datasets will end up being similar. Methods like Word2vec, Node2vec or DeepWalk return different results with every run.
- - cross-dataset compositionality - thanks to stability of Cleora embeddings, embeddings of the same entity on multiple datasets can be combined by averaging, yielding meaningful vectors
- - dim-wise independence - thanks to the process producing Cleora embeddings, every dimension is independent of others. This property allows for efficient and low-parameter method for combining multi-view embeddings with Conv1d layers.
- - extreme parallelism and performance - Cleora is written in Rust utilizing thread-level parallelism for all calculations except input file loading. In practice this means that the embedding process is often faster than loading the input data.
+ - **efficiency** - Cleora is two orders of magnitude faster than Node2Vec or DeepWalk
+ - **inductivity** - as Cleora embeddings of an entity are defined only by interactions with other entities, vectors for new entities can be computed on-the-fly
+ - **updatability** - refreshing a Cleora embedding for an entity is a very fast operation allowing for real-time updates without retraining
+ - **stability** - all starting vectors for entities are deterministic, which means that Cleora embeddings on similar datasets will end up being similar. Methods like Word2vec, Node2vec or DeepWalk return different results with every run.
+ - **cross-dataset compositionality** - thanks to stability of Cleora embeddings, embeddings of the same entity on multiple datasets can be combined by averaging, yielding meaningful vectors
+ - **dim-wise independence** - thanks to the process producing Cleora embeddings, every dimension is independent of others. This property allows for efficient and low-parameter method for combining multi-view embeddings with Conv1d layers.
+ - **extreme parallelism and performance** - Cleora is written in Rust utilizing thread-level parallelism for all calculations except input file loading. In practice this means that the embedding process is often faster than loading the input data.
 
 ## Key usability features of Cleora embeddings
 
@@ -302,4 +302,4 @@ Every `SparseMatrix` object allocates space for:
 - `|V|` objects, each occupying 40 bytes,
 - `2 x |E|` objects (in undirected graphs we need to count an edge in both directions), each occupying 24 bytes.
 
-During training we need additonal `2 x d x |V|` objects, each occupying 4 bytes (this can be avoided by using memory-mapped files, see `--in-memory-embedding-calculation` argument for the program).
+During training we need additonal `2 x d x |V|` objects, each occupying 4 bytes (this can be avoided by using memory-mapped files, see `--in-memory-embedding-calculation` argument for the program).