diff --git a/README.md b/README.md index bd1025d..3332dab 100644 --- a/README.md +++ b/README.md @@ -12,13 +12,8 @@ This library provides an implementation of CSG, from CVPR 2019 paper: [Spectral Metric for Dataset Complexity Assessment](https://arxiv.org/abs/1905.07299). -**Installation** - -`pip install spectral-metric` - - -CSG is a measure which estimates the complexity of a dataset by combining probability product kernel (Jebara et al.) and -Graph Theory. By doing so, one can estimate the complexity of their dataset without training a model. +> [!NOTE] +> CSG is a measure that estimates the complexity of a dataset by combining probability product kernel (Jebara et al.) and Graph Theory. By doing so, one can estimate the complexity of their dataset without training a model. For the experiment part of the repo, please see [./experiments/README.md](./experiments/README.md) @@ -27,7 +22,9 @@ For the experiment part of the repo, please see [./experiments/README.md](./expe 1. [🤗 HuggingFace Space](https://huggingface.co/spaces/Dref360/spectral-metric) 2. [In-depth analysis of CLINC-150](https://github.com/Dref360/spectral-metric/blob/master/notebooks/clinc_oos.ipynb) - +**Installation** + +`pip install spectral-metric` ## How to use @@ -48,9 +45,9 @@ estimator.evals, estimator.evecs # The eigenvalues and vectors. make_graph(estimator.difference, title="Your dataset", classes=["A", "B", "C"]) ``` -## Support - -For support, please submit an issue! +
+
+