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README.md

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 TabFM is a zero-shot tabular foundation model from Google Research. It supports
 classification and regression on structured/tabular data with mixed numerical and
-categorical columns, requiring no fine-tuning or hyperparameter search — training
+categorical columns, requiring no fine-tuning or hyperparameter search - training
 examples are passed as context and predictions are made in a single forward pass.
 
 This repository contains the **PyTorch** weights. For the JAX/Flax weights see
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 ## Performance
 
 TabFM was evaluated on [TabArena](https://tabarena.ai) across 51 datasets
-(38 classification, 13 regression). In zero-shot mode — a single forward pass with no
-hyperparameter search — TabFM outperforms heavily-tuned supervised baselines including
+(38 classification, 13 regression). In zero-shot mode - a single forward pass with no
+hyperparameter search - TabFM outperforms heavily-tuned supervised baselines including
 gradient-boosted trees. The `TabFMClassifier.ensemble()` preset (feature crosses,
 SVD features, NNLS blending) yields further improvements.
 
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 ## License
 
 The model weights in this repository are released under the
-**TabFM Non-Commercial License v1.0** — see [LICENSE](LICENSE). The source code is
+**TabFM Non-Commercial License v1.0** - see [LICENSE](LICENSE). The source code is
 Apache 2.0 licensed via [google-research/tabfm](https://github.com/google-research/tabfm).
 
 ## Version