From 66ed538fe869a693b45e8722d7d7de17ed8b18da Mon Sep 17 00:00:00 2001 From: rchan Date: Fri, 17 Nov 2023 17:45:17 +0000 Subject: [PATCH] add links for dim reduce and data splits --- README.md | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 8345cc3..b12173e 100644 --- a/README.md +++ b/README.md @@ -48,9 +48,10 @@ embeddings obtained from transformers. The key contributions are: [`nlpsig.PrepareData`](https://nlpsig.readthedocs.io/en/latest/data_preparation.html) allows you to easily construct paths/streams of embeddings which can be used for several downstream tasks. -- Simple API for performing dimensionality reduction on the embeddings obtained - from transformers by some simple wrappers over popular dimensionality - reduction algorithms such as PCA, UMAP, t-SNE, etc. +- Simple API for performing dimensionality reduction with + [`nlpsig.DimReduce`](https://nlpsig.readthedocs.io/en/latest/dimensionality_reduction.html) + on the embeddings obtained from transformers by some simple wrappers over + popular dimensionality reduction algorithms such as PCA, UMAP, t-SNE, etc. - This is particularly useful if we wish to use path signatures in any downstream model since the dimensionality of the embeddings obtained from transformers is usually very high. @@ -58,8 +59,10 @@ embeddings obtained from transformers. The key contributions are: [`sig-networks`](https://github.com/ttseriotou/sig-networks) library which uses these paths constructed in this library as inputs to neural networks which utilise path signature methodology. -- We also have simple classes for constructing train/test splits of the data and - for K-fold cross-validation which are specific for the Signature Networks in +- We also have + [simple classes](https://nlpsig.readthedocs.io/en/latest/classification_utils.html) + for constructing train/test splits of the data and for K-fold cross-validation + in which are general and are applied to examples in the Signature Networks in the [`sig-networks`](https://github.com/ttseriotou/sig-networks) library. ## Installation