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Parameter Name Correction #86

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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -175,7 +175,7 @@ TabFM uses in-context learning over a bounded context window, so very large
tables should be sampled or split before inference. The scikit-learn estimators
expose the main practical limits through `max_num_features` and `max_num_rows`
(defaults are 500 features and 100 context rows), plus `n_estimators` for
ensembling over multiple sampled contexts and `inference_batch_size` for memory
ensembling over multiple sampled contexts and `batch_size` for memory
control. If your dataset is larger than these limits, TabFM will work with the
sampled/context rows rather than consuming the full table at once.

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