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update readme

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@@ -21,7 +21,6 @@ posterior-mean coefficients, with no MCMC and no per-dataset fitting.
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  Code and documentation: [github.com/schiekiera/LinearPFN](https://github.com/schiekiera/LinearPFN)
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- <br>
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  ## Use
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@@ -39,7 +38,6 @@ result = model.fit(X, y)
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  print(result.summary())
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  ```
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- <br>
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  ## Model
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@@ -58,7 +56,6 @@ The outputs are the posterior under the training prior: linear main effects, pai
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  interactions, strong heredity, Gaussian noise. Missing values and categorical predictors
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  must be handled before fitting.
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- <br>
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  ## Files
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  Code and documentation: [github.com/schiekiera/LinearPFN](https://github.com/schiekiera/LinearPFN)
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  ## Use
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  print(result.summary())
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  ```
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  ## Model
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  interactions, strong heredity, Gaussian noise. Missing values and categorical predictors
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  must be handled before fitting.
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  ## Files
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